Reviewerly/exHarmony huggingface dataset:
A separation logic for effect handlers

User-defined effects and effect handlers are advertised and advocated as a relatively easy-to-understand and modular approach to delimited control. They offer the ability of suspending and resuming a computation and allow information to be transmitted both ways between the computation, which requests a certain service, and the handler, which provides this service. Yet, a key question remains, to this day, largely unanswered: how does one modularly specify and verify programs in the presence of both user-defined effect handlers and primitive effects, such as heap-allocated mutable state? We answer this question by presenting a Separation Logic with built-in support for effect handlers, both shallow and deep. The specification of a program fragment includes a protocol that describes the effects that the program may perform as well as the replies that it can expect to receive. The logic allows local reasoning via a frame rule and a bind rule. It is based on Iris and inherits all of its advanced features, including support for higher-order functions, user-defined ghost state, and invariants. We illustrate its power via several case studies, including (1) a generic formulation of control inversion, which turns a producer that ``pushes'' elements towards a consumer into a producer from which one can ``pull'' elements on demand, and (2) a simple system for cooperative concurrency, where several threads execute concurrently, can spawn new threads, and communicate via promises.
Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.
Regularized Attentive Capsule Network for Overlapped Relation Extraction

Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts.However, the automatically established training datasets in distant supervision contain low-quality instances with noisy words and overlapped relations, introducing great challenges to the accurate extraction of relations.To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlapped relations in each informal sentence.To discover multiple relation features in an instance, we embed multi-head attention into the capsule network as the low-level capsules, where the subtraction of two entities acts as a new form of relation query to select salient features regardless of their positions.To further discriminate overlapped relation features, we devise disagreement regularization to explicitly encourage the diversity among both multiple attention heads and low-level capsules.Extensive experiments conducted on widely used datasets show that our model achieves significant improvements in relation extraction.
Do Word Embeddings Capture Spelling Variation?

Analyses of word embeddings have primarily focused on semantic and syntactic properties.However, word embeddings have the potential to encode other properties as well.In this paper, we propose a new perspective on the analysis of word embeddings by focusing on spelling variation.In social media, spelling variation is abundant and often socially meaningful.Here, we analyze word embeddings trained on Twitter and Reddit data.We present three analyses using pairs of word forms covering seven types of spelling variation in English.Taken together, our results show that word embeddings encode spelling variation patterns of various types to some extent, even embeddings trained using the skipgram model which does not take spelling into account.Our results also suggest a link between the intentionality of the variation and the distance of the non-conventional spellings to their conventional spellings.
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization

Extractive methods have been proven effective in automatic document summarization.Previous works perform this task by identifying informative contents at sentence level.However, it is unclear whether performing extraction at sentence level is the best solution.In this work, we show that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising alternative.Specifically, we propose extracting sub-sentential units based on the constituency parsing tree.A neural extractive model which leverages the subsentential information and extracts them is presented.Extensive experiments and analyses show that extracting sub-sentential units performs competitively comparing to full sentence extraction under the evaluation of both automatic and human evaluations.Hopefully, our work could provide some inspiration of the basic extraction units in extractive summarization for future research.
A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Research on the current speech recognition system leads to the creation of a noise-resistant system. The Mel Frequency Cepstral Coefficients (MFCC) extraction method becomes a popular method in the speech recognition system. In this paper, the MFCC's weakness of noise interference is the main reason underlies the accomplishment of a robust speech recognition system. Development was carried out by improving the denoising performance using a wavelet transform. Modifications were carried out by analyzing the weakness of the wavelet denoising process on the recognition system using the MFCC method. The analysis was conducted at one of the MFCC stages, the Fast Fourier Transform (FFT) stage. The proposed method was conducted by performing the denoising process using Wavelet only on the noise-related data based on the FFT process' analysis results. The study utilized speech data in the form of eleven isolated words in English added with noise with several different characteristics. Results showed that the proposed method was capable of generating a better accuracy than conventional wavelet denoising methods on the signal to noise ratio (SNR) of 10dB, 15dB, and 20dB using a Fejer Korovkin 6 wavelet type. The highest accuracy increase of the proposed method was in signal to noise ratio (SNR) of 15dB with a rise of 4.63%, followed by a 3.96% increase at 20dB intensity, and 2.3% at 10dB intensity. The performance of the proposed method is then compared with other methods. The results show that the proposed method has the best performance on clean speech and noisy speech at SNR intensities of 10dB, 15dB, and 20dB.
Guoym at SemEval-2020 Task 8: Ensemble-based Classification of Visuo-Lingual Metaphor in Memes

In this paper, we describe our ensemble-based system designed by guoym Team for the SemEval-2020 Task 8, Memotion Analysis.In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects.We train five base classifiers for each type of representation using five-fold cross-validation.Then the outputs of these base classifiers are combined through data-based ensemble method and feature-based ensemble method to make full use of all data and representations from different aspects.Our method achieves the performance within the top 2 ranks in the final leaderboard of Memotion Analysis among 36 Teams.
Virtual Reality in Design Processes

Virtual Reality (VR) opens new possibilities in the fields of architecture, design and engineering. If combined with Building Information Modelling (BIM) or simpler 3D models, it could be possible to walk into buildings not yet built or to examine designed objects in three dimensions before they are made. This literature review examines studies in which VR was used in architecture, design and engineering as part of design processes. The review highlights promising benefits, such as increased understanding of complex issues concerning design tasks, size and dimensions. At the same time, several challenges are revealed, such as the inability of VR-systems to offer satisfactory functionalities for sketching and designing. Finally, the author discusses how VR can be implemented in relevant subjects in lower and upper secondary school.
Studying Taxonomy Enrichment on Diachronic WordNet Versions

Ontologies, taxonomies, and thesauri are used in many NLP tasks.However, most studies are focused on the creation of these lexical resources rather than the maintenance of the existing ones.Thus, we address the problem of taxonomy enrichment.We explore the possibilities of taxonomy extension in a resource-poor setting and present methods which are applicable to a large number of languages.We create novel English and Russian datasets for training and evaluating taxonomy enrichment models and describe a technique of creating such datasets for other languages.
Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing

In this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels.To this end, we developed a hybrid system with the BERT classifier trained with tweets selected using Statistical Sampling Algorithm (SA) and Post-Processed (PP) using an offensive wordlist.Our developed system achieved 34 th position with Macro-averaged F1-score (Macro-F1) of 0.90913 over both offensive and non-offensive classes.We further show comprehensive results and error analysis to assist future research in offensive language identification with noisy labels.
The Self-Perceived Digital Competence of Social Educators in Spain: Influence of Demographic and Professional Variables

In 21st-century society, Information and Communication Technologies establish the ways of communication and socialization. The way we live, study, work, entertain ourselves, etc. have changed, and the patterns of social and economic development demand new skills from citizens. In Spain, the scientific and professional career called Social Education and their social educators, are responsible for promoting and facilitating the adaptation of people to society. Nowadays, these professionals must be aware that one of the fundamental competencies, along with others, is digital competence, and its development is a requirement to agree with contemporary society. This article presents the findings of a research that aims to be a pioneer in the field of study of Social Education in Spain and whose objective is to analyze the self-perceived digital competence of 452 working social educators and determine if demographic variables and professionals influence it. A quantitative, non-experimental, and descriptive method was used, with an ex-post-facto methodology, of a descriptive type in which no kind of treatment was applied to the object of study. The electronic survey technique was used to collect the information. For the analysis of the data, descriptive, inferential and regression tests were carried out. The main results obtained allow us to conclude that neither gender, nor age, nor the Autonomous Community influence the dimensions of management and attitude towards Information and Communication Technologies. On the contrary, professional variables do have an influence on the self-assessment of digital competence.
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis.Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment.Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation.We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet.Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet.Adding text features is however not always helpful for Memotion Analysis.
A Novel Image Encryption Scheme Based on PWLCM and Standard Map

In the past decades, considerable attention has been paid to the chaos-based image encryption schemes owing to their characteristics such as extreme sensitivity to initial conditions and parameters, pseudo-randomness, and unpredictability. However, some schemes have been proven to be insecure due to using a single chaotic system. To increase the security, this work proposes a novel image encryption scheme based on the piecewise linear chaotic map (PWLCM) and the standard map. To the best of our knowledge, it is the first chaos-based image encryption scheme combining the PWLCM with the standard map, which adopts permutation-diffusion structure. Unlike the traditional scrambling way, a hierarchical diffusion strategy, which not only changes the pixel position but also modifies the value, is employed in the permutation phase. The operation model of row-by-row and column-by-column is further used to enhance the efficiency in the diffusion process. Consequently, a good trade-off efficiency and security can be achieved. Furthermore, the numerical simulations and performance analyses illustrate that the proposed encryption scheme can be used in practical application scenarios requiring lightweight security.
TECHSSN at SemEval-2020 Task 12: Offensive Language Detection Using BERT Embeddings

This paper describes the work of identifying the presence of offensive language in social media posts and categorizing a post as targeted to a particular person or not.The work developed by team TECHSSN for solving the Multilingual Offensive Language Identification in Social Media (Task 12) in SemEval-2020 involves the use of deep learning models with BERT embeddings.The dataset is preprocessed and given to a Bidirectional Encoder Representations from Transformers (BERT) model with pretrained weight vectors.The model is retrained and the weights are learned for the offensive language dataset.We have developed a system with the English language dataset.The results are better when compared to the model we developed in SemEval-2019 Task6. Related WorkRecently, we have seen great strides in the research on profanity speech detection in social media, which includes hate speech detection, offensive language identification, and abusive language detection.Several workshops such as GermEval, SemEval, HatEval, and TRAC gain attention of the researchers in this field.Research in hate speech includes work done by Basile et al. (2019), Fortuna and Nunes (2018), Malmasi and Zampieri (2017).Difference between profanity and hate speech, and the challenges involved are discussed in Malmasi and Zampieri
WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets

Communicating through social platforms has become one of the principal means of personal communications and interactions. Unfortunately, healthy communication is often interfered by offensive language that can have damaging effects on the users. A key to fight offensive language on social media is the existence of an automatic offensive language detection system. This paper presents the results and the main findings of SemEval-2020, Task 12 OffensEval Sub-task A Zampieri et al. (2020), on Identifying and categorising Offensive Language in Social Media. The task was based on the Arabic OffensEval dataset Mubarak et al. (2020). In this paper, we describe the system submitted by WideBot AI Lab for the shared task which ranked 10th out of 52 participants with Macro-F1 86.9% on the golden dataset under CodaLab username “yasserotiefy”. We experimented with various models and the best model is a linear SVM in which we use a combination of both character and word n-grams. We also introduced a neural network approach that enhanced the predictive ability of our system that includes CNN, highway network, Bi-LSTM, and attention layers.
An Analysis of Dataset Overlap on Winograd-Style Tasks

The Winograd Schema Challenge (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR).Model performance on the WSC has quickly progressed from chance-level to near-human using neural language models trained on massive corpora.In this paper, we analyze the effects of varying degrees of overlap between these training corpora and the test instances in WSC-style tasks.We find that a large number of test instances overlap considerably with the corpora on which state-of-the-art models are (pre)trained, and that a significant drop in classification accuracy occurs when we evaluate models on instances with minimal overlap.Based on these results, we develop the KNOWREF-60K dataset, which consists of over 60k pronoun disambiguation problems scraped from web data.KNOWREF-60K is the largest corpus to date for WSC-style common-sense reasoning and exhibits a significantly lower proportion of overlaps with current pretraining corpora.
A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AI

Automatic Speech Recognition (ASR) systems are increasingly powerful and more accurate, but also more numerous with several options existing currently as a service (e.g.Google, IBM, and Microsoft).Currently the most stringent standards for such systems are set within the context of their use in, and for, Conversational AI technology.These systems are expected to operate incrementally in real-time, be responsive, stable, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps.In this paper we evaluate the most popular of such systems with metrics and experiments designed with these standards in mind.We also evaluate the speaker diarization (SD) capabilities of the same systems which will be particularly important for dialogue systems designed to handle multi-party interaction.We found that Microsoft has the leading incremental ASR system which preserves disfluent materials and IBM has the leading incremental SD system in addition to the ASR that is most robust to speech overlaps.Google strikes a balance between the two but none of these systems are yet suitable to reliably handle natural spontaneous conversations in real-time.
How LSTM Encodes Syntax: Exploring Context Vectors and Semi-Quantization on Natural Text

Long Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies.However, how such information are reflected in its internal vectors for natural text has not yet been sufficiently investigated.We analyze them by learning a language model where syntactic structures are implicitly given.We empirically show that the context update vectors, i.e. outputs of internal gates, are approximately quantized to binary or ternary values to help the language model to count the depth of nesting accurately, as Suzgun et al. ( 2019) recently showed for synthetic Dyck languages.For some dimensions in the context vector, we show that their activations are highly correlated with the depth of phrase structures, such as VP and NP.Moreover, with an L 1 regularization, we also found that it can be accurately predicted whether a word is inside a phrase structure or not from a small number of components of the context vector.Even for the case of learning from raw text, context vectors are still shown to correlate well with the phrase structures.Finally, we show that natural clusters of the functional words and the parts of speech that trigger phrases are represented in a small but principal subspace of the context-update vector of LSTM.
Leakage-Resilient Outsourced Revocable Certificateless Signature with a Cloud Revocation Server

Certificateless public-key system (CL-PKS) is a significant public-key cryptography and it solves both the key escrow and certificate management problems. Outsourced revocable certificateless public-key system (ORCL-PKS) with a cloud revocation server (CRS) not only provides a revocation mechanism, but also further outsources the revocation functionality to the CRS to reduce the computational burden of the key generation center (KGC). Recently, side-channel attacks have threatened some existing conventional cryptography (including CL-PKS). Indeed, adversaries can apply side-channel attacks to derive fractional constituents of private (or secret) keys to damage the security of these cryptographic protocols (or schemes). To withstand such attacks, leakage-resilient cryptography is an attractive approach. However, little research concerns with leakage-resilient certificateless cryptography. In this paper, the first leakage-resilient outsourced revocable certificateless signature (LR-ORCLS) scheme is presented. The proposed scheme allows adversaries to continually derive fractional constituents of private (or secret) keys and possesses overall unbounded leakage property. In the generic bilinear group (GBG) model, our scheme is shown to be existential unforgeable against adversaries. Finally, the comparisons between the proposed scheme and the previous revocable certificateless signature schemes are provided to demonstrate the merits of the proposed scheme.