Reviewerly/exHarmony huggingface dataset:
Data-Driven Mark Orientation for Trend Estimation in Scatterplots

A common task for scatterplots is communicating trends in bivariate data. However, the ability of people to visually estimate these trends is under-explored, especially when the data violate assumptions required for common statistical models, or visual trend estimates are in conflict with statistical ones. In such cases, designers may need to intervene and de-bias these estimations, or otherwise inform viewers about differences between statistical and visual trend estimations. We propose data-driven mark orientation as a solution in such cases, where the directionality of marks in the scatterplot guide participants when visual estimation is otherwise unclear or ambiguous. Through a set of laboratory studies, we investigate trend estimation across a variety of data distributions and mark directionalities, and find that data-driven mark orientation can help resolve ambiguities in visual trend estimates.
Cervical cancer risk prediction with robust ensemble and explainable black boxes method

Abstract Clinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they suffer from the fact that often the methods used are hard to interpret and difficult to understand on how some decisions are made; the opacity of some methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also suffer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly affects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classification of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem.
Hierarchical Transformer-Based Large-Context End-To-End ASR with Large-Context Knowledge Distillation

We present a novel large-context end-to-end automatic speech recognition (E2E-ASR) model and its effective training method based on knowledge distillation. Common E2E-ASR models have mainly focused on utterance-level processing in which each utterance is independently transcribed. On the other hand, large-context E2E-ASR models, which take into account long-range sequential contexts beyond utterance boundaries, well handle a sequence of utterances such as discourses and conversations. However, the transformer architecture, which has recently achieved state-of-the-art ASR performance among utterance-level ASR systems, has not yet been introduced into the large-context ASR systems. We can expect that the transformer architecture can be leveraged for effectively capturing not only input speech contexts but also long-range sequential contexts beyond utterance boundaries. Therefore, this paper proposes a hierarchical transformer-based large-context E2E-ASR model that combines the transformer architecture with hierarchical encoder-decoder based large-context modeling. In addition, in order to enable the proposed model to use long-range sequential contexts, we also propose a large-context knowledge distillation that distills the knowledge from a pre-trained large-context language model in the training phase. We evaluate the effectiveness of the proposed model and proposed training method on Japanese discourse ASR tasks.
Sign Language Segmentation with Temporal Convolutional Networks

The objective of this work is to determine the location of temporal boundaries between signs in continuous sign language videos. Our approach employs 3D convolutional neural network representations with iterative temporal segment refinement to resolve ambiguities between sign boundary cues. We demonstrate the effectiveness of our approach on the BSLCORPUS, PHOENIX14 and BSL-1K datasets, showing considerable improvement over the state of the art and the ability to generalise to new signers, languages and domains.
Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search

Product search has been a crucial entry point to serve people shopping online. Most existing personalized product models follow the paradigm of representing and matching user intents and items in the semantic space, where finer-grained matching is totally discarded and the ranking of an item cannot be explained further than just user/item level similarity. In addition, while some models in existing studies have created dynamic user representations based on search context, their representations for items are static across all search sessions. This makes every piece of information about the item always equally important in representing the item during matching with various user intents. Aware of the above limitations, we propose a review-based transformer model (RTM) for personalized product search, which encodes the sequence of query, user reviews, and item reviews with a transformer architecture. RTM conducts review-level matching between the user and item, where each review has a dynamic effect according to the context in the sequence. This makes it possible to identify useful reviews to explain the scoring. Experimental results show that RTM significantly outperforms state-of-the-art personalized product search baselines.
Attention-guided Temporally Coherent Video Object Matting

This paper proposes a novel deep learning-based video object matting method that can achieve temporally coherent matting results. Its key component is an attention-based temporal aggregation module that maximizes image matting networks' strength for video matting networks. This module computes temporal correlations for pixels adjacent to each other along the time axis in feature space, which is robust against motion noises. We also design a novel loss term to train the attention weights, which drastically boosts the video matting performance. Besides, we show how to effectively solve the trimap generation problem by fine-tuning a state-of-the-art video object segmentation network with a sparse set of user-annotated keyframes. To facilitate video matting and trimap generation networks' training, we construct a large-scale video matting dataset with 80 training and 28 validation foreground video clips with ground-truth alpha mattes. Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion. Our code and dataset can be found at: https://github.com/yunkezhang/TCVOM
Automated Generation of Robotic Planning Domains from Observations

Automated planning enables robots to find plans to achieve complex, long-horizon tasks, given a planning domain. This planning domain consists of a list of actions, with their associated preconditions and effects, and is usually manually defined by a human expert, which is very time-consuming or even infeasible. In this paper, we introduce a novel method for generating this domain automatically from human demonstrations. First, we automatically segment and recognize the different observed actions from human demonstrations. From these demonstrations, the relevant preconditions and effects are obtained, and the associated planning operators are generated. Finally, a sequence of actions that satisfies a user-defined goal can be planned using a symbolic planner. The generated plan is executed in a simulated environment by the TIAGo robot. We tested our method on a dataset of 12 demonstrations collected from three different participants. The results show that our method is able to generate executable plans from using one single demonstration with a 92% success rate, and 100% when the information from all demonstrations are included, even for previously unseen stacking goals.
Two-Server Distributed ORAM with Sublinear Computation and Constant Rounds

Distributed ORAM (DORAM) is a multi-server variant of Oblivious RAM. Originally proposed to lower bandwidth, DORAM has recently been of great interest due to its applicability to secure computation in the RAM model, where circuit complexity and rounds of communication are equally important metrics of efficiency. All prior DORAM constructions either involve linear work per server (e.g., Floram) or logarithmic rounds of communication between servers (e.g., square root ORAM). In this work, we construct the first DORAM schemes in the 2-server, semi-honest setting that simultaneously achieve sublinear server computation and constant rounds of communication. We provide two constant-round constructions, one based on square root ORAM that has \(O(\sqrt{N}\log N)\) local computation and another based on secure computation of a doubly efficient PIR that achieves local computation of \(O(N ^\epsilon )\) for any \(\epsilon > 0\) but that allows the servers to distinguish between reads and writes. As a building block in the latter construction, we provide secure computation protocols for evaluation and interpolation of multivariate polynomials based on the Fast Fourier Transform, which may be of independent interest.
OTEANN: Estimating the Transparency of Orthographies with an Artificial Neural Network

To transcribe spoken language to written medium, most alphabets enable an unambiguous sound-to-letter rule.However, some writing systems have distanced themselves from this simple concept and little work exists in Natural Language Processing (NLP) on measuring such distance.In this study, we use an Artificial Neural Network (ANN) model to evaluate the transparency between written words and their pronunciation, hence its name Orthographic Transparency Estimation with an ANN (OTEANN).Based on datasets derived from Wikimedia dictionaries, we trained and tested this model to score the percentage of correct predictions in phoneme-tographeme and grapheme-to-phoneme translation tasks.The scores obtained on 17 orthographies were in line with the estimations of other studies.Interestingly, the model also provided insight into typical mistakes made by learners who only consider the phonemic rule in reading and writing.
Epistemic fragmentation poses a threat to the governance of online targeting

Online targeting isolates individual consumers, causing what we call epistemic fragmentation. This phenomenon amplifies the harms of advertising and inflicts structural damage to the public forum. The two natural strategies to tackle the problem of regulating online targeted advertising, increasing consumer awareness and extending proactive monitoring, fail because even sophisticated individual consumers are vulnerable in isolation, and the contextual knowledge needed for effective proactive monitoring remains largely inaccessible to platforms and external regulators. The limitations of both consumer awareness and of proactive monitoring strategies can be attributed to their failure to address epistemic fragmentation. We call attention to a third possibility that we call a civic model of governance for online targeted advertising, which overcomes this problem, and describe four possible pathways to implement this model. Online targeted advertising fuelled by machine learning can lead to the isolation of individual consumers. This problem of ‘epistemic fragmentation’ cannot be tackled with current regulation strategies and a new, civic model of governance for advertising is needed.
Digitalization of contact tracing: balancing data privacy with public health benefit

The COVID-19 pandemic has brought the long-standing public health practice of contact tracing into the public spotlight. While contact tracing and case investigation have been carefully designed to protect privacy, the huge volume of tracing which is being carried out as part of the pandemic response in the United States is highlighting potential concerns around privacy, legality, and equity. Contact tracing during the pandemic has gained particular attention for the new use of digital technologies-both on the consumer side in the form of Exposure Notification applications, and for public health agencies as digital case management software systems enable massive scaling of operations. While the consumer application side of digital innovation has dominated the news and academic discourse around privacy, people are likely to interact more intensively with public health agencies and their use of digital case management systems. Effective use of digital case management for contact tracing requires revisiting the existing legal frameworks, privacy protections, and security practices for management of sensitive health data. The scale of these tools and demands of an unprecedented pandemic response are introducing new risks through the collection of huge volumes of data, and expanding requirements for more adept data sharing among jurisdictions. Public health agencies must strengthen their best practices for data collection and protection even in the absence of comprehensive or clear guidance. This requires navigating a difficult balance between rigorous data protection and remaining highly adaptive and agile.
Cross-language code search using static and dynamic analyses

As code search permeates most activities in software development,code-to-code search has emerged to support using code as a query and retrieving similar code in the search results. Applications include duplicate code detection for refactoring, patch identification for program repair, and language translation. Existing code-to-code search tools rely on static similarity approaches such as the comparison of tokens and abstract syntax trees (AST) to approximate dynamic behavior, leading to low precision. Most tools do not support cross-language code-to-code search, and those that do, rely on machine learning models that require labeled training data. We present Code-to-Code Search Across Languages (COSAL), a cross-language technique that uses both static and dynamic analyses to identify similar code and does not require a machine learning model. Code snippets are ranked using non-dominated sorting based on code token similarity, structural similarity, and behavioral similarity. We empirically evaluate COSAL on two datasets of 43,146Java and Python files and 55,499 Java files and find that 1) code search based on non-dominated ranking of static and dynamic similarity measures is more effective compared to single or weighted measures; and 2) COSAL has better precision and recall compared to state-of-the-art within-language and cross-language code-to-code search tools. We explore the potential for using COSAL on large open-source repositories and discuss scalability to more languages and similarity metrics, providing a gateway for practical,multi-language code-to-code search.
Document Layout Analysis via Dynamic Residual Feature Fusion

The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval. However, it is a challenge to build a DLA system because the training data is very limited and lacks an efficient model. In this paper, we propose an end-to-end united network named Dynamic Residual Fusion Network (DRFN) for the DLA task. Specifically, we design a dynamic residual feature fusion module which can fully utilize low-dimensional information and maintain high-dimensional category information. Besides, to deal with the model overfitting problem that is caused by lacking enough data, we propose the dynamic select mechanism for efficient fine-tuning in limited train data. We experiment with two challenging datasets and demonstrate the effectiveness of the proposed module.
The Construction of a Smart City Energy Efficiency Management System Oriented to the Mobile Data Aggregation of the Internet of Things

Aiming at the current situation of complicated energy-consuming equipment and large energy consumption in the current enterprise, a reference model scheme of an enterprise energy efficiency management platform based on the Internet of Things is analyzed and the dynamically collected energy consumption information is analyzed using the Internet of Things technology and data mining technology. They provide decision-making support for enterprise energy efficiency management decisions and formulation of energy-saving and emission reduction plans. This article analyzes the current status of energy efficiency management in buildings and points out several pain points in energy efficiency management, including installation difficulties, high software development costs, upgrade difficulties, long debugging cycles, and closed systems, and introduces intelligent power distribution monitoring based on the Internet of Things technology to solve these difficulties. In the system, we analyze the actual value brought by energy efficiency management technology through case studies. It can collect scattered information through the Internet of Things, gather it together to form massive data, and obtain the corresponding information through data analysis and processing. The design is reasonable, the practicality is strong, the use effect is good, and it is easy to promote and use.
EVALUATING DISTANCE EDUCATION IN SERBIA DURING THE COVID-19 PANDEMIC

Education is a significant factor which contributes to overcoming social, economic, health, ecological and other challenges. During a pandemic, formal education adapts to ongoing circumstances, and the learning-teaching process transitions to distance education. A study of the advantages and disadvantages of the realization of certain modalities of distance education is significant as it contributes to the improvement of its quality. The aim was to determine how the citizens of Serbia evaluated the quality of distance education during the COVID-19 pandemic. The research questions focused on how they evaluated: distance education based on modality (via Radio Television of Serbia, Google Classroom, Google Meet, Zoom, Viber groups, WhatsApp), the level of involvement of the learners/students during distance learning classes, the quality and extent of their acquired knowledge, and the level of skills that teachers need to effectively teach in distance education. The study presents the results of a survey carried out in 2020 on a sample of 602 adult Serbian citizens. The results confirm the initial hypothesis that certain modalities of distance education received a highly positive evaluation (Google Classroom, Google Meet, Zoom), and that teachers, learners/students, and other citizens provided statistically significant different evaluations of their quality (classes for primary and secondary school students broadcast on the Radio Television of Serbia; classes via Viber groups, on WhatsApp). Citizens feel that distance education enables efficient learning-teaching during the pandemic, confirming the importance of modern technologies for the learning-teaching process in Serbia, and the need for their continued intense development and application in education. Keywords: learners/students, distance education, Serbia, teachers’ skills, the COVID-19 pandemic
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models

Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models.The essential idea is to exit early without passing through all the inference layers at the inference stage.To make accurate predictions for downstream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered.However, much of the research up to now has been limited to use local representations of the exit layer.Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance.To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective.We first take into consideration all the linguistic information embedded in the past layers and further engage the future information which is originally inaccessible for predictions.Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance 1 .
A Clustering Algorithm for Multi-Modal Heterogeneous Big Data With Abnormal Data

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.
A Graph Convolutional Network With Multiple Dependency Representations for Relation Extraction

Dependency analysis can assist neural networks to capture semantic features within a sentence for entity relation extraction (RE). Both hard and soft strategies of encoding dependency tree structure have been developed to balance the beneficial extra information against the unfavorable interference in the task of RE. A wide application of graph convolutional network (GCN) in the field of natural language processing (NLP) has demonstrated its effectiveness in encoding the input sentence with the dependency tree structure, as well as its efficiency in parallel computation. This study proposes a novel GCN-based model using multiple representations to depict the dependency tree from various perspectives, and combines those dependency representations afterward to obtain a better sentence representation for relation classification. This model can maximally draw from the sentence the semantic features relevant to the relationship between entities. Results show that our model achieves state-of-the-art performance in terms of the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score (68.0) on the Text Analysis Conference relation extraction dataset (TACRED). In addition, we verify that the renormalization parameter in the GCN operation should be carefully chosen to help GCN-based models achieve its best performance.