Reviewerly/exHarmony huggingface dataset:
Detecting Impoliteness and Incivility in Online Discussions

Impoliteness and incivility in online discussions have recently been discussed as relevant issues in communication science. However, automatically detecting these concepts with computational methods is challenging. In our study, we build and compare supervised classification models to predict impoliteness and incivility in online discussions on German media outlets on Facebook. Using a sample of 10,000 hand-coded user comments and a theory-grounded coding scheme, we develop classifiers on different feature sets including unigram and n-gram distributions as well as various dictionary-based features. Our findings show that impoliteness and incivility can be measured to a certain extent on the word level of a comment, but the models suffer from high misclassification rates, even if lexical resources are included. This is mainly because the classifiers cannot reveal subtle forms of incivility and because comment authors often use predictive words of incivility or impoliteness in non-offensive ways or in different contexts. Still, when applying the classifiers to a comparable set of comments, we find that the machine-coded categories and the hand-coded categories reveal similar patterns regarding the distribution of and the user reactions to uncivil/impolite comments. The findings of our study therefore provide new insights into the supervised machine learning approach to the detection of different forms of offensive language.
Improving Object Detection with Inverted Attention

Improving object detectors against occlusion, blur and noise is a critical step to deploy detectors in real applications. Since it is not possible to exhaust all image defects and occlusions through data collection, many researchers seek to generate occluded samples. The generated hard samples are either images or feature maps with coarse patches dropped out in the spatial dimensions. Significant overheads are required in generating hard samples and/or estimating drop-out patches using extra network branches. In this paper, we improve object detectors using a highly efficient and fine-grain mechanism called Inverted Attention (IA). Different from the original detector network that only focuses on the dominant part of objects, the detector network with IA iteratively inverts attention on feature maps which pushes the detector to discover new discriminative clues and puts more attention on complementary object parts, feature channels and even context. Our approach (1) operates along both the spatial and channels dimensions of the feature maps; (2) requires no extra training on hard samples, no extra network parameters for attention estimation, and no testing overheads. Experiments show that our approach consistently improved state-of-the-art detectors on benchmark databases.
Does virtual reality training improve veterinary students’ first canine surgical performance?

Background Virtual reality (VR) applications are effective tools in many educational disciplines. A minimally interactive VR application allowing stereoscopic viewing of surgical videos has been developed to aid veterinary students learning to perform surgery. We sought to describe how students used the VR application while preparing to perform their first canine sterilisation surgery and compare surgical performance of students who prepared using traditional methods with students who also used VR. Methods Third‐year veterinary students (n=44) were randomised into control and VR groups in a parallel superiority randomised controlled trial. All were given lectures, videos and skills practice on models. VR group students were also given a VR application and headset to view stereoscopic surgical videos. Blinded raters scored a subset of students (n=19) as they performed their first canine ovariohysterectomy. Results and conclusions Groups spent similar time preparing to perform surgery, potentially because of the rigour of students’ non‐surgical course load. When VR training was added to an already comprehensive surgical skills curriculum, students watched VR videos for a median of 90 min. Groups did not differ in surgical performance scores or time. A larger study of the VR application with prescribed use guidelines would be a helpful subsequent study.
Comparative Analysis of Consensus Algorithms of Blockchain Technology

In today’s era of big data and machine learning, IoT is playing a very crucial role in nearly all areas like social, economic, political, education, health care. This drastic increase in data creates security, privacy, and trust issues in the era of the Internet. The responsibility of IT is to ensure the privacy and security for huge incoming information and data due to the drastic evolution of the IoT in the coming years. The blockchain has emerged as one of the major technologies that have the potential to transform the way of sharing the huge information and increase trust among. Building trust in a distributed and decentralized environment without the call for a trusted third party is a technological challenge for researchers. Due to the emergence of IoT, the huge and critical information is available over the Internet. The trust over the information is reduced drastically, causing an increase in security and privacy concern day by day. Blockchain is one of the best-emerging technologies for ensuring privacy and security by using cryptographic algorithms and hashing. We will discuss the basics of blockchain technology, consensus algorithms, comparison of important consensus algorithms, and areas of application.
Joint specific and correlated information exploration for multi-view action clustering

Human action clustering is crucial in many practical applications. However, existing action clustering methods work in a single-view manner, which always ignore the relationships among different views and fail to discover correct clusters as the viewpoint and position change. To address the challenges, we propose a unified framework for multi-view human action clustering. First, we design a new Bag-of-Shared-Words (BoSW) model to discover the view-shared visual words that preserve the consistency among visual words of different views. Then, we obtain a more discriminative feature representation, from which the view correlation can be fully explored. Then, we present a novel JOint INformation boTtleneck (JOINT) algorithm to jointly exploit both the view-specific and view-correlated information to improve the action clustering performance. Specifically, JOINT formulates the problem as minimizing an information loss function, which compresses the actions of each view while jointly preserving the complementary view-specific information and correlated information among views. To solve the proposed objective function, a new sequential procedure is presented to guarantee convergence to a local optimal solution. Extensive experiments on three challenging multi-view single-person and interactive action datasets demonstrate the superiority of our algorithm.
Saturation Based Iterative Approach for Single Image Dehazing

In this letter, we explore the dark channel prior from a new perspective. By re-formulating the dark channel, it is found that the dark channel correlates closely with the saturation and brightness. A novel way to estimate the transmission without the need to compute the dark channel is then presented and it can prevent the transmission from being under-estimated. In order to discourage the scene radiance from becoming over-saturation and remove the haze effectively, an iterative approach with a tolerable bound on the saturation is proposed. Qualitative and quantitative experimental results demonstrate that the proposed algorithm can effectively restore the scene radiance with a comparable or better visual quality than many other algorithms. Furthermore, it runs faster than most state-of-the-arts.
Long-distance transmission of quantum key distribution coexisting with classical optical communication over a weakly-coupled few-mode fiber

Quantum key distribution (QKD) is one of the most practical applications in quantum information processing, which can generate information-theoretical secure keys between remote parties. With the help of the wavelength-division multiplexing technique, QKD has been integrated with the classical optical communication networks. The wavelength-division multiplexing can be further improved by the mode-wavelength dual multiplexing technique with few-mode fiber (FMF), which has additional modal isolation and large effective core area of mode, and particularly is practical in fabrication and splicing technology compared with the multi-core fiber. Here, we present for the first time a QKD implementation coexisting with classical optical communication over weakly-coupled FMF using all-fiber mode-selective couplers. The co-propagation of QKD with one 100 Gbps classical data channel at -2.60 dBm launched power is achieved over 86 km FMF with 1.3 kbps real-time secure key generation. Compared with single-mode fiber using wavelength-division multiplexing, given the same fiber-input power, the Raman noise in FMF using the mode-wavelength dual multiplexing is reduced by 86% in average. Our work implements an important approach to the integration between QKD and classical optical communication and previews the compatibility of quantum communications with the next-generation mode division multiplexing networks.
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied sensitivity-based adversarial examples introduce semantically-small changes to an input that result in a different model prediction. This paper studies a complementary failure mode, invariance-based adversarial examples, that introduce minimal semantic changes that modify an input's true label yet preserve the model's prediction. We demonstrate fundamental tradeoffs between these two types of adversarial examples. We show that defenses against sensitivity-based attacks actively harm a model's accuracy on invariance-based attacks, and that new approaches are needed to resist both attack types. In particular, we break state-of-the-art adversarially-trained and certifiably-robust models by generating small perturbations that the models are (provably) robust to, yet that change an input's class according to human labelers. Finally, we formally show that the existence of excessively invariant classifiers arises from the presence of overly-robust predictive features in standard datasets.
Causal Discovery from Heterogeneous/Nonstationary Data

It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a method to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the driving force of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional representation of changes. The proposed methods are nonparametric, with no hard restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that data heterogeneity benefits causal structure identification even with particular types of confounders. Finally, we show the connection between heterogeneity/nonstationarity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock market data) are presented to demonstrate the efficacy of the proposed methods.
SiGamal: A Supersingular Isogeny-Based PKE and Its Application to a PRF

We propose two new supersingular isogeny-based public key encryptions: SiGamal and C-SiGamal. They were developed by giving an additional point of the order $$2^r$$ to CSIDH. SiGamal is similar to ElGamal encryption, while C-SiGamal is a compressed version of SiGamal. We prove that SiGamal and C-SiGamal are IND-CPA secure without using hash functions under a new assumption: the P-CSSDDH assumption. This assumption comes from the expectation that no efficient algorithm can distinguish between a random point and a point that is the image of a public point under a hidden isogeny. Next, we propose a Naor-Reingold type pseudo random function (PRF) based on SiGamal. If the P-CSSDDH assumption and the CSSDDH $$^*$$ assumption, which guarantees the security of CSIDH that uses a prime p in the setting of SiGamal, hold, then our proposed function is a pseudo random function. Moreover, we estimate that the computational costs of group actions to compute our proposed PRF are about $$\sqrt{\frac{8T}{3\pi }}$$ times that of the group actions in CSIDH, where T is the Hamming weight of the input of the PRF. Finally, we experimented with group actions in SiGamal and C-SiGamal. The computational costs of group actions in SiGamal-512 with a 256-bit plaintext message space were about 2.62 times that of a group action in CSIDH-512.
IMG-Net: inner-cross-modal attentional multigranular network for description-based person re-identification

Given a natural language description, description-based person re-identification aims to retrieve images of the matched person from a large-scale visual database. Due to the existing modality heterogeneity, it is challenging to measure the cross-modal similarity between images and text descriptions. Many of the existing approaches usually utilize a deep-learning model to encode local and global fine-grained features with a strict uniform partition strategy. This breaks the part coherence, making it difficult to capture meaningful information from the within-part and semantic information among body parts. To address this issue, we proposed an inner-cross-modal attentional multigranular network (IMG-Net) to incorporate inner-modal self-attention and cross-modal hard-region attention with the fine-grained model for extracting the multigranular semantic information. Specifically, the inner-modal self-attention module is proposed to address the within-part consistency broken problem using both spatial-wise and channel-wise information. Following it is a multigranular feature extraction module, which is used to extract rich local and global visual and textual features with the help of group normalization (GN). Then a cross-modal hard-region attention module is proposed to obtain the local visual representation and phrase representation. Furthermore, a GN is used instead of batch normalization for the accurate batch statistics estimation. Comprehensive experiments with ablation analysis demonstrate that IMG-Net achieves the state-of-the-art performance on the CUHK-PEDES dataset and outperforms other previous methods significantly.
Is Multilingual BERT Fluent in Language Generation

The multilingual BERT model is trained on 104 languages and meant to serve as a universal language model and tool for encoding sentences. We explore how well the model performs on several languages across several tasks: a diagnostic classification probing the embeddings for a particular syntactic property, a cloze task testing the language modelling ability to fill in gaps in a sentence, and a natural language generation task testing for the ability to produce coherent text fitting a given context. We find that the currently available multilingual BERT model is clearly inferior to the monolingual counterparts, and cannot in many cases serve as a substitute for a well-trained monolingual model. We find that the English and German models perform well at generation, whereas the multilingual model is lacking, in particular, for Nordic languages. The code of the experiments in the paper is available at: https://github.com/TurkuNLP/bert-eval
Detection and Classification of Acute Lymphocytic Leukemia

The research work aims to develop an automated detection and classification method for acute lymphocytic leukemia (ALL). Extraction of lymphocytes is accomplished by the color based k-means clustering technique. Then, shape, texture, and color features are extracted from the segmented image. Gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) algorithms are used to extract the features of nucleus. Moreover, Principal component analysis (PCA) is applied for dimensional reduction. Finally, an SVM (support vector machine) with an RBF kernel is employed to classify WBCs. The proposed method yields promising results with 96.00% accuracy and 92.64% sensitivity.
SpeedySpeech: Efficient Neural Speech Synthesis

While recent neural sequence-to-sequence models have greatly improved the quality of speech synthesis, there has not been a system capable of fast training, fast inference and high-quality audio synthesis at the same time. We propose a student-teacher network capable of high-quality faster-than-real-time spectrogram synthesis, with low requirements on computational resources and fast training time. We show that self-attention layers are not necessary for generation of high quality audio. We utilize simple convolutional blocks with residual connections in both student and teacher networks and use only a single attention layer in the teacher model. Coupled with a MelGAN vocoder, our model's voice quality was rated significantly higher than Tacotron 2. Our model can be efficiently trained on a single GPU and can run in real time even on a CPU. We provide both our source code and audio samples in our GitHub repository.
Class LM and Word Mapping for Contextual Biasing in End-to-End ASR

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the speech recognition community. They convert speech input to text units in a single trainable Neural Network model. In ASR, many utterances contain rich named entities. Such named entities may be user or location specific and they are not seen during training. A single model makes it inflexible to utilize dynamic contextual information during inference. In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference. We also propose a simple method to adjust the cost discrepancy between the context FST and the base model. This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances. Although an E2E model does not need pronunciation dictionary, it's interesting to make use of existing pronunciation knowledge to improve accuracy. In this paper, we propose an algorithm to map the rare entity words to common words via pronunciation and treat the mapped words as an alternative form to the original word during recognition. This algorithm further reduces the WER on the named entity utterances by another 31%.
Revealing the complexity of automotive software

Software continues its procession into the core of the modern cars. Sophisticated functionalities, like connectivity and active safety, provide gratifying comfort to its users. With the sophisticated functionality, however, comes the underlying complexity that grows overwhelmingly year by year. But the invisibility of software hinders the practitioners and researchers grasping the magnitude of complexity thoroughly. Rather, from time to time, the consequences of complexity surface in forms of ultra-high design efforts, waves of defect reports, and explosions of warranty costs. This article reveals the complexity of software in four key areas of automotive software development. It points out that the existing practices are severely insufficient for systematic complexity management.
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks

Batch normalization dramatically increases the largest trainable depth of residual networks, and this benefit has been crucial to the empirical success of deep residual networks on a wide range of benchmarks. We show that this key benefit arises because, at initialization, batch normalization downscales the residual branch relative to the skip connection, by a normalizing factor on the order of the square root of the network depth. This ensures that, early in training, the function computed by normalized residual blocks in deep networks is close to the identity function (on average). We use this insight to develop a simple initialization scheme that can train deep residual networks without normalization. We also provide a detailed empirical study of residual networks, which clarifies that, although batch normalized networks can be trained with larger learning rates, this effect is only beneficial in specific compute regimes, and has minimal benefits when the batch size is small.
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering

Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach. We conduct extensive experiments while proposing pre-training tasks, methodology and also an improved dataset with more complex and balanced questions of different types. The proposed methodology shows a significant accuracy improvement compared to the state-of-the-art approaches on various chart Q/A datasets, while outperforming even human baseline on the DVQA Dataset. We also demonstrate interpretability while examining different components in the inference pipeline.