Reviewerly/exHarmony huggingface dataset:
Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation

Abstract The proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.
Repudiable ring signature: Stronger security and logarithmic-size

Ring signature, introduced by Rivest et al. [Asiacrypt’01], allows a person to sign a document on behalf of an ad-hoc group (or ring) while hiding the identity of the actual signer. But the anonymity provided by the ring signature scheme can be used to conceal a malicious signer and put other ring members under suspicion. Fortunately, Park et al. [Crypto’19] proposed a repudiable ring signature scheme which can overcome this disadvantage. However, the construction of Park et al. [Crypto’19] is not compact, in other word, the size of signatures and repudiations in their scheme increases with the square of the ring size. In this paper, we propose the first logarithmic-size repudiable ring signature scheme, which means the size of signatures and repudiations grows only logarithmically in the ring size. Moreover, in terms of security model, we present a new requirement (repudiation-unforgeability), which requires ‘no one can forge a valid repudiation’. Our scheme is the first repudiable ring signature scheme satisfies this new requirement.
On interpolation-based decoding of a class of maximum rank distance codes

In this paper we present an interpolation-based decoding algorithm to decode a family of maximum rank distance codes proposed recently by Trombetti and Zhou. We employ the properties of the Dickson matrix associated with a linearized polynomial with a given rank and the modified Berlekamp-Massey algorithm in decoding. When the rank of the error vector attains the unique decoding radius, the problem is converted to solving a quadratic polynomial, which ensures that the proposed decoding algorithm has polynomial-time complexity.
BatFL: Backdoor Detection on Federated Learning in e-Health

Federated Learning (FL) has received significant interest both from the research field and industry perspective. One of the most promising cross-silo applications on FL is electronic health records mining which trains a model on siloed data. In this application, clients can be different hospitals or health centers that are located in geo-distributed data centers. A central orchestration server (superior health center) organizes the training, while never seeing patients’ raw data. In this paper, we demonstrate that any local hospital in such a collaborative training framework can introduce hidden backdoor functionality into the joint global model. The backdoored joint global model will produce an adversary-expected output when a predefined trigger is attached to its input but it will behave normally for clean inputs. This vulnerability is exacerbated by the distributed nature of FL, making detecting backdoor attacks on FL a challenging work. Based on the coalitional game and Shapley value, we propose an effective and real-time backdoor detection system on FL. Extensive experiments over two machine learning tasks show that our techniques achieve high accuracy and are robust against multi-attackers settings.
SGUNet: Style-guided UNet for adversely conditioned fundus image super-resolution

Image super-resolution from low-resolution fundus image has valuable applications in clinical practices. The popular methods yield unsatisfactory results when the fundus images are contaminated due to the bleeding or plaques caused by eye diseases. To this end, we propose a style-guided UNet (SGUNet) which incorporates a series of style-guided U-shape block (SUB) for fundus image super-resolution. Each SUB consists of trunk and mask branches. The proposed trunk branch is a U-shape structure that intends to enlarge the receptive field by down-sampling via large-stride convolution, and fuses the complementary information under the different receptive fields. The mask branch then dynamically estimates the relative importance of individual potential styles to reweigh the feature maps according to the significance of the potential styles. To fully leverage the hierarchical features, a dense feature fusion scheme is introduced by concatenating the output of preceding SUBs. We extensively validate the proposed network on low-resolution retina dataset with adversely affected by diseases. The experimental results demonstrate that our SGUNet achieves superior performance with excellent robustness and high accuracy by comparing with six popular methods.
Decentralized payment clearing using blockchain and optimal bidding

In this paper, we construct a decentralized clearing mechanism which endogenously and automatically provides a claims resolution procedure. This mechanism can be used to clear a network of obligations through blockchain. In particular, we investigate default contagion in a network of smart contracts cleared through blockchain. In so doing, we provide an algorithm which constructs the blockchain so as to guarantee the payments can be verified and the miners earn a fee. We, additionally, consider the special case in which the blocks have unbounded capacity to provide a simple equilibrium clearing condition for the terminal net worths; existence and uniqueness are proven for this system. Finally, we consider the optimal bidding strategies for each firm in the network so that all firms are utility maximizers with respect to their terminal wealths. We show that a pure strategy Nash equilibrium need not exist, and then look for mixed Nash equilibrium bidding strategies. The implications of these strategies, and more broadly blockchain, on systemic risk are considered.
Automotive Radar-Based Vehicle Tracking Using Data-Region Association

For automotive radar-based extended object tracking, this paper proposes a new approach, which jointly estimates the kinematic state and the extension of a vehicle. The vehicle’s shape is described as a rectangle with its vertices treated as the extension state. “Having a rectangular shape” is described as a quadratic equality constraint on the state. To deal with the challenging problem of modeling measurement (scattering center) distribution over a vehicle, we partition the rectangular area into multiple regions and assume that in each region the scattering centers have a simple distribution. An approach is proposed to associate measurements with these regions. Given an association, the target state is estimated in a linear minimum mean-square-error framework with the shape constraint treated as a pseudo measurement. The probabilities for these regions to generate measurements are updated online. The estimate is then projected into the constraint space. The effectiveness of the proposed approach is illustrated using both simulated and real data.
New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.
Fast End-to-End Deep Learning Identity Document Detection, Classification and Cropping

The growing use of Know Your Customer online services generates a massive flow of dematerialised personal Identity Documents under variable capturing conditions and qualities (e.g. webcam, smartphone, scan, or even handcrafted pdfs). IDs are designed, depending on their issuing country/model, with a specific layout (i.e. background, photo(s), fixed/variable text fields) along with various anti-fraud features (e.g. checksums, Optical Variable Devices) which are non-trivial to analyse. This paper tackles the problem of detecting, classifying, and aligning captured documents onto their reference model. This task is essential in the process of document reading and fraud verification. However, due to the high variation of capture conditions and models' layout, classical handcrafted approaches require deep knowledge of documents and hence are hard to maintain. A modular approach using a fully multi-stage deep learning based approach is proposed in this work. The proposed approach allows to accurately classify the document and estimates its quadrilateral (localization). As opposed to approaches relying on a single end-to-end network, the proposed modular framework offers more flexibility and a potential for future incremental learning. All networks used in this work are derivatives of recent state-of-the-art ones. Experiments show the superiority of the proposed approach in terms of speed while maintaining good accuracy, both on the MIDV-500 academic dataset and on an industrial based dataset compared to hand crafted solutions.
Context-sensitive zero-shot semantic segmentation model based on meta-learning

The zero-shot semantic segmentation requires models with a strong image understanding ability. The majority of current solutions are based on direct mapping or generation. These schemes are effective in dealing with the zero-shot recognition, but they cannot fully transfer the visual dependence between objects in more complex scenarios of semantic segmentation. More importantly, the predicted results become seriously biased to the seen-category in the training set, which makes it difficult to accurately recognize the unseen-category. In view of the above two problems, we propose a novel zero-shot semantic segmentation model based on meta-learning. It is observed that the pure semantic space expression has certain limitations for the zero-shot learning. Therefore, based on the original semantic migration, we first migrate the shared information in the visual space by adding a context-module, and then migrate it in the visual and semantic dual space. At the same time, in order to solve the problem of biasness, we improve the adaptability of the model parameters by adjusting the parameters of the dual-space through the meta-learning, so that it can successfully complete the segmentation even in the face of new categories without reference samples. Experiments show that our algorithm outperforms the existing best methods in the zero-shot segmentation on three datasets of Pascal-VOC 2012, Pascal-Context and Coco-stuff.
An algorithm to compute the strength of competing interactions in the Bering Sea based on pythagorean fuzzy hypergraphs

Abstract The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k -competition hypergraphs, m -step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario.
Demonstration of dealer

Data-driven machine learning (ML) has witnessed great success across a variety of application domains. Since ML model training relies on a large amount of data, there is a growing demand for high-quality data to be collected for ML model training. Data markets can be employed to significantly facilitate data collection. In this work, we demonstrate Dealer, an en &lt;u&gt;D&lt;/u&gt; -to-end mod&lt;u&gt;e&lt;/u&gt;l m &lt;u&gt;a&lt;/u&gt; rketp &lt;u&gt;l&lt;/u&gt; ace with diff &lt;u&gt;e&lt;/u&gt; rential p &lt;u&gt;r&lt;/u&gt; ivacy. Dealer consists of three entities, data owners, the broker, and model buyers. Data owners receive compensation for their data usages allocated by the broker; The broker collects data from data owners, builds and sells models to model buyers; Model buyers buy their target models from the broker. We demonstrate the functionalities of the three participating entities and the abbreviated interactions between them. The demonstration allows the audience to understand and experience interactively the process of model trading. The audience can act as a data owner to control what and how the data would be compensated, can act as a broker to price machine learning models with maximum revenue, as well as can act as a model buyer to purchase target models that meet expectations.
Target-Dependent UNITER: A Transformer-Based Multimodal Language Comprehension Model for Domestic Service Robots

Currently, domestic service robots have an insufficient ability to interact naturally through language. This is because understanding human instructions is complicated by various ambiguities. In existing methods, the referring expressions that specify the relationships between objects were insufficiently modeled. In this letter, we propose Target-dependent UNITER, which learns the relationship between the target object and other objects directly by focusing on the relevant regions within an image, rather than the whole image. Our method is an extension of the UNITER [1]-based Transformer that can be pretrained on general-purpose datasets. We extend the UNITER approach by introducing a new architecture for handling candidate objects. Our model is validated on two standard datasets, and the results show that Target-dependent UNITER outperforms the baseline method in terms of classification accuracy.
Face Recognition System using Histograms of Oriented Gradients and Convolutional Neural Network based on with Particle Swarm Optimization

In this paper, Histograms of Oriented Gradients dependent on the strong point of convolutional neural organization which is new methodology for evenness face data set, is introduced. A proposed face acknowledgment framework was created to be utilized for various purposes. We utilized Gabor wavelet change for include extraction of evenness face preparing information and afterward we utilized profound learning technique for acknowledgment. We executed and assessed the proposed strategy on ORL and YALE data sets with Matlab 2020b. Besides, similar trials were directed applying Particle Swarm Optimization (PSO) for include determination approach. The execution of Gabor wavelet include extraction with a high number of preparing picture tests has end up being more viable than different strategies in our examination. The acknowledgment rate while carrying out the PSO strategies on ORL data set is 86.62% while it is 92.6% with the three techniques on YALE data set. In any case, the utilization of PSO calculation has expanded the exactness rate to 95.88% for ORL information base and 95.23% on YALE data set.
BotSpot: Deep Learning Classification of Bot Accounts Within Twitter

The openness feature of Twitter allows programs to generate and control Twitter accounts automatically via the Twitter API. These accounts, which are known as bots, can automatically perform actions such as tweeting, re-tweeting, following, unfollowing, or direct messaging other accounts, just like real people. They can also conduct malicious tasks such as spreading of fake news, spams, malicious software and other cyber-crimes. In this paper, we introduce a novel bot detection approach using deep learning, with the Multi-layer Perceptron Neural Networks and nine features of a bot account. A web crawler is developed to automatically collect data from public Twitter accounts and build the testing and training datasets, with 860 samples of human and bot accounts. After the initial training is done, the Multilayer Perceptron Neural Networks achieved an overall accuracy rate of 92%, which proves the performance of the proposed approach.
Attention based Abstractive Summarization of Malayalam Document

There are different textual content summarization processes available in natural Language Processing. Amongst them abstractive textual content summarization is one of the challenging problems in natural language processing and that too, with very little research done in regional languages. Unlike other summarization techniques, which reuses the words and phrases from the source text, abstractive text summarization builds a short and concise precis of a huge text document built from the underlying message of the text not necessarily using the same words and phrases from the source. The objective of the proposed work is to create a brief and understandable abstractive summary of a Malayalam document. Malayalam is one of the 22 scheduled languages of India spoken by over 34 million people and is designated as a Classical Language in India. Being a Classical language, Malayalam has a very unique syntactic and semantic rules which makes this work more important. The proposed work attempts to create an attention mechanism to generate the summary of the source document. In this work, the goal was to compare the efficiency of Attention model with sequence to sequence baseline model of Malayalam text and thereby implementing a better abstractive text summarizer for a malayalam document.
How light emitted at high temperature affects common digital image correlation algorithms

This manuscript serves as an addendum to our previous paper, 'Change of Exposure Time Mid-Test in High Temperature DIC Measurement,' published in 2020 (Thai et al 2020 Meas. Sci. Technol. 31 075402). Since publishing that paper, we have since discovered that our two sources of lighting affect the brightness and contrast of high-temperature images differently. While it is broadly understood that changes in camera sensitivity (for example, by reducing exposure time) produce linear scaling in lighting, here we show that changes in temperature (which produce blackbody radiation) instead produce uniform offsets in lighting. This has important implications for the correlation algorithm, as the normalized sum of squared differences (NSSD) function compensates only for linear scaling, not uniform offsets. Here, we repeat the analysis for three different temperatures (1300 °C, 1450 °C, and 1600 °C) and correlate using three different algorithms: the sum of squared differences, NSSD, and zero-normalized sum of squared differences (ZNSSD). Our findings reinforce the consensus that, when performing digital image correlation (DIC) at extreme temperatures, it is important to always select the ZNSSD algorithm due to its ability to correct for both offsets and linear scaling in lighting.
Joint Graph Learning and Matching for Semantic Feature Correspondence

In recent years, powered by the learned discriminative representation via graph neural network (GNN) models, deep graph matching methods have made great progresses in the task of matching semantic features. However, these methods usually rely on heuristically generated graph patterns, which may introduce unreliable relationships to hurt the matching performance. In this paper, we propose a joint graph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. GLAM adopts a pure attention-based framework for both graph learning and graph matching. Specifically, it employs two types of attention mechanisms, self-attention and cross-attention for the task. The self-attention discovers the relationships between features to further update feature representations over the learnt structures; and the cross-attention computes cross-graph correlations between the two feature sets to be matched for feature reconstruction. Moreover, the final matching solution is directly derived from the output of the cross-attention layer, without employing a specific matching decision module. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k), and it outperforms previous state-of-the-art graph matching methods on all benchmarks. Furthermore, the graph patterns learnt by our model are validated to be able to remarkably enhance previous deep graph matching methods by replacing their handcrafted graph structures with the learnt ones.