Reviewerly/exHarmony huggingface dataset:
CCBRSN: A System with High Embedding Capacity for Covert Communication in Bitcoin

Covert communication has been using to prevent confidential information from being leaked to an unintended receiver. In this paper, we present a general purpose novel methodology for blockchain-based covert communication system design to be used in Bitcoin environment. Blockchain is a distributed system which combines P2P network, consensus protocol, encryption algorithm to complete the first reliable cryptocurrency system Bitcoin. According to the high security and convenient access of this technology, many applications based on Blockchain such as smart contracts, distributed cloud storage have been developed. However, in the field of covert communication, there are few researches are applied in Blockchain. Therefore in this paper, we propose a system called Covert Communication based on Bitcoin Regtest Self-built Network (CCBRSN), which takes Blockchain as a covert communication channel and embeds encrypted messages into Blockchain's addresses to transmit. In this model, users can transmit covert messages via Blockchain mutually and fast. Finally, we provide experimental analysis for our proposal to show that it is suitable for practical application.
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution Based Text Attacks

Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification. For text classification, however, existing synonym substitution based adversarial attacks are effective but not very efficient to be incorporated into practical text adversarial training. Gradient-based attacks, which are very efficient for images, are hard to be implemented for synonym substitution based text attacks due to the lexical, grammatical and semantic constraints and the discrete text input space. Thereby, we propose a fast text adversarial attack method called Fast Gradient Projection Method (FGPM) based on synonym substitution, which is about 20 times faster than existing text attack methods and could achieve similar attack performance. We then incorporate FGPM with adversarial training and propose a text defense method called Adversarial Training with FGPM enhanced by Logit pairing (ATFL). Experiments show that ATFL could significantly improve the model robustness and block the transferability of adversarial examples.
MaskDGA: An Evasion Attack Against DGA Classifiers and Adversarial Defenses

Domain generation algorithms (DGAs) are commonly used by botnets to generate domain names that bots can use to establish communication channels with their command and control servers.Recent publications presented deep learning classifiers that detect algorithmically generated domain (AGD) names in real time with high accuracy and thus significantly reduce the effectiveness of DGAs for botnet communication.In this paper, we present MaskDGA, an evasion technique that uses adversarial learning to modify AGD names in order to evade inline DGA classifiers, without the need for the attacker to possess any knowledge about the DGA classifier's architecture or parameters.MaskDGA was evaluated on four state-of-the-art DGA classifiers and outperformed the recently proposed CharBot and DeepDGA evasion techniques.We also evaluated MaskDGA on enhanced versions of the same classifiers equipped with common adversarial defenses (distillation and adversarial retraining).While the results show that adversarial retraining has some limited effectiveness against the evasion technique, it is clear that a more resilient detection mechanism is required.We also propose an extension to MaskDGA that allows an attacker to omit a subset of the modified AGD names based on the classification results of the attacker's trained model, in order to achieve a desired evasion rate.
VI-Net—View-Invariant Quality of Human Movement Assessment

We propose a view-invariant method towards the assessment of the quality of human movements which does not rely on skeleton data. Our end-to-end convolutional neural network consists of two stages, where at first a view-invariant trajectory descriptor for each body joint is generated from RGB images, and then the collection of trajectories for all joints are processed by an adapted, pre-trained 2D convolutional neural network (CNN) (e.g., VGG-19 or ResNeXt-50) to learn the relationship amongst the different body parts and deliver a score for the movement quality. We release the only publicly-available, multi-view, non-skeleton, non-mocap, rehabilitation movement dataset (QMAR), and provide results for both cross-subject and cross-view scenarios on this dataset. We show that VI-Net achieves average rank correlation of 0.66 on cross-subject and 0.65 on unseen views when trained on only two views. We also evaluate the proposed method on the single-view rehabilitation dataset KIMORE and obtain 0.66 rank correlation against a baseline of 0.62.
Deep neural network models for identifying incident dementia using claims and EHR datasets

This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created. Four trained models for each cohort, boosted trees, feed forward network, recurrent neural network and recurrent neural network with pre-trained weights, were constructed and their performance compared using validation and test data. The incident model had an AUC of 94.4% and F1 score of 54.1%. Eight years removed from index date the AUC and F1 scores were 80.7% and 25.6%, respectively. The results for the remaining cohorts were between these ranges. Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements. The results of the model at index date indicate that this modeling can be effective at stratifying patients at risk of dementia. At this time, the inability to sustain this quality at longer lead times is more an issue of data availability and quality rather than one of algorithm choices.
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge

Open-domain question answering (QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple-choice QA models know definitions and taxonomic reasoning—two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodology for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evaluation. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer-based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Further, existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept.
Temporal Convolutional Neural Networks for Solar Power Forecasting

We investigate the application of Temporal Convolutional Neural Networks (TCNNs) for solar power forecasting. TCNN is a novel convolutional architecture designed for sequential modelling, which combines causal and dilated convolutions and residual connections. We compare the performance of TCNN with multi-layer feedforward neural networks, and also with recurrent networks, including the state-of-the-art LSTM and GRU recurrent networks. The evaluation is conducted on two Australian datasets containing historical solar and weather data, and weather forecast data for future days. Our results show that TCNN outperformed the other models in terms of accuracy and was able to maintain a longer effective history compared to the recurrent networks. This highlights the potential of convolutional architectures for solar power forecasting tasks.
Attention-based word embeddings using Artificial Bee Colony algorithm for aspect-level sentiment classification

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect, the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV, which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models.
Superposition of causal order enables quantum advantage in teleportation under very noisy channels

Abstract We show that the fidelity offered by the standard teleportation protocol under noisy channels applied successively can be significantly improved, when used in conjunction with a quantum switch. In particular, we find that for two such teleportation channels conjugated with an indefinite causal order, teleportation fidelity beyond the classical threshold is achieved for significantly larger noise than would be possible conventionally. One can even make the effective teleportation channel perfectly faithful for very large noise. We also discuss the generalization of our scheme for more than two pathways, and define a figure of merit in that context.
Uncovering High-dimensional Structures of Projections from Dimensionality Reduction Methods

Projections are conventional methods of dimensionality reduction for information visualization used to transform high-dimensional data into low dimensional space. If the projection method restricts the output space to two dimensions, the result is a scatter plot. The goal of this scatter plot is to visualize the relative relationships between high-dimensional data points that build up distance and density-based structures. However, the Johnson-Lindenstrauss lemma states that the two-dimensional similarities in the scatter plot cannot coercively represent high-dimensional structures. Here, a simplified emergent self-organizing map uses the projected points of such a scatter plot in combination with the dataset in order to compute the generalized U-matrix. The generalized U-matrix defines the visualization of a topographic map depicting the misrepresentations of projected points with regards to a given dimensionality reduction method and the dataset.•The topographic map provides accurate information about the high-dimensional distance and density based structures of high-dimensional data if an appropriate dimensionality reduction method is selected.•The topographic map can uncover the absence of distance-based structures.•The topographic map reveals the number of clusters in a dataset as the number of valleys.
Towards a Secure Signature Scheme Based on Multimodal Biometric Technology: Application for IOT Blockchain Network

Blockchain technology has been commonly used in the last years in numerous fields, such as transactions documenting and monitoring real assets (house, cash) or intangible assets (copyright, intellectual property). The internet of things (IoT) technology, on the other hand, has become the main driver of the fourth industrial revolution, and is currently utilized in diverse fields of industry. New approaches have been established through improving the authentication methods in the blockchain to address the constraints of scalability and protection in IoT operating environments of distributed blockchain technology by control of a private key. However, these authentication mechanisms do not consider security when applying IoT to the network, as the nature of IoT communication with numerous entities all the time in various locations increases security risks resulting in extreme asset damage. This posed many difficulties in finding harmony between security and scalability. To address this gap, the work suggested in this paper adapts multimodal biometrics to strengthen network security by extracting a private key with high entropy. Additionally, via a whitelist, the suggested scheme evaluates the security score for the IoT system with a blockchain smart contract to guarantee that highly secured applications authenticate easily and restrict compromised devices. Experimental results indicate that our system is existentially unforgeable to an efficient message attack, and therefore, decreases the expansion of infected devices to the network by up to 49 percent relative to traditional schemes.
An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things

The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.
The secure steganography for hiding images via GAN

Abstract Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In addition, another important factor that limits the security of steganography is capacity. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. Therefore, this paper proposes a steganography model—HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. The experimental results show that our proposed model is achievable and effective. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security.
Online selection of CMA-ES variants

In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms. We build upon the theoretical work done by van Rijn et al. [PPSN'18], in which the potential of switching between different CMA-ES variants was quantified in the context of a modular CMA-ES framework.
SubjQA: A Dataset for Subjectivity and Review Comprehension

Subjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and wordsense disambiguation.Furthermore, subjectivity is an important aspect of user-generated data.In spite of this, subjectivity has not been investigated in contexts where such data is widespread, such as in question answering (QA).We develop a new dataset which allows us to investigate this relationship.We find that subjectivity is an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance than found in previous work on sentiment analysis.For instance, a subjective question may or may not be associated with a subjective answer.We release an English QA dataset (SUBJQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 domains.
ConjNLI: Natural Language Inference Over Conjunctive Sentences

Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic.Existing NLI stress tests do not consider non-boolean usages of conjunctions and use templates for testing such model knowledge.Hence, we introduce CONJNLI, a challenge stress-test for natural language inference over conjunctive sentences, where the premise differs from the hypothesis by conjuncts removed, added, or replaced.These sentences contain single and multiple instances of coordinating conjunctions ("and", "or", "but", "nor") with quantifiers, negations, and requiring diverse boolean and non-boolean inferences over conjuncts.We find that large-scale pre-trained language models like RoBERTa do not understand conjunctive semantics well and resort to shallow heuristics to make inferences over such sentences.As some initial solutions, we first present an iterative adversarial fine-tuning method that uses synthetically created training data based on boolean and non-boolean heuristics.We also propose a direct model advancement by making RoBERTa aware of predicate semantic roles.While we observe some performance gains, CONJNLI is still challenging for current methods, thus encouraging interesting future work for better understanding of conjunctions. 1
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale

We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums in English.We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines.We also demonstrate that considering a broad selection of source domains is crucial for obtaining the best zero-shot transfer performances, which contrasts the standard procedure that merely relies on the largest and most similar domains.In addition, we extensively study how to best combine multiple source domains.We propose to incorporate self-supervised with supervised multi-task learning on all available source domains.Our best zero-shot transfer model considerably outperforms in-domain BERT and the previous state of the art on six benchmarks.Fine-tuning of our model with in-domain data results in additional large gains and achieves the new state of the art on all nine benchmarks.
Certification of Non-Gaussian States with Operational Measurements

We derive a theoretical framework for the experimental certification of non-Gaussian features of quantum states using double homodyne detection. We rank experimental non-Gaussian states according to the recently defined stellar hierarchy and we propose practical Wigner negativity witnesses. We simulate various use-cases ranging from fidelity estimation to witnessing Wigner negativity. Moreover, we extend results on the robustness of the stellar hierarchy of non-Gaussian states. Our results illustrate the usefulness of double homodyne detection as a practical measurement scheme for retrieving information about continuous variable quantum states.
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks.Besides, previous works usually ignore the interaction between aspect terms when labeling polarities.We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems.Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities.The polarities sequence is designed to depend on the generated aspect terms labels.To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically.The proposed GRACE adopts a post-pretraining BERT as its backbone.Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.