Reviewerly/exHarmony huggingface dataset:
TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism

In modern distributed computing applications, such as federated learning and AIoT systems, protecting privacy is crucial to prevent adversarial parties from colluding to steal others’ private information. However, guaranteeing the utility of computation outcomes while protecting all parties’ data privacy can be challenging, particularly when the parties’ privacy requirements are highly heterogeneous. In this paper, we propose a novel privacy framework for multi-party computation called Threshold Personalized Multi-party Differential Privacy (TPMDP), which addresses a limited number of semi-honest colluding adversaries. Our framework enables each party to have a personalized privacy budget. We design a multi-party Gaussian mechanism that is easy to implement and satisfies TPMDP, wherein each party perturbs the computation outcome in a secure multi-party computation protocol using Gaussian noise. To optimize the utility of the mechanism, we cast the utility loss minimization problem into a linear programming (LP) problem. We exploit the specific structure of this LP problem to compute the optimal solution after $\mathcal{O}(n)$ computations, where n is the number of parties, while a generic solver may require exponentially many computations. Extensive experiments demonstrate the benefits of our approach in terms of low utility loss and high efficiency compared to existing private mechanisms that do not consider personalized privacy requirements or collusion thresholds.
Detecting Confounders in Multivariate Time Series using Strength of Causation

One of the most important problems in science is understanding causation. This is particularly challenging when one has access to observational data only and is further compounded in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are used for estimating both, the DCE of the observed time series and the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths.
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images

Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image analysis, as the visual characteristics of tissues can vary significantly across datasets. Yet, acquiring sufficient annotated data in the medical domain is cumbersome and time-consuming. The labeling effort can be significantly reduced by leveraging active learning, which enables the selective annotation of the most informative samples. Our proposed method allows for fine-tuning a pretrained deep neural network using a small set of labeled data from the target domain, while also actively selecting the most informative samples to label next. We demonstrate that our approach performs with significantly fewer labeled samples compared to traditional supervised learning approaches for similar F1-scores, using barely a 59% of the training set. We also investigate the distribution of class balance to establish annotation guidelines.
A Relaxed Optimization Approach for Adversarial Attacks Against Neural Machine Translation Models

In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversarial examples that are semantically similar to the original sentences but destroy the translation generated by the target NMT model. This optimization problem is discrete, and we propose a continuous relaxation to solve it. With this relaxation, we find a probability distribution for each token in the adversarial example, and then we can generate multiple adversarial examples by sampling from these distributions. Experimental results show that our attack significantly degrades the translation quality of multiple NMT models while maintaining the semantic similarity between the original and adversarial sentences. Furthermore, our attack outperforms the baselines in terms of success rate, similarity preservation, effect on translation quality, and token error rate. Finally, we propose a black-box extension of our attack by sampling from an optimized probability distribution for a reference model whose gradients are accessible. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code of our attack can be found at https://github.com/sssadrizadeh/NMT-untargeted-attack.
Text-to-Text Pre-Training with Paraphrasing for Improving Transformer-Based Image Captioning

In this paper, we propose a novel training method for the transformer encoder-decoder based image captioning, which directly generates a captioning text from an input image. In general, many image- to- text paired datasets need to be prepared for robust image captioning, but such datasets cannot be collected in practical cases. Our key idea for mitigating the data preparation cost is to utilize text-to-text paraphrasing modeling, i.e., a task to convert an input text into different expressions without changing the meaning. In fact, paraphrasing deals with a similar transformation task to image captioning even though paraphrasing tasks have to handle texts instead of images. In our proposed method, an encoder-decoder network trained via the paraphrasing task is directly leveraged for image captioning. Thus, an encoder-decoder network pre-trained by a text-to-text transformation task is transferred into an image-to-text transformation task even though a different modal must be handled in the encoder network. Our experiments using the MS COCO caption datasets demonstrate the effectiveness of the proposed method.
Harnessing the Power of Explanations for Incremental Training: A LIME-Based Approach

Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of neural network outcomes are mostly limited to visualization, and there is scarce work that looks to use these explanations as feedback to improve model performance. In this work, model explanations are fed back to the feed-forward training to help the model generalize better. To this extent, a custom weighted loss where the weights are generated by considering the Euclidean distances between true LIME (Local Interpretable Model-Agnostic Explanations) explanations and model-predicted LIME explanations is proposed. Also, in practical training scenarios, developing a solution that can help the model learn sequentially without losing information on previous data distribution is imperative due to the unavailability of all the training data at once. Thus, the framework incorporates the custom weighted loss with Elastic Weight Consolidation (EWC) to maintain performance in sequential testing sets. The proposed custom training procedure results in a consistent enhancement of accuracy ranging from 0.5% to 1.5% throughout all phases of the incremental learning setup compared to traditional loss-based training methods for the keyword spotting task using the Google Speech Commands dataset.
DANSE: Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Bayesian Setup

We propose DANSE – a data-driven non-linear state estimation method. DANSE provides a closed-form posterior of the state of a model-free process, given linear measurements of the state in a Bayesian setup, like the celebrated Kalman filter (KF). Non-linear dynamics of the state are captured by data-driven recurrent neural networks (RNNs). The training of DANSE combines maximum-likelihood and gradient-descent in an unsupervised framework, i.e. only measurement data and no process data are required. Using simulated linear and non-linear process models, we demonstrate that DANSE - without knowledge of the process model - provides competitive performance against model-based approaches such as KF, unscented KF (UKF), extended KF (EKF), and a hybrid approach such as KalmanNet.
Online Handwriting Gesture Recognition Using Transformer and Natural Language Processing

The Transformer architecture is shown to provide a powerful machine transduction framework for online handwritten gestures corresponding to glyph strokes of natural language sentences. The attention mechanism is successfully used to create latent representations of an end-to-end encoder-decoder model, solving multi-level segmentation while also learning some language features and syntax rules. The additional use of a large decoding space with some learned Byte-Pair-Encoding (BPE) is shown to provide robustness to ablated inputs and syntax rules. The encoder stack was directly fed with spatio-temporal data tokens potentially forming an infinitely large input vocabulary, an approach that finds applications beyond that of this work. Encoder transfer learning capabilities is also demonstrated on several languages resulting in faster optimisation and shared parameters. A new supervised dataset of online handwriting gestures suitable for generic handwriting recognition tasks was used to successfully train a small transformer model to an average normalised Levenshtein accuracy of 96% on English or German sentences and 94% in French.
Steered-Mixture-of-Experts Regression for Image Denoising with Multi-Model Inference

In this paper we introduce a novel block-based regression strategy for image denoising based on edge-aware Steered-Mixture-of-Experts (SMoE) models. SMoEs provide very sparse image representations - able to model sharp edges as well as smooth transitions in images efficiently with few parameters. A multi-model inference strategy is developed that improves significantly the denoising capacity of single SMoE models. We show that the important edge reconstruction properties of SMoEs are well preserved, even when many models are fused under severe noise. We investigate model-inference from local neighborhood blocks as well as from distant blocks using block-matching as in BM3D. Our initial results indicate that SMoE multi-model regression can provide promising results compared to state-of-the-art BM3D with excellent edge quality.
Democratizing the discourse on criminal justice in social media: the activity for justice for Roman Zadorov as a case study

Abstract The outcomes of police investigations and legal proceedings frequently remain shielded from public scrutiny, leaving ordinary individuals devoid of access to these results and devoid of platforms for discourse. As a result, the general public’s ability to access legal materials and partake in informed discussions about them remains constricted, often reliant on processed information disseminated through mainstream media channels. However, what unfolds when materials become accessible and discussion platforms attain viability? This article studies the conditions that lead to more meaningful and extensive participation by non-state actors in criminal investigations. It also examines the attributes and consequences of the “democratization of knowledge” concerning legal cases, exemplified by the expansive public discourse surrounding the Zadorov trial on Israeli social media platforms. The paper highlights three conditions of such democratization processes: (1) the digitization of investigative materials, (2) the transparency of investigative materials to the public, and (3) the existence of discursive arenas in which people can discuss the case. I proceed to outline several ramifications stemming from the previously discussed traits of these democratization processes: (1) The preservation of materials related to individuals implicated in specific troublesome situations; (2) The utilization of “raw materials” to articulate stances and viewpoints; (3) The employment of materials to construct narratives that diverge significantly from those embraced by the establishment; and (4) The recurrent references to private individuals or public officials who might be deemed unprofessional or even corrupt.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. Previous related works aim to develop QA systems that answer temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning this period can be fully used for inference. In real-world scenarios, however, it is common that given knowledge until the current instance, we wish the TKGQA systems to answer the questions asking about future. As humans constantly plan the future, building forecasting TKGQA systems is important. In this paper, we propose a novel task: forecasting TKGQA, and propose a coupled large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions. It includes three types of forecasting questions, i.e., entity prediction, yes-unknown, and fact reasoning questions. For every question, a timestamp is annotated and QA models only have access to TKG information prior to it for answer inference. We find that previous TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-unknown and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference. Experiments show that it performs well in forecasting TKGQA.
Digital Forensic Investigation on Ponzi Schemes

The Ponzi scheme is an economic offence to lure investors by giving false assurance of huge returns with less risk. The money is provided to the older investors from the new investors in the form of payouts. Mostly the scheme would collapse or in the verge of collapse when the reduction of new investors. The scheme would try to retain later or the latest customers to reinvest or roll over the payouts into the scheme. Ponzi schemes are also performing as "Smart Ponzi Scheme" [1] where new technology (blockchain) cryptocurrency has been used indirectly. Some Ponzi scheme adopts a hybrid model without the knowledge of investors. Fugazzi financial securities are also given in the form of bonds with some security values, especially payouts are enticed to roll over again. The Ponzi scheme would initially be started and pretend to be a formal and genuine financial business and would certainly know the scheme would collapse. The scheme's brainchild or brainchildren would counter and prepare for the easy way outs. The accumulated money is invested in real estate, movable properties, Panama papers, gold investments, cryptocurrencies, smart contracts (Cryptocurrency), and offshore investments. The new and the latest investors were victimized a lot. The financial victimization will be more from the new investors to the initial or oldest investors. The fairness of identifying actual financial loss incurred by the investors has to be justified for a fair settlement. The nature of the Ponzi scheme itself is a discrete business and the brainchild/brainchildren behind the scheme have constructed business infrastructure in such a way that they cannot be caught or tracked or detected. They have chosen complex technological infrastructure to make the investigation process difficult. The Ponzi scheme is accomplished by complex infrastructure in a way digital forensics investigation process made so difficult to detect. Ponzi scheme identification and intelligence about people and infrastructure are to be collected properly else the break in the detection chain would end up in fragile evidence collection. Understanding of infrastructure of the Ponzi scheme model is crucial to gather all information and quantifying the actual amount and people who were involved in the Ponzi scheme. The magnitude of the Ponzi scheme scam would only be identified by the proper digital forensic investigation process. This paper discusses the complex infrastructure adopted by the Ponzi schemes. The hurdles and challenges faced by the investigation team and digital forensics Investigation team to detect the magnitude of the scam involved. This paper also addresses the lacuna of policy, enforcement, and regulatory lens on the Ponzi scheme with respect to the existing monitoring system and infrastructure.
Evolutionary Ensemble Learning

Evolutionary Ensemble Learning (EEL) provides a general approach for scaling evolutionary learning algorithms to increasingly complex tasks. This is generally achieved by developing a diverse complement of models that provide solutions to different (yet overlapping) aspects of the task. This chapter reviews the topic of EEL by considering two basic application contexts that were initially developed independently: (1) ensembles as applied to classification and regression problems and (2) multi-agent systemsMulti-agent system as typically applied to reinforcement learningReinforcement Learning tasks. We show that common research themes have developed from the two communities, resulting in outcomes applicable to both application contexts. More recent developments reviewed include EEL frameworks that support variable-sized ensembles, scaling to high cardinality or dimensionality, and operation under dynamic environments. Looking to the future we point out that the versatility of EEL can lead to developments that support interpretable solutions and lifelong/continuous learning.
The search for professional digital competence in Swedish teacher education policy—A content analysis of the prerequisites for teacher educators’ dual didactic task

This study is an exploration of the prerequisites in Swedish teacher education policy for teacher educators' dual didactic task of developing student teachers' professional digital competence to such a level that they are capable of developing K–12 pupils' adequate digital competence. Data were collected from 20 Swedish teacher education institutions offering teacher education programs in which student teachers could earn the degree of Master of Arts in Primary Education for School Years 4–6. Overall, the data comprised national guidelines and curriculum regulations for teacher education in Sweden (e.g. the Swedish Higher Education Ordinance; N = 1), program syllabi at the selected teacher education institutions (N = 20), and course plans (for all 240 ECTS, 4-year full-time studies; N = 450), in total N = 471 policy documents. Signs of professional digital competence in policy were few, and most were found in the course plans for mathematics and natural sciences. In the discussion, findings are problematized in relation to the challenging role of teacher educators as second-order teachers seeking to fulfill their dual didactic task.
Entanglement control in a laser driven single layer graphene system

Abstract In this letter, we have proposed a new model for quantum control of atom photon entanglement in a single layer graphene via von Neumann reduced entropy of entanglement. We consider the effect of terahertz laser field intensity on the degree of entanglement (DEM) in the resonance and off-resonance condition of the applied fields. We also investigate the spatially dependent of the DEM when two applied light becomes standing wave pattern in x and y directions. Our results show that in different parametric conditions, the population of the different states can be controlled and this leads to modifying the DEM of the system.
Integrating IoT and honey badger algorithm based ensemble learning for accurate vehicle detection and classification

The fusion of Internet of Things (IoT) and deep learning (DL) methods has proven valuable in automating vehicle detection and classification tasks on remote sensing images (RSI). This technology has broad applications, including traffic monitoring, urban planning, and transportation management. Recent advancements have demonstrated the efficacy of DL models like convolutional neural networks (CNN) in RSI classification tasks. In this aspect, this study proposes a novel honey badger optimization algorithm with an ensemble learning-based vehicle detection and classification (HBOAEL-VDC) technique. The purpose of the study is to design ensemble DL models for accurate vehicle identification and classification processes. To accomplish this, the HBOAEL-VDC technique makes use of an improved RetinaNet model for the detection of objects, i.e., vehicles on the RSI. Moreover, the classification of detected vehicles takes place using the ensemble learning process, comprising three DL models, namely gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (BiLSTM). Furthermore, the HBOA-based parameter tuning process gets carried out to adjust the hyperparameter values of the DL models and thereby improve the classification results. The simulation outcome of the HBOAEL-VDC approach is tested on benchmark RSI databases. The experimentation outcomes reported the enhanced vehicle classification performance of the HBOAEL-VDC approach over other recent DL models.
Automatic Multilingual Question Generation for Health Data Using LLMs

Question Generation (QG) involves automatic generation of yes/no, factual and Wh-questions created from data sources such as a database, raw text or semantic representation. QG can be used in an adaptive intelligent tutoring system or a dialog system for improving question answering in various text generation tasks. Traditional QG has used syntactic rules with linguistic features to generate factoid questions. However, more recent research has proposed using pre-trained Transformer-based models for generating questions that are more aware of the answers. The goal of this study was to create a multilingual database (English and Spanish) of automatically generated sets of questions using artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in particular for a culturally sensitive health intelligent tutoring system (ITS) for the Hispanic population. Several language models (LMs) including Chat GPT, valhalla/t5-based-e2e-qg, T5 (Small, Base, and Large), mrm8488/bert2bert-spanish-question-generation, mT5 (Small and Base), Flan-T5 (Small, Base and Large), BART (Base and Large) and mBART (Large) were chosen for our experiments that were given a prompt to produce a set of questions (3, 5, 7 or 10) using transcribed texts as the context. We observed that a text/transcript of at least 100 words was sufficient to generate 5–7 questions of reasonable quality. When models were prompted to produce 10 or more questions based on texts containing 100 words or less the meaningfulness, syntax and semantic soundness of outputs decreased notably.