Reviewerly/exHarmony huggingface dataset:
Automated ESG Report Analysis by Joint Entity and Relation Extraction

The banking industry has lately been under pressure, notably from regulators and NGOs, to report various Environmental, Societal and Governance (ESG) metrics (e.g., the carbon footprint of loans). For years at Crédit Agricole, a specialized division examined ESG and Corporate Social Responsibility (CSR) reports to ensure, e.g., the bank’s commitment to de-fund coal activities, and companies with social or environmental issues. With both an intensification of the aforementioned exterior pressure, and of the number of companies making such reports publicly available, the tedious process of going through each report has become unsustainable.In this work, we present two adaptations of previously published models for joint entity and relation extraction. We train them on a private dataset consisting in ESG and CSR reports annotated internally at Crédit Agricole. We show that we are able to effectively detect entities such as coal activities and environmental or social issues, as well as relations between these entities, thus enabling the financial industry to quickly grasp the creditworthiness of clients and prospects w.r.t. ESG criteria. The resulting model is provided at https://github.com/adimajo/renard_joint.
Review of <i>Applied Univariate, Bivariate, and Multivariate Statistics Using Python</i> by Daniel Denis

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Lightweight Deep Network With Context Information and Attention Mechanism for Vehicle Detection in Aerial Image

Vehicle detection in aerial photography scenarios has a wide range of promising applications in the military and civilian fields. Recently, object detection algorithms based on depth models have shown superior performance in aerial vehicle detection tasks. However, these detection algorithms are often accompanied by a large amount of computation and resource consumption, which leads to the inability to perform real-time detection. In addition, the insufficient feature extraction capability of the vehicle and the complex background information also lead to low detection accuracy. In this letter, we propose a lightweight backbone network with a context information module and an attention mechanism module for vehicle detection in the aerial image, which enables the feature extraction network to increase the utilization of contextual information and salient regions. In addition, we use adaptive anchor-free in the detection model to predict the bounding box. The proposed detection algorithm achieves 89.7% and 94.1% mean average precision (mAP) on the German Aerospace Center (DLR)-3K dataset and the created dataset, and the detection time for each image is 1.66 and 0.049 s, respectively.
The Blindfolded Robot: A Bayesian Approach to Planning with Contact Feedback

We address the problem of robot motion planning under uncertainty where the only observations are through contact with the environment. Such problems are typically solved by planning optimistically assuming unknown space is free, moving along the planned path and re-planning if the robot collides. However this approach can lead to many unnecessary collisions and movements. We propose a new formulation, the Blindfolded Traveler’s Problem (BTP), for planning on a graph containing edges with unknown validity, with true validity observed only through attempted traversal by the robot. We prove that BTP is NP-complete and present a number of approximation-based policies. In particular, we analyze the case of a robot arm where it is challenging to construct a reasonable prior over obstacles. We examine a number of belief approximation techniques and finally propose a policy-belief combination. For the policy we propose graph search with edges weights augmented by the probability of collision. For the belief representation we propose a weighted Mixture of Experts of Collision Hypothesis Sets and a Manifold Particle Filter. Empirical evaluation in simulation and on a real robot arm shows that our proposed approach vastly outperforms several baselines as well as a previous approach that does not employ the BTP framework.
k-Clustering with Fair Outliers

Clustering problems and clustering algorithms are often overly sensitive to the presence of outliers: even a handful of points can greatly affect the structure of the optimal solution and its cost. This is why many algorithms for robust clustering problems have been formulated in recent years. These algorithms discard some points as outliers, excluding them from the clustering. However, outlier selection can be unfair: some categories of input points may be disproportionately affected by the outlier removal algorithm.
A Broad Learning Ensemble System Using Bagging for Typhoon Trajectory Forecasting

The prediction of typhoon trajectory is a very important work, as typhoon is one of nature disasters. This paper builds a broad learning ensemble system by bagging to predict the typhoon trajectory in the South China Sea. Firstly, the prediction ability of single Broad Learning System is proved by our experiments. But considering the defect of instability, 10 Broad Learning System models are aggregated with bagging method in the framework of ensemble learning, to predict the typhoon trajectory. The experiments show that the mean distance error of Broad Learning Ensemble System is reduced by 17.86% compared with the stepwise regression model, while the stepwise regression model as classic model, is widely used in typhoon prediction practice.
Phishing Detection using Extra Trees Classifier

With a rapid growth in global networking, the online users are vulnerable to different kinds of attacks, phishing being prevalent among them. Phishing is the type of attack where the attacker aims to steal critical information by tricking the user to click on phishing links. There already exists several anti-phishing software and computational methods for actively detecting phishing activities. However, new methods of cybercrimes are evolved by the attackers that surpass the existing detection models. So, there is a constant need to research and improvise the ways to detect phishing. The proposed system develops a web-based application to detect phishing URLs using a machine learning model. Two ensemble classifiers, Random Forest (RF) and Extra Trees (ET) are compared to find the one with higher performance measures. The models are trained on the UCI dataset with 30 features. Hyperparameter Tuning is performed on the models to check whether it enhances their predictive performance. The Extra Trees classifier without tuning achieved the highest accuracy of 97.47% on the test dataset with the least false positive rate.
Formal semantics of a classical-quantum language

We investigate the formal semantics of a simple imperative language that has both classical and quantum constructs. More specifically, we provide an operational semantics, a denotational semantics and two Hoare-style proof systems: an abstract one and a concrete one. The two proof systems are satisfaction-based, as inspired by the program logics of Barthe et al. for probabilistic programs. The abstract proof system turns out to be sound and relatively complete, while the concrete one is sound only.
Object Detection, Localization and Tracking-Based Deep Learning for Smart Wheelchair

In this article, we present our work regarding the development of advanced driver-assistance systems for an electric-powered wheelchair. Our project aims at improving the autonomy of people with reduced mobility. After conducting a clinical study, we identified several use-cases. In this paper, we introduce the detection, localization and tracking of points of interest in the immediate surroundings of the chair in an indoor environment, i.e.: doors, handles, light switches, etc. The aim is not only to improve perception around the chair but also to enable semi-autonomous driving towards these targets. First, we introduce a repurposing of YOLOv3, the object detection algorithm, to our use case. Then, we show our use of the Intel Realsense camera for depth estimation. Finally, we describe our adaptation of the SORT algorithm to track 3D interest points. To validate our approach, we realized several experiments in a controlled indoor environment. The detection, distance estimation, and tracking pipeline is tested using our custom dataset. This includes corridors, doors, handles, and switches. One of the scenarios studied to validate the proposed platform includes not only the detection and tracking of objects but also the movement of the wheelchair towards one of these points of interest.
Count Me In! Extendability for Threshold Ring Signatures

Ring signatures enable a signer to sign a message on behalf of a group anonymously, without revealing her identity. Similarly, threshold ring signatures allow several signers to sign the same message on behalf of a group; while the combined signature reveals that some threshold $$t $$ of the group members signed the message, it does not leak anything else about the signers’ identities. Anonymity is a central feature in threshold ring signature applications, such as whistleblowing, e-voting and privacy-preserving cryptocurrencies: it is often crucial for signers to remain anonymous even from their fellow signers. When the generation of a signature requires interaction, this is difficult to achieve. There exist threshold ring signatures with non-interactive signing—where signers locally produce partial signatures which can then be aggregated—but a limitation of existing threshold ring signature constructions is that all of the signers must agree on the group on whose behalf they are signing, which implicitly assumes some coordination amongst them. The need to agree on a group before generating a signature also prevents others—from outside that group—from endorsing a message by adding their signature to the statement post-factum. We overcome this limitation by introducing extendability for ring signatures, same-message linkable ring signatures, and threshold ring signatures. Extendability allows an untrusted third party to take a signature, and extend it by enlarging the anonymity set to a larger set. In the extendable threshold ring signature, two signatures on the same message which have been extended to the same anonymity set can then be combined into one signature with a higher threshold. This enhances signers’ anonymity, and enables new signers to anonymously support a statement already made by others. For each of those primitives, we formalize the syntax and provide a meaningful security model which includes different flavors of anonymous extendability. In addition, we present concrete realizations of each primitive and formally prove their security relying on signatures of knowledge and the hardness of the discrete logarithm problem. We also describe a generic transformation to obtain extendable threshold ring signatures from same-message-linkable extendable ring signatures. Finally, we implement and benchmark our constructions.
Data-Augmentation Method for BERT-based Legal Textual Entailment Systems in COLIEE Statute Law Task

Abstract A legal textual entailment task is a task to recognize entailment between a law article and its statements. In the Competition on Legal Information Extraction/Entailment (COLIEE), this task is designed as a task to confirm the entailment of a yes/no answer from the given civil code article(s). Based on the development of deep-learning-based natural language processing tools such as bidirectional encoder representations from transformers (BERT), many participants in the task used such tools, and the best performance system of COLIEE 2020 was a BERT-based system. However, because of the limitation of the size of training data provided by the task organizer, training such tools to adapt to the variability of the questions is difficult. In this paper, we propose a data-augmentation method to make training data using civil code articles for understanding the syntactic structure of the questions and articles for entailment. Our BERT-based ensemble system, which uses this augmentation method, achieves the best performance (accuracy = 0.7037) in Task 4 of COLIEE 2021. We also introduce the results of additional experiments to discuss the characteristics of the proposed method.
Multi-Feature Single Target Robust Tracking Fused with Particle Filter

Aiming at the problems of target model drift or loss of target tracking caused by serious deformation, occlusion, fast motion, and out of view of the target in long-term moving target tracking in complex scenes, this paper presents a robust multi-feature single-target tracking algorithm based on a particle filter. The algorithm is based on the correlation filtering framework. First, to extract more accurate target appearance features, in addition to the manual features histogram of oriented gradient features and color histogram features, the depth features from the conv3-4, conv4-4 and conv5-4 convolutional layer outputs in VGGNet-19 are also fused. Secondly, this paper designs a re-detection module of a fusion particle filter for the problem of how to return to accurate tracking after the target tracking fails, so that the algorithm in this paper can maintain high robustness during long-term tracking. Finally, in the adaptive model update stage, the adaptive learning rate update and adaptive filter update are performed to improve the accuracy of target tracking. Extensive experiments are conducted on dataset OTB-2015, dataset OTB-2013, and dataset UAV123. The experimental results show that the proposed multi-feature single-target robust tracking algorithm with fused particle filtering can effectively solve the long-time target tracking problem in complex scenes, while showing more stable and accurate tracking performance.
More than Relata Refero: Representing the Various Roles of Reported Speech in Argumentative Discourse

Reported speech, or relata refero, although not always part of the argumentation tout court, can be an important element of argumentative discourse. It might, for instance, provide information on the position of another party in the discussion or function as part of the premise of an argument from authority. Whereas existing methods of representing argumentative discourse focus on arguments and their interrelations, this paper develops a method that enables the analyst to also include informative elements in the representation, focusing on reported speech. It does so by incorporating the notion of ‘voice’ into the representation framework of Adpositional Argumentation (AdArg). In particular, the paper explains how to formalize the constituents of this notion and illustrates its use in representing (1) an author’s report of the position of another party (including the supporting argumentation); (2) an author’s own position (including the supporting argumentation); and (3) source-based arguments such as the argument from authority, with an indication of the distance of the source from the author.
Evaluating graph neural networks under graph sampling scenarios

Background It is often the case that only a portion of the underlying network structure is observed in real-world settings. However, as most network analysis methods are built on a complete network structure, the natural questions to ask are: (a) how well these methods perform with incomplete network structure, (b) which structural observation and network analysis method to choose for a specific task, and (c) is it beneficial to complete the missing structure. Methods In this paper, we consider the incomplete network structure as one random sampling instance from a complete graph, and we choose graph neural networks (GNNs), which have achieved promising results on various graph learning tasks, as the representative of network analysis methods. To identify the robustness of GNNs under graph sampling scenarios, we systemically evaluated six state-of-the-art GNNs under four commonly used graph sampling methods. Results We show that GNNs can still be applied on single static networks under graph sampling scenarios, and simpler GNN models are able to outperform more sophisticated ones in a fairly experimental procedure. More importantly, we find that completing the sampled subgraph does improve the performance of downstream tasks in most cases; however, completion is not always effective and needs to be evaluated for a specific dataset. Our code is available at https://github.com/weiqianglg/evaluate-GNNs-under-graph-sampling .
Learning GUI Completions with User-defined Constraints

A key objective in the design of graphical user interfaces (GUIs) is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies layout recommendation methods that fulfill such consistency requirements using machine learning. Given a desired element type and size, the methods suggest element placements following real-world GUI design processes. Consistency requirements are given implicitly through previous layouts from which patterns are to be learned, comparable to existing screens of a software product. We adopt two recently proposed methods for this task, a Graph Neural Network (GNN) and a Transformer model, and compare them with a custom approach based on sequence alignment and nearest neighbor search (kNN) . The methods were tested on handcrafted datasets with explicit layout patterns, as well as large-scale public datasets of diverse mobile design layouts. Our results show that our instance-based learning algorithm outperforms both neural network approaches. Ultimately, this work contributes to establishing smarter design tools for professional designers with explainable algorithms that increase their efficacy.
Analysis of digital noise reduction methods on classifiers used in automated visual evaluation

The burden of cervical cancer disproportionately falls on low- and middle-income countries (LMICs). Automated visual evaluation (AVE) is a technology being considered as an adjunct tool for the management of HPV-positive women. AVE involves analysis of a white light illuminated cervical image using machine learning classifiers. It is of importance to analyze various impacts of different kinds of image degradations on AVE. In this paper, we report our work regarding the impact of one type of image degradation, Gaussian noise, and one of its remedies we have been exploring. The images, originated from the Natural History Study (NHS) and ASCUS-LSIL Triage Study (ALTS), were modified by the addition of white Gaussian noise at different levels. The AVE pipeline used in the experiments consists of two deep learning components: a cervix locator which uses RetinaNet (an object detection network), and a binary pathology classifier that uses the ResNeSt network. Our findings indicate that Gaussian noise, which frequently appears in low light conditions, is a key factor in degrading the AVE's performance. A blind image denoising technique which uses Variational Denoising Network (VDNet) was tested on a set of 345 digitized cervigram images (115 positives) and evaluated both visually and quantitatively. AVE performances on both the synthetically generated noisy images and the corresponding denoised images were examined and compared. In addition, the denoising technique was evaluated on several real noisy cervix images captured by a camera-based imaging device used for AVE that have no histology confirmation. The comparison between the AVE performances on images with and without denoising shows that denoising can be effective at mitigating classification performance degradation.
A Theory of PAC Learnability of Partial Concept Classes

We extend the classical theory of PAC learning in a way which allows to model a rich variety of practical learning tasks where the data satisfy special properties that ease the learning process. For example, tasks where the distance of the data from the decision boundary is bounded away from zero, or tasks where the data lie on a lower dimensional surface. The basic and simple idea is to consider partial concepts: these are functions that can be undefined on certain parts of the space. When learning a partial concept, we assume that the source distribution is supported only on points where the partial concept is defined. This way, one can naturally express assumptions on the data such as lying on a lower dimensional surface, or that it satisfies margin conditions. In contrast, it is not at all clear that such assumptions can be expressed by the traditional PAC theory using learnable total concept classes, and in fact we exhibit easy-to-learn partial concept classes which provably cannot be captured by the traditional PAC theory. This also resolves, in a strong negative sense, a question posed by Attias, Kontorovich, and Mansour (2019). We characterize PAC learnability of partial concept classes and reveal an algorithmic landscape which is fundamentally different than the classical one. For example, in the classical PAC model, learning boils down to Empirical Risk Minimization (ERM). This basic principle follows from Uniform Convergence and the Fundamental Theorem of PAC Learning (Vapnik and Chervonenkis, 1971, 1974b; Blumer, Ehrenfeucht, Haussler, and Warmuth, 1989; Hodges, 1993). In stark contrast, we show that the ERM principle fails spectacularly in explaining learnability of partial concept classes. In fact, we demonstrate classes that are incredibly easy to learn, but such that any algorithm that learns them must use an hypothesis space with unbounded VC dimension. We also find that the sample compression conjecture of Littlestone and Warmuth fails in this setting. Our impossibility results hinge on the recent breakthroughs in communication complexity and graph theory by Göös (2015); Ben-David, Hatami, and Tal (2017); Balodis, Ben-David, Göös, Jain, and Kothari (2021). Thus, this theory features problems that cannot be represented in the traditional way and cannot be solved in the traditional way. We view this as evidence that it might provide insights on the nature of learnability in realistic scenarios which the classical theory fails to explain. We include in the paper suggestions for future research and open problems in several contexts, including combinatorics, geometry, and learning theory.
A double Λ-five-level moving atom interacting with a two-mode field in the presence of damping and nonlinear Kerr medium

In this paper, we present the interaction between a five-level [Formula: see text]-configuration atom and a two-mode quantized field in the presence of the damping with the nonlinearity. According to the generalized nonlinear Jaynes–Cummings model (JCM) and the analytical solution of the Schrödinger equation, the general formula of the wave function for this system under special conditions, where the atom (the field) is initially prepared in the excited state (coherent state), has been found. Under some particular conditions, the five-level [Formula: see text]-type atom can be reduced to the three-level [Formula: see text]-type atom. Some physical aspects of the atom-field entangled state showing the entanglement degree, such as the field entropy. Moreover, we evaluate some of their non-classical statistical aspects such as atomic inversion. The effects of the physical parameters such as a Kerr medium, detuning parameter and the intensity-dependent coupling on the temporal behavior of the latter mentioned non-classical statistical aspects have been explored. We show that by choosing the evolved parameters in the interaction process, each of the above non-classical statistical features can be treated. We found that the parameters have an important influence on the properties of these phenomena.