Reviewerly/exHarmony huggingface dataset:
BubbleBoard: A Zoom-Based Text Entry Method on Smartwatches

We present a novel text entry method for smartwatches named BubbleBoard. It is based on a soft keyboard with a QWERTY layout in which some characters are in a big font and can be entered with a single touch, while other characters are in a small font and a touch enlarges the pressed character and its neighbors, so that they can be entered with a second touch. Different variants of the method, regarding the use of a static or dynamic layout and the input of space/backspace with keys or gestures were evaluated through a user study, with the one with a dynamic layout and space/backspace keys resulting as the fastest. This variant was then compared to ZoomBoard, an existing text entry method for smartwatches based on key magnification. The experiment showed a 33% advantage in text entry speed in favor of BubbleBoard. In particular, participants typed at 10.7 wpm with BubbleBoard and at 8.0 wpm with ZoomBoard.
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation

Typical generative dialogue models utilize the dialogue history to generate the response.However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy.Intuitively, if the chatbot can foresee in advance what the user would talk about (i.e., the dialogue future) after receiving its response, it could possibly provide a more informative response.Accordingly, we propose a novel dialogue generation framework named ProphetChat that utilizes the simulated dialogue futures in the inference phase to enhance response generation.To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.With the simulated futures, we then utilize the ensemble of a history-to-response generator and a future-to-response generator to jointly generate a more informative response.Experiments on two popular open-domain dialogue datasets demonstrate that ProphetChat can generate better responses over strong baselines, which validates the advantages of incorporating the simulated dialogue futures.
Deep Learning-Based Upscaling for In Situ Volume Visualization

Complementary to the classical use of feature-based condensation and temporal subsampling for in situ visualization, learning-based data upscaling has recently emerged as an interesting approach that can supplement existing in situ volume visualization techniques. By upscaling we mean the spatial or temporal reconstruction of a signal from a reduced representation that requires less memory to store and sometimes even less time to generate. The concrete tasks where upscaling has been shown to work effectively are geometry upscaling, to infer high-resolution geometry images from given low-resolution images of sampled features; upscaling in the data domain, to infer the original spatial resolution of a 3D dataset from a downscaled version; and upscaling of temporally sparse volume sequences, to generate refined temporal features. In this book chapter, we aim at providing a summary of existing learning-based upscaling approaches and a discussion of possible use cases for in situ volume visualization. We discuss the basic foundation of learning-based upscaling, and review existing works in image and video super-resolution from other fields. We then show the specific adaptations and extensions that have been proposed in visualization to realize upscaling tasks beyond color images, discuss how these approaches can be employed for in situ visualization, and provide an outlook on future developments in the field.
Neural Networks and Deep Learning

In this chapter, a brief introduction to the field of artificial neural networks is provided with a focus on deep learning [9], neural network training, and different architectures. Artificial neural networks are powerful pattern recognition machines, and they have proved to be the most successful. Neural networks and deep learning are quite successful at end-to-end learning, and they do not require feature engineering as in traditional machine learning techniques such as SVM and decision trees. Deep learning has achieved unprecedented results in various fields, including signal processing [143, 144], computer vision [145], speech and audio processing [146], and natural language processing [147, 148].
A Generalization of NTRUEncrypt —Cryptosystem Based on Ideal Lattice

The purpose of this article is to extend the theory of circulant matrix to general ideal matrix, and to construct more general NTRU cryptosystem combined with the φ -cyclic code.To understand our construction, first we discuss a more general form of the ordinary cyclic code, namely φ -cyclic code, which firstly appeared in [1] and [2], thus we give a more generalized NTRUEncrypt by replacing finite field with real number field » .
MMEKG: Multi-modal Event Knowledge Graph towards Universal Representation across Modalities

Events are fundamental building blocks of realworld happenings.In this paper, we present a large-scale, multi-modal event knowledge graph named MMEKG.MMEKG unifies different modalities of knowledge via events, which complement and disambiguate each other.Specifically, MMEKG incorporates (i) over 990 thousand concept events with 644 relation types to cover most types of happenings, and (ii) over 863 million instance events connected through 934 million relations, which provide rich contextual information in texts and/or images.To collect billion-scale instance events and relations among them, we additionally develop an efficient yet effective pipeline for textual/visual knowledge extraction system.We also develop an induction strategy to create million-scale concept events and a schema organizing all events and relations in MMEKG.To this end, we also provide a pipeline 1 enabling our system to seamlessly parse texts/images to event graphs and to retrieve multi-modal knowledge at both concept-and instance-levels.
Light-Weight File Fragments Classification Using Depthwise Separable Convolutions

In digital forensics, classification of file fragments is an important step to complete the file carving process. There exist several approaches to identify the type of file fragments without relying on meta-data. Examples of such approaches are using features like header/footer and N-gram to identify the fragment type. Recently, deep learning models have been successfully used to build classification models to achieve this task. In this paper, we propose a light-weight file fragment classification using depthwise separable convolutional neural network model. We show that our proposed model does not only yield faster inference time, but also provide higher accuracy as compared to the state-of-art convolutional neural network based models. In particular, our model achieves an accuracy of 78.45% on the FFT-75 dataset with 100K parameters and 167M FLOPs, which is 24 $$\times $$ faster and 4–5 $$\times $$ smaller than the state-of-the-art classifier in the literature.
Probing Factually Grounded Content Transfer with Factual Ablation

Despite recent success, large neural models often generate factually incorrect text.Compounding this is the lack of a standard automatic evaluation for factuality-it cannot be meaningfully improved if it cannot be measured.Grounded generation promises a path to solving both of these problems: models draw on a reliable external document (grounding) for factual information, simplifying the challenge of factuality.Measuring factuality is also simplified-to factual consistency, testing whether the generation agrees with the grounding, rather than all facts.Yet, without a standard automatic metric for factual consistency, factually grounded generation remains an open problem.We study this problem for content transfer, in which generations extend a prompt, using information from factual grounding.Particularly, this domain allows us to introduce the notion of factual ablation for automatically measuring factual consistency: this captures the intuition that the model should be less likely to produce an output given a less relevant grounding document.In practice, we measure this by presenting a model with two grounding documents, and the model should prefer to use the more factually relevant one.We contribute two evaluation sets to measure this.Applying our new evaluation, we propose multiple novel methods improving over strong baselines.
Two classes of permutation polynomials with Niho exponents over finite fields with even characteristic

In this paper, by transforming the permutation problem into the root distribution problem in the unit circle of certain quadratic and cubic equations, we investigate the permutation behavior of the type f (
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation

Multi-hop question generation focuses on generating complex questions that require reasoning over multiple pieces of information of the input passage.Current models with state-of-the-art performance have been able to generate the correct questions corresponding to the answers.However, most models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning.To address this challenge, we propose the CQG, which is a simple and effective controlled framework.CQG employs a simple method to generate the multi-hop questions that contain key entities in multi-hop reasoning chains, which ensure the complexity and quality of the questions.In addition, we introduce a novel controlled Transformer-based decoder to guarantee that key entities appear in the questions.Experiment results show that our model greatly improves performance, which also outperforms the state-of-the-art model about 25% by 5 BLEU points on HotpotQA 1 .
Personalized recommendation system based on social tags in the era of Internet of Things

Abstract With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the influence of continuous user behavior, in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of &lt;user-tag-item&gt; into two two-dimensional relationships of &lt;user-tag&gt; and &lt;tag-item&gt;. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms.
Modeling Intensification for Sign Language Generation: A Computational Approach

End-to-end sign language generation models do not accurately represent the prosody in sign language.A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters.In this paper, we aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner.We present different strategies grounded in linguistics of sign language that inform how intensity modifiers can be represented in gloss annotations.To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification.We then use a supervised intensity tagger to extend the annotated dataset and obtain labels for the remaining portion of it.This enhanced dataset is then used to train state-of-the-art transformer models for sign language generation.We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics.Human evaluation also indicates a higher preference of the videos generated using our model.
An ELECTRA-Based Model for Neural Coreference Resolution

In last years, coreference resolution has received a sensibly performance boost exploiting different pre-trained Neural Language Models, from BERT to SpanBERT until Longformer. This work is aimed at assessing, for the first time, the impact of ELECTRA model on this task, moved by the experimental evidence of an improved contextual representation and better performance on different downstream tasks. In particular, ELECTRA has been employed as representation layer in an assessed neural coreference architecture able to determine entity mentions among spans of text and to best cluster them. The architecture itself has been optimized: i) by simplifying the modality of representation of spans of text but still considering both the context they appear and their entire content, ii) by maximizing both the number and length of input textual segments to exploit better the improved contextual representation power of ELECTRA, iii) by maximizing the number of spans of text to be processed, since potentially representing mentions, preserving computational efficiency. Experimental results on the OntoNotes dataset have shown the effectiveness of this solution from both a quantitative and qualitative perspective, and also with respect to other state-of-the-art models, thanks to a more proficient token and span representation. The results also hint at the possible use of this solution also for low-resource languages, simply requiring a pre-trained version of ELECTRA instead of language-specific models trained to handle either spans of text or long documents.
A Study on Characteristics and Identification of Smart Ponzi Schemes

A smart Ponzi scheme (SPS) is a financial Ponzi scheme that is implemented and deployed in blockchain through smart contract technology. It is built on treachery and lies, by which, the organizers and speculators jointly deceive innocent investors by fostering a belief in obtaining the expected benefits. The occurrence of SPSs is originated from the vulnerability of the supervision mechanism on the blockchain. Although there are many excellent studies, these contributions overemphasized the methods themselves, did not describe the characteristics of the SPS well, and had certain limitations in practice. We made a thorough study on the characteristics of an SPS and brought out the vital features to identify an SPS for an investor. Based on the analysis of the contributions of predecessors, we propose an approach to test whether a contract is an SPS. This approach could deal with two situations, the contracts to be deployed and the long-run contracts respectively. Of the approach, the priori method can be exploited to distinguish whether the contract to be deployed is an SPS for the runner of a blockchain; the posterior method could protect an investor from being trapped in a fraud. At the same time, the posterior method can also be extended to monitor some contracts dynamically to alert users with the probability to be fallen into SPSs.
Clinical evaluation of a novel atlas-based PET/CT brain image segmentation and quantification method for epilepsy

Background: Positron emission tomography (PET)/computed tomography (CT) with [18F]fluorodeoxyglucose {[18F]FDG} has been shown to be an effective imaging method for the lateralization and localization of epilepsy. However, the efficacy of PET/CT image processing and analysis needs to be improved for clinical application. Our previous research proposed a novel atlas-based image method for PET brain image segmentation and quantification; in this study, we evaluated its effectiveness in clinical patients. Methods: For image segmentation, a head anatomy template was registered to the subject image by integrating dual-modality image registration and landmark-constraint. The localizations of abnormalities were examined by quantitative comparison using the collected database. The PET/CT images of 20 reference patients and 11 patients with epilepsy were used to compare results between the proposed manual method and statistical parameter mapping (SPM). A dice coefficient analysis was performed on the six central brain regions to assess the segmentation effectiveness, and the diagnostic results of the epileptic regions were examined using pathological results as a reference. Results: The dice results of the proposed method were generally higher than those of SPM, with the averaged dice values for the proposed method and SPM being 0.78 and 0.55, respectively, in the reference group (P<0.001), and 0.73 and 0.48, respectively, in the epileptic group (P<0.001). Our proposed method detected all the pathologically reported epileptic defects; however, using the visual assessment method, epileptic defects were missed in three patients. Both the proposed and visual assessment methods incorrectly identified non-epileptic areas as epileptic areas. Conclusions: The results provide strong evidence of the feasibility of using our proposed method for accurate brain region segmentation in the diagnosis of epilepsy. Our atlas-based approach has promise for clinical application in the image processing and diagnosis of patients with epilepsy.
Magphi: Sequence extraction tool from FASTA and GFF3 files using seed pairs

Researchers working with genomes originating from microorganisms often work with multiple genomes in a single analysis.The number of genomes in datasets can pose challenges when it comes to extracting specific regions of interest from multiple genomes.Manual extraction of regions becomes impractical and time consuming when datasets exceed 10-20 genomes.The complexity of this task increases when working within complex regions of genomes that may not assemble into a single contiguous sequence using some existing technologies such as short read-based sequencing technologies.Therefore, automation is required as datasets of microbial genomes routinely consist of tens or hundreds of genomes.Here we present Magphi, a BLAST (Altschul et al., 1990;Mount, 2007) based contig aware genome extraction tool utilising seed sequences to identify and extract regions of interest.
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model

Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns. In this paper, we propose a generic and language-independent strategy for multilingual GEC, which can train a GEC system effectively for a new non-English language with only two easy-to-access resources: 1) a pre-trained cross-lingual language model (PXLM) and 2) parallel translation data between English and the language. Our approach creates diverse parallel GEC data without any language-specific operations by taking the non-autoregressive translation generated by PXLM and the gold translation as error-corrected sentence pairs. Then, we reuse PXLM to initialize the GEC model and pre-train it with the synthetic data generated by itself, which yields further improvement. We evaluate our approach on three public benchmarks of GEC in different languages. It achieves the state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian). Further analysis demonstrates that our data construction method is complementary to rule-based approaches.
Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection

The rapid development of face forgery techniques has drawn growing attention due to security concerns. Existing deepfake video detection methods always attempt to capture the discriminative features by directly exploiting static temporal convolution to mine temporal inconsistency, without explicit exploration on the diverse temporal dynamics of different forged regions. To effectively and comprehensively capture the various inconsistency, in this paper, we propose a novel Region-Aware Temporal Filter (RATF) module which automatically generates corresponding temporal filters for different spatial regions. Specifically, we decouple the dynamic temporal kernel into a set of region-agnostic basic filters and region-sensitive aggregation weights. And different weights guide the corresponding regions to adaptively learn temporal inconsistency, which greatly enhances the overall representational ability. Moreover, to cover the long-term temporal dynamics, we divide the video into multiple snippets and propose a Cross-Snippet Attention (CSA) to promote the cross-snippet information interaction. Extensive experiments and visualizations on several benchmarks demonstrate the effectiveness of our method against state-of-the-art competitors.
Weakening the Influence of Clothing: Universal Clothing Attribute Disentanglement for Person Re-Identification

Most existing Re-ID studies focus on the short-term cloth-consistent setting and thus dominate by the visual appearance of clothing. However, the same person would wear different clothes and different people would wear the same clothes in reality, which invalidates these methods. To tackle the challenge of clothes change, we propose a Universal Clothing Attribute Disentanglement network (UCAD) which can effectively weaken the influence of clothing (identity-unrelated) and force the model to learn identity-related features that are unrelated to the worn clothing. For further study of Re-ID in cloth-changing scenarios, we construct a large-scale dataset called CSCC with the following unique features: (1) Severe: A large number of people have cloth-changing over four seasons. (2) High definition: The resolution of the cameras ranges from 1920×1080 to 3840×2160, which ensures that the recorded people are clear. Furthermore, we provide two variants of CSCC considering different degrees of cloth-changing, namely moderate and severe, so that researchers can effectively evaluate their models from various aspects. Experiments on several cloth-changing datasets including our CSCC and short-term dataset Market-1501 prove the superiority of UCAD. The dataset is available at https://github.com/yomin-y/UCAD.