Reviewerly/exHarmony huggingface dataset:
Demystifying Social Bots: On the Intelligence of Automated Social Media Actors

Recently, social bots, (semi-) automatized accounts in social media, gained global attention in the context of public opinion manipulation. Dystopian scenarios like the malicious amplification of topics, the spreading of disinformation, and the manipulation of elections through “opinion machines” created headlines around the globe. As a consequence, much research effort has been put into the classification and detection of social bots. Yet, it is still unclear how easy an average online media user can purchase social bots, which platforms they target, where they originate from, and how sophisticated these bots are. This work provides a much needed new perspective on these questions. By providing insights into the markets of social bots in the clearnet and darknet as well as an exhaustive analysis of freely available software tools for automation during the last decade, we shed light on the availability and capabilities of automated profiles in social media platforms. Our results confirm the increasing importance of social bot technology but also uncover an as yet unknown discrepancy of theoretical and practically achieved artificial intelligence in social bots: while literature reports on a high degree of intelligence for chat bots and assumes the same for social bots, the observed degree of intelligence in social bot implementations is limited. In fact, the overwhelming majority of available services and software are of supportive nature and merely provide modules of automation instead of fully fledged “intelligent” social bots.
3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud

3D single object tracking is a key issue for autonomous following robot, where the robot should robustly track and accurately localize the target for efficient following. In this paper, we propose a 3D tracking method called 3D-SiamRPN Network to track a single target object by using raw 3D point cloud data. The proposed network consists of two subnetworks. The first subnetwork is feature embedding subnetwork which is used for point cloud feature extraction and fusion. In this subnetwork, we first use PointNet++ to extract features of point cloud from template and search branches. Then, to fuse the information of features in the two branches and obtain their similarity, we propose two cross correlation modules, named Pointcloud-wise and Point-wise respectively. The second subnetwork is region proposal network(RPN), which is used to get the final 3D bounding box of the target object based on the fusion feature from cross correlation modules. In this subnetwork, we utilize the regression and classification branches of a region proposal subnetwork to obtain proposals and scores, thus get the final 3D bounding box of the target object. Experimental results on KITTI dataset show that our method has a competitive performance in both Success and Precision compared to the state-of-the-art methods, and could run in real-time at 20.8 FPS. Additionally, experimental results on H3D dataset demonstrate that our method also has good generalization ability and could achieve good tracking performance in a new scene without re-training.
Modified Particle Swarm Optimization With Effective Guides

Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm.In this article, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics.Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information.Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions.In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches.Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters.Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.
Reinforcement learning decoders for fault-tolerant quantum computation

Abstract Topological error correcting codes, and particularly the surface code, currently provide the most feasible road-map towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally realistic and challenging context of faulty syndrome measurements, without requiring any final read-out of the physical qubits, is of critical importance. In this work, we show that the problem of decoding such codes can be naturally reformulated as a process of repeated interactions between a decoding agent and a code environment, to which the machinery of reinforcement learning can be applied to obtain decoding agents. While in principle this framework can be instantiated with environments modelling circuit level noise, we take a first step towards this goal by using deepQ learning to obtain decoding agents for a variety of simplified phenomenological noise models, which yield faulty syndrome measurements without including the propagation of errors which arise in full circuit level noise models.
Next Generation NDE Sensor Systems as IIoT Elements of Industry 4.0

Industry 4.0 (I4.0) describes the current revolution of the industrial world with a strong impact on the complete production sector. Data about production processes and the corresponding material and product status are the key elements. All over the world, the protagonists of I4.0 are facing the challenges to define appropriate concepts for I4.0 infrastructure, data exchange, communication interfaces and efficient procedures for the interaction of I4.0 elements. The role of future Nondestructive Evaluation (NDE4.0) and corresponding workflows (i.e. data generation and evaluation) will change accordingly. Thus, NDE4.0 systems will be elements of the Industrial Internet of Things (IIoT) that communicate with the production machines and devices. They become an integral part of the digital production world and the industrial data space. This paper is a summarized overview of our current developments as well as of general key technologies and future challenges to enable the paradigm change from classical NDT toward NDE4.0, starting with approaches on signal processing, artificial intelligence-based information generation and decision making, generic data formats and communication protocols. For illustration purposes, prototypical implementations of our work are presented. This includes a pilot development of a modern human- machine-interaction by the use of assistance technologies for manual inspection.
iCmSC: Incomplete Cross-Modal Subspace Clustering

Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering (i.e., iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,2</sub> -norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.
Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support

Computer Vision, and hence Artificial Intelligence-based extraction of information from images, has increasingly received attention over the last years, for instance in medical diagnostics. While the algorithms’ complexity is a reason for their increased performance, it also leads to the ‘black box’ problem, consequently decreasing trust towards AI. In this regard, “Explainable Artificial Intelligence” (XAI) allows to open that black box and to improve the degree of AI transparency. In this paper, we first discuss the theoretical impact of explainability on trust towards AI, followed by showcasing how the usage of XAI in a health-related setting can look like. More specifically, we show how XAI can be applied to understand why Computer Vision, based on deep learning, did or did not detect a disease (malaria) on image data (thin blood smear slide images). Furthermore, we investigate, how XAI can be used to compare the detection strategy of two different deep learning models often used for Computer Vision: Convolutional Neural Network and Multi-Layer Perceptron. Our empirical results show that i) the AI sometimes used questionable or irrelevant data features of an image to detect malaria (even if correctly predicted), and ii) that there may be significant discrepancies in how different deep learning models explain the same prediction. Our theoretical discussion highlights that XAI can support trust in Computer Vision systems, and AI systems in general, especially through an increased understandability and predictability.
FQuAD: French Question Answering Dataset

Recent advances in the field of language modeling have improved state-of-the-art results on many Natural Language Processing tasks.Among them, Reading Comprehension has made significant progress over the past few years.However, most results are reported in English since labeled resources available in other languages, such as French, remain scarce.In the present work, we introduce the French Question Answering Dataset (FQuAD).FQuAD is a French Native Reading Comprehension dataset of questions and answers on a set of Wikipedia articles that consists of 25,000+ samples for the 1.0 version and 60,000+ samples for the 1.1 version.We train a baseline model which achieves an F1 score of 92.2 and an exact match ratio of 82.1 on the test set.In an effort to track the progress of French Question Answering models we propose a leaderboard and we have made the 1.
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers

Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models.While on the one hand, joint pretraining (i.e., training from scratch, adding objectives based on external knowledge to the primary LM objective) may be prohibitively computationally expensive, post-hoc fine-tuning on external knowledge, on the other hand, may lead to the catastrophic forgetting of distributional knowledge.In this work, we investigate models for complementing the distributional knowledge of BERT with conceptual knowledge from ConceptNet and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using adapter training.While overall results on the GLUE benchmark paint an inconclusive picture, a deeper analysis reveals that our adapter-based models substantially outperform BERT (up to 15-20 performance points) on inference tasks that require the type of conceptual knowledge explicitly present in ConceptNet and OMCS.We also open source all our experiments and relevant code under: https://github.com/wluper/retrograph.
Question Directed Graph Attention Network for Numerical Reasoning over Text

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph.Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP 1 .
Asymmetric twin-field quantum key distribution

Abstract Twin-Field (TF) quantum key distribution (QKD) is a major candidate to be the new benchmark for far-distance QKD implementations, since its secret key rate can overcome the repeaterless bound by means of a simple interferometric measurement. Many variants of the original protocol have been recently proven to be secure. Here, we focus on the TF-QKD type protocol proposed by Curty et al (2019 NPJ Quantum Inf. 5 64), which can provide a high secret key rate and whose practical feasibility has been demonstrated in various recent experiments. The security of this protocol relies on the estimation of certain detection probabilities (yields) through the decoy-state technique. Analytical bounds on the relevant yields have been recently derived assuming that both parties use the same set of decoy intensities, thus providing sub-optimal key rates in asymmetric-loss scenarios. Here we derive new analytical bounds when the parties use either two, three or four independent decoy intensity settings each. With the new bounds we optimize the protocol’s performance in asymmetric-loss scenarios and show that the protocol is robust against uncorrelated intensity fluctuations affecting the parties’ lasers.
A Unified Taxonomy of Harmful Content

The ability to recognize harmful content within online communities has come into focus for researchers, engineers and policy makers seeking to protect users from abuse.While the number of datasets aiming to capture forms of abuse has grown in recent years, the community has not standardized around how various harmful behaviors are defined, creating challenges for reliable moderation, modeling and evaluation.As a step towards attaining shared understanding of how online abuse may be modeled, we synthesize the most common types of abuse described by industry, policy, community and health experts into a unified typology of harmful content, with detailed criteria and exceptions for each type of abuse.
Hierarchical Visual-Textual Graph for Temporal Activity Localization via Language

Temporal Activity Localization via Language (TALL) in video is a recently proposed challenging vision task, and tackling it requires fine-grained understanding of the video content, however, this is overlooked by most of the existing works. In this paper, we propose a novel TALL method which builds a Hierarchical Visual-Textual Graph to model interactions between the objects and words as well as among the objects to jointly understand the video contents and the language. We also design a convolutional network with cross-channel communication mechanism to further encourage the information passing between the visual and textual modalities. Finally, we propose a loss function that enforces alignment of the visual representation of the localized activity and the sentence representation, so that the model can predict more accurate temporal boundaries. We evaluated our proposed method on two popular benchmark datasets: Charades-STA and ActivityNet Captions, and achieved state-of-the-art performances on both datasets. Code is available at https://github.com/forwchen/HVTG .
GAN Ensemble for Anomaly Detection

When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works modify Generative Adversarial Networks (GANs) by using encoder-decoders as generators and apply them to anomaly detection tasks. Previous studies indicate that GAN ensembles are often more stable than single GANs in image generation tasks. In this work, we propose to construct GAN ensembles for anomaly detection. In the proposed method, a group of generators interact with a group of discriminators, so every generator gets feedback from every discriminator, and vice versa. Compared to a single GAN, an ensemble of GANs can better model the distribution of normal data and thus better detect anomalies. We also make a theoretical analysis of GANs and GAN ensembles in the context of anomaly detection. The empirical study constructs ensembles based on four different types of detecting models, and the results show that the ensemble outperforms the single model for all four model types.
Incrementally Aggregatable Vector Commitments and Applications to Verifiable Decentralized Storage

Vector commitments with subvector openings (SVC) [Lai-Malavolta, Boneh-Bunz-Fisch; CRYPTO'19] allow one to open a committed vector at a set of positions with an opening of size independent of both the vector's length and the number of opened positions. We continue the study of SVC with two goals in mind: improving their efficiency and making them more suitable to decentralized settings. We address both problems by proposing a new notion for VC that we call incremental aggregation and that allows one to merge openings in a succinct way an unbounded number of times. We show two applications of this property. The first one is immediate and is a method to generate openings in a distributed way. The second application is an algorithm for faster generation of openings via preprocessing. We then proceed to realize SVC with incremental aggregation. We provide two constructions in groups of unknown order that, similarly to that of Boneh et al. (which supports aggregating only once), have constant-size public parameters, commitments and openings. As an additional feature, for the first construction we propose efficient arguments of knowledge of subvector openings which immediately yields a keyless proof of storage with compact proofs. Finally, we address a problem closely related to that of SVC: storing a file efficiently in completely decentralized networks. We introduce and construct verifiable decentralized storage (VDS), a cryptographic primitive that allows to check the integrity of a file stored by a network of nodes in a distributed and decentralized way. Our VDS constructions rely on our new vector commitment techniques.
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network

Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text.To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification.However, currently only a few simple studies have focused on this problem.The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments.In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a review sentence, and jointly predict the sentiment for each of the identified categories.Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments.Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model consistently achieves the best results on four benchmarks.
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base

Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain.In this paper, a novel joint training method is introduced for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus.We show that in three different downstream clinical NLP tasks, our pre-trained language model outperforms the corresponding model with no knowledge base information and other state-of-the-art models.Specifically, in a natural language inference task applied to clinical texts, our knowledge base pre-training approach improves accuracy by up to 1.7%, whereas in clinical name entity recognition tasks, the F1-score improves by up to 1.0%.The pre-trained models are available at
Adaptive Universal Generalized PageRank Graph Neural Network

In many important graph data processing applications the acquired information includes both node features and observations of the graph topology. Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility and integrate them in a manner that is also universal. Here, universality refers to independence on homophily or heterophily graph assumptions. We address these issues by introducing a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights so as to jointly optimize node feature and topological information extraction, regardless of the extent to which the node labels are homophilic or heterophilic. Learned GPR weights automatically adjust to the node label pattern, irrelevant on the type of initialization, and thereby guarantee excellent learning performance for label patterns that are usually hard to handle. Furthermore, they allow one to avoid feature over-smoothing, a process which renders feature information nondiscriminative, without requiring the network to be shallow. Our accompanying theoretical analysis of the GPR-GNN method is facilitated by novel synthetic benchmark datasets generated by the so-called contextual stochastic block model. We also compare the performance of our GNN architecture with that of several state-of-the-art GNNs on the problem of node-classification, using well-known benchmark homophilic and heterophilic datasets. The results demonstrate that GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.