Reviewerly/exHarmony huggingface dataset:
Vehicle Detection and Classification using YOLOv5 on Fused Infrared and Visible Images

Visual surveillance systems in intelligent transportation systems need to detect and classify vehicles in order to create an ambient environment in smart cities. The presence of data imbalances reduces the performance of these systems. The suggested model utilizes deep learning techniques for effective identification and classification of vehicles. The YOLO v5 architecture is utilised for detection and can identify very small vehicles like bikes and scooters on the road. A fusion model is designed to generate a composite image with more complementary features for detection purposes. The fusion of infrared and visible modalities can create images of improved resolution that are suitable for night-time vehicle recognition and suited for the surveillance of remote locations. The quality of the fused image was also verified with similar models, and structural similarity is very good. In comparison with similar models, the novel algorithm produces good metric values like 0.96 for accuracy, 0.97 for sensitivity, 0.97 for specificity, and 0.93 for precision.
Conversion of Ambiguous Grammar to Unambiguous Grammar using Parse Tree

The approach described in this research study uses parse trees to transform ambiguous syntax into unambiguous grammar. There is no algorithm that can detect whether a grammar is ambiguous or not. The steps that will be considered in the proposed system to convert an ambiguous grammar into unambiguous are: Precedence of operators and the Associativity rule. Multiple interpretations of a statement might result from ambiguous grammar, making it challenging for natural language processing systems to recognize and respond to the intended meaning. The proposed approach entails creating a parse tree for the input text and utilizing it to locate and eliminate ambiguity sources. Experiments on a dataset of phrases with unclear syntax are used to assess the method's efficacy, indicating the potential for enhanced performance in natural language processing systems.
A Light Recipe to Train Robust Vision Transformers

In this paper, we ask whether Vision Transformers (ViTs) can serve as an underlying architecture for improving the adversarial robustness of machine learning models against evasion attacks. While earlier works have focused on improving Convolutional Neural Networks, we show that also ViTs are highly suitable for adversarial training to achieve competitive performance. We achieve this objective using a custom adversarial training recipe, discovered using rigorous ablation studies on a subset of the ImageNet dataset. The canonical training recipe for ViTs recommends strong data augmentation, in part to compensate for the lack of vision inductive bias of attention modules, when compared to convolutions. We show that this recipe achieves suboptimal performance when used for adversarial training. In contrast, we find that omitting all heavy data augmentation, and adding some additional bag-of-tricks <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon-\mathbf{warmup}$</tex> and larger weight decay), significantly boosts the performance of robust ViTs. We show that our recipe generalizes to different classes of ViT architectures and large-scale models on full ImageNet-1k. Additionally, investigating the reasons for the robustness of our models, we show that it is easier to generate strong attacks during training when using our recipe and that this leads to better robustness at test time. Finally, we further study one consequence of adversarial training by proposing a way to quantify the semantic nature of adversarial perturbations and highlight its correlation with the robustness of the model. Overall, we recommend that the community should avoid translating the canonical training recipes in ViTs to robust training and rethink common training choices in the context of adversarial training. We share the code for your experiments at the following URL: https://github.com/dedeswim/vits-robustness-torch.
Optimal Data Acquisition with Privacy-Aware Agents

We study the problem faced by a data analyst or platform that wishes to collect private data from privacy-aware agents. To incentivize participation, in exchange for this data, the platform provides a service to the agents in the form of a statistic computed using all agents' submitted data. The agents decide whether to join the platform (and truthfully reveal their data) or not participate by considering both the privacy costs of joining and the benefit they get from obtaining the statistic. The platform must ensure the statistic is computed differentially privately and chooses a central level of noise to add to the computation, but can also induce personalized privacy levels (or costs) by giving different weights to different agents in the computation as a function of their heterogeneous privacy preferences (which are known to the platform). We assume the platform aims to optimize the accuracy of the statistic, and must pick the privacy level of each agent to trade-off between i) incentivizing more participation and ii) adding less noise to the estimate. We provide a semi-closed form characterization of the optimal choice of agent weights for the platform in two variants of our model. In both of these models, we identify a common nontrivial structure in the platform's optimal solution: an instance-specific number of agents with the least stringent privacy requirements are pooled together and given the same weight, while the weights of the remaining agents decrease as a function of the strength of their privacy requirement. We also provide algorithmic results on how to find the optimal value of the noise parameter used by the platform and of the weights given to the agents.
Harnessing Prior Knowledge for Explainable Machine Learning: An Overview

The application of complex machine learning models has elicited research to make them more explainable. However, most explainability methods cannot provide insight beyond the given data, requiring additional information about the context. We argue that harnessing prior knowledge improves the accessibility of explanations. We hereby present an overview of integrating prior knowledge into machine learning systems in order to improve explainability. We introduce a categorization of current research into three main categories which integrate knowledge either into the machine learning pipeline, into the explainability method or derive knowledge from explanations. To classify the papers, we build upon the existing taxonomy of informed machine learning and extend it from the perspective of explainability. We conclude with open challenges and research directions.
Encoder and Decoder-Based Feature Fusion Network for Single Image Dehazing

Single image defogging that aims to restore a fog-free image from its appropriately unconstrained hazy environment is a fundamental yet complex work that has recently achieved enormous interest. However, images reconstructed by certain available haze-removal approaches frequently retain artefacts, and color distortions, drastically degrading the visual quality and adversely affecting vision tasks. To that aim, we propose an encoder-decoder model that combines feature fusion with channel and color attention to improve real-time dehazing performance. Feature fusion block analyzes distinct features and pixels unequally, allowing for greater mobility in handling multiple types of input features and increasing model efficiency. The detailed quantitative and qualitative evaluation findings show that the suggested technique outperforms state-of-the-art techniques on dehazing data sets and real-time hazy images.
Elliptic Curve Cryptography for Network Security

This research study discusses about the concept of elliptic curve cryptography (ECC), and how it offers a faster and more secure technique of encryption in compared to the existing standards in the RSA public-key cryptographic methods. All pertinent asymmetric cryptographic primitives, such as digital signatures and key agreement procedures, are covered by elliptic curve cryptography. The primary operation of ECCs, scalar multiplication (k.P), is the function employed for this purpose. P is a point on an elliptic curve, and k is an integer. In this article, the function of ECC in network security is explained. ECC uses smaller keys for enhanced security and quick processing.
Comparative Analysis of Path-based Similarity Measures for Word Sense Disambiguation

In this paper, we have implemented a novel unsupervised graph-based algorithm for Hindi Word Sense Disambiguation. For disambiguation, we perform a random walk on graph created for each instance. The nodes in the graph are various senses of the words appearing in the context of ambiguous word. The edge weights are assigned using semantic similarity between pair of nodes. We compare two path-based similarity measures. The experimental investigations suggest that a Leacock-Chodrow similarity measure performs better than Shortest path measure. We observed an accuracy of 72.09% averaged over all the instances of five Polysemous nouns.
Reducing Certified Regression to Certified Classification for General Poisoning Attacks

Adversarial training instances can severely distort a model's behavior. This work investigates certified regression defenses, which provide guaranteed limits on how much a regressor's prediction may change under a poisoning attack. Our key insight is that certified regression reduces to voting-based certified classification when using median as a model's primary decision function. Coupling our reduction with existing certified classifiers, we propose six new regressors provably-robust to poisoning attacks. To the extent of our knowledge, this is the first work that certifies the robustness of individual regression predictions without any assumptions about the data distribution and model architecture. We also show that the assumptions made by existing state-of-the-art certified classifiers are often overly pessimistic. We introduce a tighter analysis of model robustness, which in many cases results in significantly improved certified guarantees. Lastly, we empirically demonstrate our approaches' effectiveness on both regression and classification data, where the accuracy of up to 50% of test predictions can be guaranteed under 1% training set corruption and up to 30% of predictions under 4% corruption. Our source code is available at https://github.com/ZaydH/certified-regression.
Align-then-abstract representation learning for low-resource summarization

Generative transformer-based models have achieved state-of-the-art performance in text summarization. Nevertheless, they still struggle in real-world scenarios with long documents when trained in low-resource settings of a few dozen labeled training instances, namely in low-resource summarization (LRS). This paper bridges the gap by addressing two key research challenges when summarizing long documents, i.e., long-input processing and document representation, in one coherent model trained for LRS. Specifically, our novel align-then-abstract representation learning model (Athena) jointly trains a segmenter and a summarizer by maximizing the alignment between the chunk-target pairs in output from the text segmentation. Extensive experiments reveal that Athena outperforms the current state-of-the-art approaches in LRS on multiple long document summarization datasets from different domains.
Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Support vector machine has emerged as one of the most crucial classification techniques in recent years in pattern recognition, machine learning, and data mining. The model training will become more challenging when dealing with multi-classification problems, and its training time will increase considerably as the number of samples increases. A quick training data reduction technique MOIS that works with multiple classification problems is provided to address the issues above. This method takes the cluster centre as the reference point. While removing redundant training samples, selects the decisive boundary samples to significantly reduce the training data and reduce the distribution imbalance between categories. Experimental results show that MOIS can maintain or even improve the classification effect of support vector machines. Moreover, it can significantly improve training efficiency. For example, on the Opt digit data set, the proposed method improves the classification accuracy from 98.94% to 99.05% and, at the same time, shortens the training time to 15%; On the data set composed of the first 100 categories, when the accuracy rate is slightly improved (from 99.29% to 99.30%), the training time is significantly shortened to less than 6% of the original. In addition, MOIS itself has High operating efficiency.
A Lie group kernel learning method for medical image classification

Medical image classification is a basic step in medical image analysis and has been an essential task in computer-aided diagnosis. Existing classification methods are proved to be effective in conventional image classification tasks, but they often achieve a suboptimal performance when applied to medical images characterizing by complex nonlinear variation. Aiming at this challenge, this paper proposes a Lie group kernel learning method for medical image classification by combining Lie group theory, kernel functions, SVM and KNN classifiers. The method represents each image with a Lie group feature descriptor constructed from low-level features and builds a SVM classifier from the training images. Geodesic distances between categorical pivots and each testing image are calculated with Lie group kernel functions to select either the SVM or a KNN classifier to do the classification. The proposed method is applied to three medical image datasets and the results demonstrate the efficacy of the method.
Reflection transformation calibration for mirror-assisted multi-view digital image correlation system using fluorescent speckle patterns

Mirror-assisted multi-view digital image correlation (MV-DIC) is a simple-to-implement DIC technique for panoramic kinematic field measurements of regular-sized specimens. In the practical implementation of mirror-assisted MV-DIC, accurate reflection transformation calibration of the planar mirrors is considered as a pivotal step. In this work, we propose to use fluorescent speckle patterns fabricated on the mirrors to calibrate their reflection transformation matrices, which can fully utilize the camera's spatial resolution and simplify the calibration processes. The fluorescent speckle patterns on the mirrors do not interfere with the virtual object surfaces using a regular blue-light stereo-DIC system. However, under the illumination of ultraviolet light, the fluorescent speckle patterns can be captured and used to calibrate the position and orientation of the planar mirrors. The practicality and efficacy of the fluorescent speckle pattern-based calibration method are validated by profile measurement and translation tests, and an application in a four-point bending experiment is also demonstrated.
Federated Learning on Multimodal Data: A Comprehensive Survey

With the growing awareness of data privacy, federated learning (FL) has gained increasing attention in recent years as a major paradigm for training models with privacy protection in mind, which allows building models in a collaborative but private way without exchanging data. However, most FL clients are currently unimodal. With the rise of edge computing, various types of sensors and wearable devices generate a large amount of data from different modalities, which has inspired research efforts in multimodal federated learning (MMFL). In this survey, we explore the area of MMFL to address the fundamental challenges of FL on multimodal data. First, we analyse the key motivations for MMFL. Second, the currently proposed MMFL methods are technically classified according to the modality distributions and modality annotations in MMFL. Then, we discuss the datasets and application scenarios of MMFL. Finally, we highlight the limitations and challenges of MMFL and provide insights and methods for future research.
Subsample analysis of stock market – cryptocurrency returns tail dependence: A copula approach for the tails

This paper describes the extremal and tail dependence between G7 stock market returns (USA, Canada, UK, Japan, Germany, France, Italy) and cryptocurrency returns (Bitcoin, Ethereum, Dash, Monero, Ripple) on the basis of the bivariate extremal dependence model (Padoan and Stupfler, 2022) and the bivariate test for equality of tail copulas (Can et al., 2023). Over a daily data period from January 1, 2016 through January 5, 2023, the findings reveal significant differences in tail risk between the categories of G7 stock market returns and cryptocurrency returns, highlighting the need to find an appropriate asset to hedge individual financial asset tail risks. The results also demonstrate a time-invariant tail dependence between G7 stock market returns and cryptocurrency returns, as the Wald test supports the null of an equal tail dependence structure between stock market and cryptocurrency returns during both calm and crises times. The finding offer important policy implications for portfolio managers and investors.
Deep learning for few-shot white blood cell image classification and feature learning

Differential counting of white blood cells (WBCs) in bone marrow using artificial intelligence (AI)-based models, such as convolutional neural network (CNN) and its various variants, can help physicians to efficiently diagnose many critical diseases such as leukaemia, AIDS and cancers. In this work, we implement a deep transfer learning to several CNN models to examine their effectiveness on automatically classifying WBCs into lymphocytes and non-lymphocytes groups. Our results show that transfer learning can enhance the training of the model and improve the model performance. We also discover that using image masking to remove irrelevant image pixels can further increase the accuracy of the model predictions. Moreover, we assess the impact of three data augmentation techniques to address the imbalance in the data set, which commonly occurs in many biological applications. Our results show that all the three examined data augmentation methods improve the classification results on both training and testing data sets. Altogether, we demonstrate that deep neural networks, when combined with transfer learning and imaging processing techniques, can serve as a powerful tool to conduct automatic differential counting of WBCs, and thus facilitate the diagnosis of the WBC-related disorders, monitor the disease progression and improve the effectiveness of therapeutics.
Self-protection of IoT Gateways Against Breakdowns and Failures Enabling Automated Sensing and Control

Smart home IoT technologies have provided a new level of overall degrees of control freedom over modern homes. The core of such a system is an edge device. In this paper, a CNN-based LSTM-Autoencoder method is presented to detect anomaly points in five critical operating parameters of an edge device while managing its perpetual operation. This proposed method is based on a hybrid model using 1D-CNN layers in the encoder layer and LSTM layers in the decoder layer. Experiments were conducted using real data from Raspberry Pi devices. Compared to other state-of-the-art methods, the proposed approach had a remarkable accuracy close to 0.996 and an execution time of 312 ms.
Accelerating DNN Inference With Reliability Guarantee in Vehicular Edge Computing

This paper explores on accelerating Deep Neural Network (DNN) inference with reliability guarantee in Vehicular Edge Computing (VEC) by considering the synergistic impacts of vehicle mobility and Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. First, we show the necessity of striking a balance between DNN inference acceleration and reliability in VEC, and give insights into the design rationale by analyzing the features of overlapped DNN partitioning and mobility-aware task offloading. Second, we formulate the Cooperative Partitioning and Offloading (CPO) problem by presenting a cooperative DNN partitioning and offloading scenario, followed by deriving an offloading reliability model and a DNN inference delay model. The CPO is proved as NP-hard. Third, we propose two approximation algorithms, i.e., Submodular Approximation Allocation Algorithm (SA3) and Feed Me the Rest algorithm (FMtR). In particular, SA3 determines the edge allocation in a centralized way, which achieves 1/3-optimal approximation on maximizing the inference reliability. On this basis, FMtR partitions the DNN models and offloads the tasks to the allocated edge nodes in a distributed way, which achieves 1/2-optimal approximation on maximizing the inference reliability. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solutions.
SpoTNet: A spoofing-aware Transformer Network for Effective Synthetic Speech Detection

The prevalence of voice spoofing attacks in today's digital world has become a critical security concern. Attackers employ various techniques, such as voice conversion (VC) and text-to-speech (TTS), to generate synthetic speech that imitates the victim's voice and gain access to sensitive information. The recent advances in synthetic speech generation pose a significant threat to modern security systems, while traditional voice authentication methods are incapable of detecting them effectively. To address this issue, a novel solution for logical access (LA)-based synthetic speech detection is proposed in this paper. SpoTNet is an attention-based spoofing transformer network that includes crafted front-end spoofing features and deep attentive features retrieved using the developed logical spoofing transformer encoder (LSTE). The derived attentive features were then processed by the proposed multi-layer spoofing classifier to classify speech samples as bona fide or synthetic. In synthetic speeches produced by the TTS algorithm, the spectral characteristics of the synthetic speech are altered to match the target speaker's formant frequencies, while in VC attacks, the temporal alignment of the speech segments is manipulated to preserve the target speaker's prosodic features. By highlighting these observations, this paper targets the prosodic and phonetic-based crafted features, i.e., the Mel-spectrogram, spectral contrast, and spectral envelope, presenting an effective preprocessing pipeline proven to be effective in synthetic speech detection. The proposed solution achieved state-of-the-art performance against eight recent feature fusion methods with lower EER of 0.95% on the ASVspoof-LA dataset, demonstrating its potential to advance the field of speaker identification and improve speaker recognition systems.