Reviewerly/exHarmony huggingface dataset:
Towards self-regulating AI

AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.
Road images augmentation with synthetic traffic signs using neural networks

Traffic sign recognition is a well-researched problem in computer vision. However, the state of the art methods works only for frequent sign classes, which are well represented in training datasets. We consider the task of rare traffic sign detection and classification. We aim to solve that problem by using synthetic training data. Such training data is obtained by embedding synthetic images of signs in the real photos. We propose three methods for making synthetic signs consistent with a scene in appearance. These methods are based on modern generative adversarial network (GAN) architectures. Our proposed methods allow realistic embedding of rare traffic sign classes that are absent in the training set. We adapt a variational autoencoder for sampling plausible locations of new traffic signs in images. We demonstrate that using a mixture of our synthetic data with real data improves the accuracy of both classifier and detector.
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning

Privacy is important considering the financial Domain as such data is highly confidential and sensitive.Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains such as customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data.In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features such as Differential Privacy (DP) and Federated Learning (FL).We present how to privately train NLP models and desirable privacyutility tradeoffs and evaluate them on the Financial Phrase Bank dataset.
Declarative Machine Learning Systems

The people training and using ML models now are typically experienced developers with years of study working within large organizations, but the next wave of ML systems should allow a substantially larger number of people, potentially without any coding skills, to perform the same tasks. These new ML systems will not require users to fully understand all the details of how models are trained and used for obtaining predictions, but will provide them a more abstract interface that is less demanding and more familiar. Declarative interfaces are well-suited for this goal, by hiding complexity and favoring separation of interest, and ultimately leading to increased productivity.
Probing Language Models for Understanding of Temporal Expressions

We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions.More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units.We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.
Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition

Text recognition is a popular topic for its broad applications. In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost. The implicit task plays as an auxiliary branch for complementing the sequential recognition. We design a two-branch reciprocal feature learning framework in order to adequately utilize the features from both the tasks. Through exploiting the complementary effect between explicit and implicit tasks, the feature is reliably enhanced. Extensive experiments on 7 benchmarks show the advantages of the proposed methods in both text recognition and the new-built character counting tasks. In addition, it is convenient yet effective to equip with variable networks and tasks. We offer abundant ablation studies, generalizing experiments with deeper understanding on the tasks. Code is available.
ACE-ADP: Adversarial Contextual Embeddings Based Named Entity Recognition for Agricultural Diseases and Pests

Entity recognition tasks, which aim to utilize the deep learning-based models to identify the agricultural diseases and pests-related nouns such as the names of diseases, pests, and drugs from the texts collected on the internet or input by users, are a fundamental component for agricultural knowledge graph construction and question-answering, which will be implemented as a web application and provide the general public with solutions for agricultural diseases and pest control. Nonetheless, there are still challenges: (1) the polysemous problem needs to be further solved, (2) the quality of the text representation needs to be further enhanced, (3) the performance for rare entities needs to be further improved. We proposed an adversarial contextual embeddings-based model named ACE-ADP for named entity recognition in Chinese agricultural diseases and pests domain (CNER-ADP). First, we enhanced the text representation and overcame the polysemy problem by using the fine-tuned BERT model to generate the contextual character-level embedded representation with the specific knowledge. Second, adversarial training was also introduced to enhance the generalization and robustness in terms of identifying the rare entities. The experimental results showed that our model achieved an F1 of 98.31% with 4.23% relative improvement compared to the baseline model (i.e., word2vec-based BiLSTM-CRF) on the self-annotated corpus named Chinese named entity recognition dataset for agricultural diseases and pests (AgCNER). Besides, the ablation study and discussion demonstrated that ACE-ADP could not only effectively extract rare entities but also maintain a powerful ability to predict new entities in new datasets with high accuracy. It could be used as a basis for further research on other domain-specific named entity recognition.
Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction

While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation. Initially designed for natural language processing tasks, Transformers have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies. In this paper, we propose TransDepth, an architecture that benefits from both convolutional neural networks and transformers. To avoid the network losing its ability to capture local-level details due to the adoption of transformers, we propose a novel decoder that employs attention mechanisms based on gates. Notably, this is the first paper that applies transformers to pixel-wise prediction problems involving continuous labels (i.e., monocular depth prediction and surface normal estimation). Extensive experiments demonstrate that the proposed TransDepth achieves state-of-the-art performance on three challenging datasets. Our code is available at: https://github.com/ygjwd12345/TransDepth.
Evaluation of JATSdecoder as an automated text extraction tool for statistical results in scientific reports

The extraction of statistical results in scientific reports is beneficial for checking studies on plausibility and reliability. The R package JATSdecoder supports the application of text mining approaches to scientific reports. Its function get.stats() extracts all reported statistical results from text and recomputes p values for most standard test results. The output can be reduced to results with checkable or computable p values only. In this article, get.stats()'s ability to extract, recompute and check statistical results is compared to that of statcheck, which is an already established tool. A manually coded data set, containing the number of statistically significant results in 49 articles, serves as an initial indicator for get.stats()'s and statcheck's differing detection rates for statistical results. Further 13,531 PDF files by 10 mayor psychological journals, 18,744 XML documents by Frontiers of Psychology and 23,730 articles related to psychological research and published by PLoS One are scanned for statistical results with both algorithms. get.stats() almost replicates the manually extracted number of significant results in 49 PDF articles. get.stats() outperforms the statcheck functions in identifying statistical results in every included journal and input format. Furthermore, the raw results extracted by get.stats() increase statcheck's detection rate. JATSdecoder's function get.stats() is a highly general and reliable tool to extract statistical results from text. It copes with a wide range of textual representations of statistical standard results and recomputes p values for two- and one-sided tests. It facilitates manual and automated checks on consistency and completeness of the reported results within a manuscript.
Digital homotopy relations and digital homology theories

In this paper we prove results relating to two homotopy relations and four homology theories developed in the topology of digital images.&lt;br /&gt;&lt;br /&gt;We introduce a new type of homotopy relation for digitally continuous functions which we call ``strong homotopy.'' Both digital homotopy and strong homotopy are natural digitizations of classical topological homotopy: the difference between them is analogous to the difference between digital 4-adjacency and 8-adjacency in the plane.&lt;br /&gt;&lt;br /&gt;We also consider four different digital homology theories: a simplicial homology theory by Arslan et al which is the homology of the clique complex, a singular simplicial homology theory by D. W. Lee, a cubical homology theory by Jamil and Ali, and a new kind of cubical homology for digital images with $c_1$-adjacency which is easily computed, and generalizes a construction by Karaca \&amp;amp; Ege. We show that the two simplicial homology theories are isomorphic to each other, but distinct from the two cubical theories.&lt;br /&gt;&lt;br /&gt;We also show that homotopic maps have the same induced homomorphisms in the cubical homology theory, and strong homotopic maps additionally have the same induced homomorphisms in the simplicial theory.
Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluation

Machine learning methods are increasingly applied for medical data analysis to reduce human efforts and improve our understanding of disease propagation.When the data is complicated and unstructured, shallow learning methods may not be suitable or feasible.Deep learning neural networks like multilayer perceptron (MLP) and convolutional neural network (CNN), have been incorporated in medical diagnosis and prognosis for better health care practice.For a binary outcome, these learning methods directly output predicted probabilities for patient's health condition.Investigators still need to consider appropriate decision threshold to split the predicted probabilities into positive and negative regions.We review methods to select the cut-off values, including the relatively automatic methods based on optimization of the ROC curve criteria and also the utility-based methods with a net benefit curve.In particular, decision curve analysis (DCA) is now acknowledged in medical studies as a good complement to the ROC analysis for the purpose of decision making.In this paper, we provide the R code to illustrate how to perform the statistical learning methods, select decision threshold to yield the binary prediction and evaluate the accuracy of the resulting classification.This article will help medical decision makers to understand different classification methods and use them in real world scenario.
Variational Bayesian Approach to Condition-Invariant Feature Extraction for Visual Place Recognition

As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition-invariant features for visual place recognition in a changing environment. Under the assumption that a latent representation of the variational autoencoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variation autoencoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity matrix, and the places can be recognized even in severe environmental changes. Experiments were conducted to verify the proposed method, and the experimental results showed that our assumption was reasonable and effective in recognizing places in changing environments.
Use and Misuse of the Term “Experiment” in Mining Software Repositories Research

The significant momentum and importance of Mining Software Repositories (MSR) in Software Engineering (SE) has fostered new opportunities and challenges for extensive empirical research. However, MSR researchers seem to struggle to characterize the empirical methods they use into the existing empirical SE body of knowledge. This is especially the case of MSR experiments. To provide evidence on the special characteristics of MSR experiments and their differences with experiments traditionally acknowledged in SE so far, we elicited the hallmarks that differentiate an experiment from other types of empirical studies and characterized the hallmarks and types of experiments in MSR. We analyzed MSR literature obtained from a small-scale systematic mapping study to assess the use of the term experiment in MSR. We found that 19% of the papers claiming to be an experiment are indeed not an experiment at all but also observational studies, so they use the term in a misleading way. From the remaining 81% of the papers, only one of them refers to a genuine controlled experiment while the others stand for experiments with limited control. MSR researchers tend to overlook such limitations, compromising the interpretation of the results of their studies. We provide recommendations and insights to support the improvement of MSR experiments.
Entanglement instability in the interaction of two qubits with a common non-Markovian environment

In this work, we study the steady-state entanglement between two qubits interacting asymmetrically with a common non-Markovian environment. Depending on the initial two-qubit state, the asymmetry in the couplings between each qubit and the non-Markovian environment may lead to enhanced entanglement in the steady state of the system, measured in terms of the two-qubit concurrence. Our results indicate that, if a qubit–qubit interaction is also present, the two-qubit steady-state concurrence is always favored by the symmetric or anti-symmetric coupling configuration. Although finite, the steady concurrence is predicted to be highly unstable in this regime as long as the interaction between the two qubits is larger than the couplings between each qubit and the non-Markovian reservoir. In comparison with the previous work, the features we have uncovered extend the work of Yang Li et al. Phys. Rev. A 79, 012309 (2009).
Gab, Parler, and (Mis)educational Technologies: Reconsidering Informal Learning on Social Media Platforms

&ldquo;Alternative&rdquo; social media platforms like Parler and Gab&mdash;on which hate speech and conspiracy theories often exist unchecked&mdash;present an opportunity for instructional designers and other education professionals to revisit assumptions about informal learning on social media. Employing a conceptual framework that distinguishes education from miseducation, we use these controversial platforms to argue that educators should more fully consider: 1) the miseducation happening in learning spaces, 2) how the design of educational technologies may amplify miseducation, and 3) the importance that formal education resist miseducation.
Design of WhatsApp Image Folder Categorization Using CNN Method in the Android Domain

Recently, the use of different social media platforms such as Twitter, Facebook, and WhatsApp have increased significantly. A vast number of static images and motion frame pictures posted on such platforms get stored in the device folder making it critical to identify the social network of the downloaded images in the android domain. This is a multimedia forensic job with major cyber security consequences and is said to be accomplished using unique traces contained in picture material (SNs). Therefore, this proposal has been endeavoured to construct a new framework called FusionNet to combine two well-established single shared Convolutional Neural Networks (CNN) to accelerate the search. Moreover, the FusionNet has been found to improve classification accuracy. Image searching is one of the challenging issues in the android domain besides being a time-consuming process. The goal of the proposed network's architecture and training is to enhance the forensic information included in the digital pictures shared on social media. Furthermore, several network designs for the categorization of WhatsApp pictures have been compared and this suggested method has shown better performance in the comparison. The proposed framework's overall performance was measured using the performance metrics.
CHEMBOND3D e-Module Effectiveness in Enhancing Students’ Knowledge of Chemical Bonding Concept and Visual-spatial Skills

Today's educational challenges necessitate the creative use of digital technology to adapt an effective pedagogical approach in chemistry teaching. While various visualization tools have been developed to improve visual-spatial skills, previous studies on digital technology interventions provide limited findings and show moderate effects on students' learning. Moreover, students still have misconceptions even after using three-dimensional models physically or virtually while learning chemical bonding. Therefore, this study investigates the effectiveness of the CHEMBOND3D e-module that integrates the web-based visualization tool, Molview, on the chemical bonding concept knowledge and visual-spatial skills between treatment groups and control groups. A pretest-posttest non-equivalent control group with a quasi-experimental quantitative design is used in the research. Pilot studies were conducted to verify the validity and reliability of the CHEMBOND3D Chemical Bonding Knowledge Test and Revised Purdue Visualization Test of Rotations. A total of 112 pre-university students from 10 schools in Sabah were selected based on the sampling method. The findings showed significant improvement in the chemical bonding concept knowledge and visual-spatial skills for treatment group students using CHEMBOND3D e-module compared to control group students using conventional methods. This provides new evidence of the potential of web-based application in learning microscopic chemistry concept in chemical bonding. These findings can facilitate further studies of other digital visualization tools such as virtual reality and augmented reality in support of learning complex chemistry concepts in reaction mechanisms and chemical equilibrium.
A Novel Method for Solving Ordinary Differential Equations with Artificial Neural Networks

This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions.In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights.A trial solution of the differential equation is written as a sum of two parts.The first part satisfies the initial or boundary conditions and contains no adjustable parameters.The second part involves a feed-forward neural network to be trained to satisfy the differential equation.Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation.For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression.From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution.To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations.We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective.The weights obtained in this manner need not be adjusted.We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases.We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.