Reviewerly/exHarmony huggingface dataset:
Inductive Anomaly Detection on Attributed Networks

Anomaly detection on attributed networks has attracted a surge of research attention due to its broad applications in various high-impact domains, such as security, finance, and healthcare. Nonetheless, most of the existing efforts do not naturally generalize to unseen nodes, leading to the fact that people have to retrain the detection model from scratch when dealing with newly observed data. In this study, we propose to tackle the problem of inductive anomaly detection on attributed networks with a novel unsupervised framework: Aegis (adversarial graph differentiation networks). Specifically, we design a new graph neural layer to learn anomaly-aware node representations and further employ generative adversarial learning to detect anomalies among new data. Extensive experiments on various attributed networks demonstrate the efficacy of the proposed approach.
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations

To increase trust in artificial intelligence systems, a promising research direction consists of designing neural models capable of generating natural language explanations for their predictions.In this work, we show that such models are nonetheless prone to generating mutually inconsistent explanations, such as "Because there is a dog in the image."and "Because there is no dog in the [same] image.",exposing flaws in either the decision-making process of the model or in the generation of the explanations.We introduce a simple yet effective adversarial framework for sanity checking models against the generation of inconsistent natural language explanations.Moreover, as part of the framework, we address the problem of adversarial attacks with full target sequences, a scenario that was not previously addressed in sequence-to-sequence attacks.Finally, we apply our framework on a state-of-the-art neural natural language inference model that provides natural language explanations for its predictions.Our framework shows that this model is capable of generating a significant number of inconsistent explanations.PREMISE: A guy in a red jacket is snowboarding in midair.ORIGINAL HYPOTHESIS: A guy is outside in the snow.
iSarcasm: A Dataset of Intended Sarcasm

We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection.The former occurs when an utterance is sarcastic from the perspective of its author, while the latter occurs when the utterance is interpreted as sarcastic by the audience.We show the limitations of previous labelling methods in capturing intended sarcasm and introduce the iSarcasm dataset of tweets labeled for sarcasm directly by their authors.Examining the state-of-theart sarcasm detection models on our dataset showed low performance compared to previously studied datasets, which indicates that these datasets might be biased or obvious and sarcasm could be a phenomenon under-studied computationally thus far.By providing the iSarcasm dataset, we aim to encourage future NLP research to develop methods for detecting sarcasm in text as intended by the authors of the text, not as labeled under assumptions that we demonstrate to be sub-optimal.
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation

Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents.Motivated by the intuition that developers usually retrieve resources on the web when writing code, we explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation.Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa.The code and resources are available at https://github.com/neulab/external-knowledge-codegen.
Lexically Constrained Neural Machine Translation with Levenshtein Transformer

This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation.Previous work either required re-training existing models with the lexical constraints or incorporating them during beam search decoding with significantly higher computational overheads.Leveraging the flexibility and speed of a recently proposed Levenshtein Transformer model (Gu et al., 2019), our method injects terminology constraints at inference time without any impact on decoding speed.Our method does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries.Experiments on English-German WMT datasets show that our approach improves an unconstrained baseline and previous approaches.
A wavelet denoising approach based on unsupervised learning model

Abstract Image denoising plays an important role in image processing, which aims to separate clean images from noisy images. A number of methods have been presented to deal with this practical problem over the past several years. The best currently available wavelet-based denoising methods take advantage of the merits of the wavelet transform. Most of these methods, however, still have difficulties in defining the threshold parameter which can limit their capability. In this paper, we propose a novel wavelet denoising approach based on unsupervised learning model. The approach taken aims at exploiting the merits of the wavelet transform: sparsity, multi-resolution structure, and similarity with the human visual system, to adapt an unsupervised dictionary learning algorithm for creating a dictionary devoted to noise reduction. Using the K-Singular Value Decomposition (K-SVD) algorithm, we obtain an adaptive dictionary by learning over the wavelet decomposition of the noisy image. Experimental results on benchmark test images show that our proposed method achieves very competitive denoising performance and outperforms state-of-the-art denoising methods, especially in the peak signal to noise ratio (PSNR), the structural similarity (SSIM) index, and visual effects with different noise levels.
User personality prediction based on topic preference and sentiment analysis using LSTM model

Based on the original text information, this paper converts the users' theme preferences and text sentiment features into attention information and combines different forms with the LSTM (Long Short-Term Memory) model to predict the personality characteristics of social network users. Finally, the experimental results of multiple groups’ show that the Attention-based LSTM model proposed in the paper can achieve better results than the currently popular methods in the recognition of user personality traits and that the model has good generalization ability.
A Novel Image Encryption Scheme Based on 2D Fractional Chaotic Map, DWT and 4D Hyper-chaos

In this paper, a novel image encryption scheme based on a fractional-order Henon chaotic map, a two-dimensional (2D) Discrete Wavelet Transform (DWT) and a four-dimensional (4D) hyperchaotic system is proposed. Firstly, the original image is transformed and scrambled by the 2D DWT, and then the image is shuffled with the fractional-order Henon chaotic time series. Finally, the shuffled image is diffused and encrypted by the 4D hyperchaos system. Through the application of DWT and high-low dimensional chaotic systems, the encryption effect of this algorithm is better than those done by single or ordinary chaotic encryption algorithm, and it has a larger key space and higher security. The experimental tests show that the system has good statistical characteristics, such as histogram analysis, correlation coefficient analysis, key space and key sensitivity, information entropy analysis and so on. The encryption algorithm also passes the relevant security attack tests with good security.
Saturnin: a suite of lightweight symmetric algorithms for post-quantum security

The cryptographic algorithms needed to ensure the security of our communications have a cost. For devices with little computing power, whose number is expected to grow significantly with the spread of the Internet of Things (IoT), this cost can be a problem. A simple answer to this problem is a compromise on the security level: through a weaker round function or a smaller number of rounds, the security level can be decreased in order to cheapen the implementation of the cipher. At the same time, quantum computers are expected to disrupt the state of the art in cryptography in the near future. For public-key cryptography, the NIST has organized a dedicated process to standardize new algorithms. The impact of quantum computing is harder to assess in the symmetric case but its study is an active research area.In this paper, we specify a new block cipher, Saturnin, and its usage in different modes to provide hashing and authenticated encryption in such a way that we can rigorously argue its security in the post-quantum setting. Its security analysis follows naturally from that of the AES, while our use of components that are easily implemented in a bitsliced fashion ensures a low cost for our primitives. Our aim is to provide a new lightweight suite of algorithms that performs well on small devices, in particular micro-controllers, while providing a high security level even in the presence of quantum computers. Saturnin is a 256-bit block cipher with a 256-bit key and an additional 9-bit parameter for domain separation. Using it, we built two authenticated ciphers and a hash function.• Saturnin-CTR-Cascade is an authenticated cipher using the counter mode and a separate MAC. It requires two passes over the data but its implementation does not require the inverse block cipher.• Saturnin-Short is an authenticated cipher intended for messages with a length strictly smaller than 128 bits which uses only one call to Saturnin to providenconfidentiality and integrity.• Saturnin-Hash is a 256-bit hash function. In this paper, we specify this suite of algorithms and argue about their security in both the classical and the post-quantum setting.&#x0D; https://project.inria.fr/saturnin/
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models that encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on the AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively. 1
ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Multimodal classification methods using different modalities have great advantages over traditional single-modality-based ones for the diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). With the increasing amount of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become a crucial research direction for AD classification. However, traditional methods usually depict the data structure using pre-defined similarity matrix as a priori, which is difficult to precisely measure the intrinsic relationship across different modalities in high-dimensional space. In this paper, we propose a novel multimodal feature selection method called Adaptive-Similarity-based Multi-modality Feature Selection (ASMFS) which performs adaptive similarity learning and feature selection simultaneously. Specifically, a similarity matrix is learned by jointly considering different modalities and at the same time, an efficient feature selection is conducted by imposing group sparsity-inducing l2,1-norm constraint. Evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline MRI and FDG-PET imaging data collected from 51 AD, 43 MCI converters (MCI-C), 56 MCI non-converters (MCI-NC) and 52 normal controls (NC), we demonstrate the effectiveness and superiority of our proposed method against other state-of-the-art approaches for multi-modality classification of AD/MCI.
An optimized K-Nearest Neighbor based breast cancer detection

In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.
Understanding data search as a socio-technical practice

Open research data are heralded as having the potential to increase effectiveness, productivity and reproducibility in science, but little is known about the actual practices involved in data search. The socio-technical problem of locating data for reuse is often reduced to the technological dimension of designing data search systems. We combine a bibliometric study of the current academic discourse around data search with interviews with data seekers. In this article, we explore how adopting a contextual, socio-technical perspective can help to understand user practices and behaviour and ultimately help to improve the design of data discovery systems.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

Dialogue systems play an increasingly important role in various aspects of our daily life.It is evident from recent research that dialogue systems trained on human conversation data are biased.In particular, they can produce responses that reflect people's gender prejudice.Many debiasing methods have been developed for various NLP tasks, such as word embedding.However, they are not directly applicable to dialogue systems because they are likely to force dialogue models to generate similar responses for different genders.This greatly degrades the diversity of the generated responses and immensely hurts the performance of the dialogue models.In this paper, we propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance.Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.The implementation of the proposed framework is released 1 .
Continuous-variable quantum repeater based on quantum scissors and mode multiplexing

This paper presents a continuous variable, second generation, quantum repeater scheme for entanglement distribution over a lossy bosonic channel that beats the direct transmission exponential rate-loss tradeoff. The scheme is primarily based on noiseless linear amplification with the quantum scissors and a layer of switched, mode multiplexing
Resource-efficient verification of quantum computing using Serfling’s bound

Abstract Verifying quantum states is central to certifying the correct operation of various quantum information processing tasks. In particular, in measurement-based quantum computing, checking whether correct graph states are generated is essential for reliable quantum computing. Several verification protocols for graph states have been proposed, but none of these are particularly resource efficient: multiple copies are required to extract a single state that is guaranteed to be close to the ideal one. The best protocol currently known requires O ( n 15 ) copies of the state, where n is the size of the graph state. In this paper, we construct a significantly more resource-efficient verification protocol for graph states that only requires O ( n 5 log n ) copies. The key idea is to employ Serfling’s bound, which is a probability inequality in classical statistics. Utilizing Serfling’s bound also enables us to generalize our protocol for qudit and continuous-variable graph states. Constructing a resource-efficient verification protocol for them is non-trivial. For example, the previous verification protocols for qubit graph states that use the quantum de Finetti theorem cannot be generalized to qudit and continuous-variable graph states without tremendously increasing the resource overhead. This is because the overhead caused by the quantum de Finetti theorem depends on the local dimension. On the other hand, in our protocol, the resource overhead is independent of the local dimension, and therefore generalizing to qudit or continuous-variable graph states does not increase the overhead. The flexibility of Serfling’s bound also makes our protocol robust: our protocol accepts slightly noisy but still useful graph states.
HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification

We introduce HOVER (HOppy VERification), a dataset for many-hop evidence extraction and fact verification.It challenges models to extract facts from several Wikipedia articles that are relevant to a claim and classify whether the claim is SUPPORTED or NOT-SUPPORTED by the facts.In HOVER, the claims require evidence to be extracted from as many as four English Wikipedia articles and embody reasoning graphs of diverse shapes.Moreover, most of the 3/4-hop claims are written in multiple sentences, which adds to the complexity of understanding long-range dependency relations such as coreference.We show that the performance of an existing stateof-the-art semantic-matching model degrades significantly on our dataset as the number of reasoning hops increases, hence demonstrating the necessity of many-hop reasoning to achieve strong results.We hope that the introduction of this challenging dataset and the accompanying evaluation task will encourage research in many-hop fact retrieval and information verification.
Steem Blockchain: Mining the Inner Structure of the Graph

Since their introduction, Online Social Networks (OSNs) have transformed the way people interact with each other. Lately, a new trend is rising in the development of OSNs, fueled by an increasing interest of the blockchain technology and the benefits it can bring to the world of OSNs. Blockchain Online Social Media (BOSMs) are Social Media applications that are supported by the blockchain technology. Thanks to a blockchain, BOSMs either try to enforce the privacy of the users or try to redistribute with their users the economic wealth generated by the platform through a rewarding system. There are countless BOSMs available which incorporate a rewarding system. Among them, Steemit can be considered the most well-known platform exceeding 1 million registered users. Steemit is supported by the blockchain Steem, which is a blockchain that natively supports the development of social applications by the usage of transactions that model social activity. Even if other important blockchains, such as Ethereum has been widely analysed, at the best of our knowledge, no study exists concerning the topology of the transactions graph of Steem. The main goal of this paper is to study the structure of the Steem transaction graph to understand its characteristics and unveil crucial knowledge concerning their users. More in detail, we build the Interactions Graph and, after its study, we evaluate three subgraphs that capture its social and monetary aspects. The degree distributions of the graphs follow a power-law. Additionally, we detect a substantial number of bots that offer paid services on the platform among the most active users. Lastly, the investigation of the four analysed graphs through a bow-tie structure, suggesting that half of the users have a passive social behaviour and that 80% of the users tend to accrue economic value
If beam search is the answer, what was the question?

Quite surprisingly, exact maximum a posteriori (MAP) decoding of neural language generators frequently leads to low-quality results (Stahlberg and Byrne, 2019).Rather, most state-of-the-art results on language generation tasks are attained using beam search despite its overwhelmingly high search error rate.This implies that the MAP objective alone does not express the properties we desire in text, which merits the question: if beam search is the answer, what was the question?We frame beam search as the exact solution to a different decoding objective in order to gain insights into why high probability under a model alone may not indicate adequacy.We find that beam search enforces uniform information density in text, a property motivated by cognitive science.We suggest a set of decoding objectives that explicitly enforce this property and find that exact decoding with these objectives alleviates the problems encountered when decoding poorly calibrated language generation models.Additionally, we analyze the text produced using various decoding strategies and see that, in our neural machine translation experiments, the extent to which this property is adhered to strongly correlates with BLEU.
Shapley Q-Value: A Local Reward Approach to Solve Global Reward Games

Cooperative game is a critical research area in the multi-agent reinforcement learning (MARL). Global reward game is a subclass of cooperative games, where all agents aim to maximize the global reward. Credit assignment is an important problem studied in the global reward game. Most of previous works stood by the view of non-cooperative-game theoretical framework with the shared reward approach, i.e., each agent being assigned a shared global reward directly. This, however, may give each agent an inaccurate reward on its contribution to the group, which could cause inefficient learning. To deal with this problem, we i) introduce a cooperative-game theoretical framework called extended convex game (ECG) that is a superset of global reward game, and ii) propose a local reward approach called Shapley Q-value. Shapley Q-value is able to distribute the global reward, reflecting each agent's own contribution in contrast to the shared reward approach. Moreover, we derive an MARL algorithm called Shapley Q-value deep deterministic policy gradient (SQDDPG), using Shapley Q-value as the critic for each agent. We evaluate SQDDPG on Cooperative Navigation, Prey-and-Predator and Traffic Junction, compared with the state-of-the-art algorithms, e.g., MADDPG, COMA, Independent DDPG and Independent A2C. In the experiments, SQDDPG shows a significant improvement on the convergence rate. Finally, we plot Shapley Q-value and validate the property of fair credit assignment.