Reviewerly/exHarmony huggingface dataset:
CompTLL-UNet: Compressed Domain Text-Line Localization in Challenging Handwritten Documents Using Deep Feature Learning from JPEG Coefficients

Automatic localization of text-lines in handwritten documents is still an open and challenging research problem. Various writing issues such as uneven spacing between the lines, oscillating and touching text, and the presence of skew become much more challenging when the case of complex handwritten document images are considered for segmentation directly in their respective compressed representation. This is because, the conventional way of processing compressed documents is through decompression, but here in this paper, we propose an idea that employs deep feature learning directly from the JPEG compressed coefficients without full decompression to accomplish text-line localization in the JPEG compressed domain. A modified U-Net architecture known as Compressed Text-Line Localization Network (CompTLL-UNet) is designed to accomplish it. The model is trained and tested with JPEG compressed version of benchmark datasets including ICDAR2017 (cBAD) and ICDAR2019 (cBAD), reporting the state-of-the-art performance with reduced storage and computational costs in the JPEG compressed domain.
Detecting Data Drift with KS Test Using Attention Map

Data drift is a change in the distribution of data during machine learning model training and during operation. This occurs regardless of the type of data and adversely affects model performance. However, this method can only analyze the drift detection results from the difference in the distribution of the output of a two-sample test, and does not analyze the actual degradation of the prediction performance of the machine learning model due to drift. In addition to class probability, we believe that detecting drift for changes in the local region that the model is actually gazing at will improve accuracy. In this study, we propose a drift detection method based on the Attention Branch Network (ABN), which enables visualization of the basis of judgment in image classification. In our method, drift is detected using the class probabilities output by the attention branch and perception branch, which constitute the ABN, and the attention map. The results show that the detection rate can be improved by introducing an attention map to drift detection in addition to class probability. We also observed that the attention map tended to shrink with drift.
Smooth Mask Matters: A Stroke Smoothing Text Removal Framework

Most Scene Text Removal (STR) frameworks are based on predicting the text mask. The predicted text mask is either supervised by the text stroke mask label or the text box mask label in training. We find some matters related to the text stroke mask label, including the label being noisy and the inappropriate 0–1 text stroke mask representation (hard mask). We propose that these matters could be handled by the smooth text stroke mask. Specifically, we made considerable synthetic text segmentation data (text image and its smooth text stroke mask label) by a text image synthesis engine and then trained the text stroke segmentation sub-network of our framework only on the above synthetic data. We also discover that most STR frameworks lack an effective receptive field in their text region inpainting network, which limits their perception ability of the global structure. For the receptive field issue, we devise a two-stage coarse-to-refinement text region inpainting sub-network that consists of a coarse-inpainting stage with a global receptive field and a refinement stage with a local receptive field. Experiments on the benchmark datasets demonstrate that our framework outperforms existing state-of-the-art methods in all Image-Eval metrics and Detection-Eval metrics.
Omnisolver: An extensible interface to Ising spin–glass and QUBO solvers

We introduce a new framework for implementing Binary Quadratic Model (BQM) solvers called Omnisolver. The framework provides an out-of-the-box dynamically built command-line interface as well as an input/output system, thus heavily reducing the effort required for implementing new algorithms for solving BQMs. The proposed software should be of benefit for researchers focusing on quantum annealers or discrete optimization algorithms as well as groups utilizing discrete optimization as a part of their daily work. We demonstrate the ease of use of the proposed software by presenting a step-by-step, concise implementation of an example plugin.
Transformer-based convolutional forgetting knowledge tracking

Knowledge tracking is to analyze the mastery of students' knowledge through the learning track. This is very important for online education, since it can determine a learner's current knowledge level by analyzing the learning history and then make recommendations for future learning. In the past, the commonly used model for knowledge tracking is the convolutional neural network, but it has long-term sequence dependencies. With the invention of Transformer, it has excellent performance in long-sequence modeling by virtue of the attention mechanism, and is gradually introduced into the field of knowledge tracking. However, through our research, some knowledge tracking data sets have a large number of continuous and repetitive training, which will cause Transformer model to ignore the potential connections between some knowledge points. To overcome this problem, we introduce a convolutional attention mechanism to help the model perceive contextual information better. In addition, we simulate the forgetting phenomenon of students during the learning process by calculating the forgetting factor, and fuse it with the weight matrix generated by the model to improve the accuracy of the model. As a result, a Transformer-based Convolutional Forgetting Knowledge Tracking (TCFKT) model is presented in this paper. According to the experimental results conducted on the real world ASSITments2012, ASSISTments2017, KDD a, STATIC datasets, the TCFKT model outperforms other knowledge tracking models.
Adaptive batch mode active learning with deep similarity

Active learning is usually used in scenarios where few labels are available and manual labeling is expensive. To improve model performance, it is necessary to find the most valuable instance among all instances and label it to maximize the benefits of labeling. In practical scenarios, it is often more efficient to query a group of instances instead of a individual instance during each iteration. To achieve this goal, we need to explore the similarities between instances to ensure the informativeness and diversity. Many ad-hoc algorithms are proposed for batch mode active learning, and there are generally two major issues. One is that similarity measurement among instances often only relies on the expression of features but it is not well integrated with the classification algorithm model. This will cut down the precise measurement of diversity. The other is that in order to explore the decision boundary, these algorithms often choose the instance near the boundary. It is difficult to get the true boundary when there are few labeled instances. As a large number of instances continue to be labeled, information between instances is less used, and the performance will be greatly improved if it is properly used. In our work, we propose an adaptive algorithm based on deep neural networks to solve the two problems mentioned above. During the training phase, we established a paired network to improve the accuracy of the classification model, and the network can project the instance to a new feature space for more accurate similarity measurement. When batch labeling instances, we use the adaptive algorithm to select the instance by balancing the maximum uncertainty (exploration) and diversity (exploitation). Our algorithm has been validated for heart failure prediction tasks in real-world EHR datasets. Due to the no public of EHR data, we also conducted validation on two other classic classification tasks. Our algorithm is superior to the baseline method in both accuracy and convergence rate.
FIDES: An ontology-based approach for making machine learning systems accountable

Although the maturity of technologies based on Artificial Intelligence (AI) is rather advanced nowadays, their adoption, deployment and application are not as wide as it could be expected. This could be attributed to many barriers, among which the lack of trust of users stands out. Accountability is a relevant factor to progress in this trustworthiness aspect, as it allows to determine the causes that derived a given decision or suggestion made by an AI system. This article focuses on the accountability of a specific branch of AI, statistical machine learning (ML), based on a semantic approach. FIDES, an ontology-based approach towards achieving the accountability of ML systems is presented, where all the relevant information related to a ML-based model is semantically annotated, from the dataset and model parametrization to deployment aspects, to be exploited later to answer issues related to, reproducibility, replicability, definitely, accountability. The feasibility of the proposed approach has been demonstrated in two scenarios, real-world energy efficiency and manufacturing, and it is expected to pave the way towards raising awareness about the potential of Semantic Technologies in different factors that may be key in the trustworthiness of AI-based systems.
OFDM sensing based on deep learning

Detection of OFDM signals has received paramount interest especially for spectrum sensing in cognitive radios. We present a deep learning-based OFDM signal detection method, namely OFDM-DetNet, which combines feature learning and classifier in a single densely connected convolutional network (DenseNet) structure. OFDM-DetNet completes the detection in an end-to-end manner and utilizes the raw in-phase (I) and quadrature (Q) components of the received signal as the input and the detection result as the output. In addition, a confidence-based fusion method, namely OFDM-DetNet-F, is proposed to further improve the detection performance under low signal-to-noise ratio (SNR) scenarios without retraining the network. We construct three datasets with simulations and three datasets with real-world signals to evaluate the performance of the proposed methods. Both simulation results and experimental results indicate the superior performance of the proposed OFDM-DetNet compared with two deep learning-based OFDM detection methods, i.e., stacked autoencoder (SAE)-based method and covariance matrix (CM)-based detection method, in terms of probability of detection. Furthermore, the performance can be further improved with our proposed fusion scheme in low SNR region.
A convertible neural processor supporting adaptive quantization for real-time neural networks

This paper presents Stimpack, a responsive approach toward adaptive neural processing unit (NPU), aiming to satisfy service-level objectives (SLOs) under highly massive loads of neural network (NN) inference. Initially, Stimpack operates in a base-mode processing and computes NNs similarly to a conventional NPU accelerator. During base-mode processing, if SLO violation is likely to occur soon, it changes the operation mode to a burst-mode processing. In the burst-mode processing, Stimpack computes quantized networks instead of the original ones, enabling computational throughput to be scaled up to two times higher than the base-mode processing. This switchable processing is facilitated by three hardware/software schemes. First, a reconfigurable core is adopted to support two different precisions and boost processing throughput for avoiding SLO violations. As computation resources can be shared between the two operation modes, the area overhead of a reconfigurable core is negligible. The second is an on-chip quantization unit that mitigates the data transfer overhead incurred by mode changing. It quantizes parameters stored in on-chip memory on the fly, instead of bringing quantized parameters from off-chip memory. Third, Stimpack leverages a scheduler that determines mode switching based on the amount of workloads in the server. By monitoring ongoing and queued requests of NPU, the scheduler conservatively activates burst-mode processing to minimize accuracy loss. Our analysis shows that, compared to a state-of-the-art NPU, Stimpack achieves 48.4% speedup and allows a 41.4% large load on average while satisfying SLO and near-ideal accuracy.
Exploring quantum critical phenomena in a nonlinear Dicke model through algebraic deformation

In this work, we use a deformed version of the Heisenberg-Weyl algebra in the Dicke Model to incorporate a Kerr medium and nonlinear interaction between matter and radiation fields. The deformed algebra allows us to define two distinct nonlinear coherent states for the radiation field. These coherent states, along with the conventional Heisenberg-Weyl coherent states and their symmetry-adapted versions, are employed as trial variational states in the energy-surface minimisation method. The obtained results reveal that the Kerr medium introduces a delay in reaching the super-radiant region, with the delay becoming more pronounced as the nonlinear parameter increases. To evaluate the different approaches, we analyse the behavior of the critical coupling, ground-state energy, entropy of entanglement between matter and radiation, and fidelity between the numerical solution and the symmetry-adapted solution. We find that one particular deformed coherent state outperforms the other trial states in approximating the ground state of the system, providing an accurate description of the critical behavior in the proposed nonlinear model.
IDA: An Imbalanced Data Augmentation for Text Classification

With the increasing amount of textual data generated online, an automatic system for text classification is imperative. However, classification models face the challenge of limited and imbalanced data, resulting in poor performance on minority classes. This paper presents a data augmentation technique for imbalanced text classification called Imbalanced Data Augmentation (IDA). The proposed technique consists of three main components: word selection, synonym substitution, and stop word insertion. We evaluate IDA’s performance using an imbalanced dataset of user-generated feedback on Algerian higher education sourced from tweets. Our proposed technique significantly improves the detection of the minority class by achieving the highest F1-score compared to the other evaluated data augmentation methods. Overall, IDA is a useful tool for enhancing the performance of text classifiers on imbalanced datasets by preventing overfitting, improving model generalization, addressing class imbalances, and reducing the cost of collecting and labeling data.
A Real-Time Deep UAV Detection Framework Based on a YOLOv8 Perception Module

Unmanned Aerial Vehicles (UAVs) or drones are currently gaining a lot of popularity due to the versatility of this technology and its ability to perform multiple tasks in various industries. However, arbitrary or malicious use of drones can pose a major risk for public and aviation safety. The automated detection and neutralization of malicious drones to avoid fatal incidents is therefore of primary interest for aerial security systems. Recently, deep learning based approaches for object detection have gained great attention due to their high prediction accuracy. In the proposed work, we use a deep learning object detection model based on latest versions of YOLO, i.e. v7 and v8, to detect and track one drone by another drone in a pursuit-evasion scenario. The detection accuracy achieved with the YOLOv8 model is 99% Average Precision (AP) and the inference is 107.5 FPS, proving the effectiveness of the proposed approach for real-time UAV detection. A comparative study shows that the YOLOv7 model achieves the same accuracy but with slower inference.
Graph Autoencoder with Community Neighborhood Network

Neighborhood information can be extracted from graph data structure. The neighborhood is valuable because similar objects tend to be connected. Graph neural networks (GNN) represent the neighborhood in layers depending on their proximity. Graph autoencoders (GAE) learn the lower dimensional representation of graph and reconstruct it afterward. The performance of the GAE might be enhanced with the behavior of GNNs. However, utilizing the neighborhood information is challenging. Far neighbors are capable of building redundantly complex networks due to their decreasing similarity. Yet, less neighborhood models are closer to GAE. Restricting the neighborhood within the same community enriches the GNN. In this work, we propose a new unsupervised model that combines GNN and GAE to improve the representation learning of graphs. We examine the outcomes of the model under different neighborhood configurations and hyperparameters. We also prove that the model is applicable to varying sizes and types of graphs within different categories on both synthetic and published datasets. The outcome of the community neighborhood network is resistant to overfitting with fewer learnable parameters.
On the Different Concepts and Taxonomies of eXplainable Artificial Intelligence

Presently, Artificial Intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the Machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly “interpretability” and “explainability” was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications.
Simsiam Network Based Self-supervised Model for Sign Language Recognition

To make a more accurate and robust deep learning model, more labeled data is required. Unfortunately, in many areas, it’s very difficult to manage properly labeled data. Sign language recognition is one of the challenging areas of computer vision, to make a successful deep learning model to recognize sign gestures in real-time, a huge amount of labeled data is needed. Authors have proposed a self-supervised learning approach to address this problem. The proposed architecture used Resnet50 v1 backbone-based simsiam encoder network to learn the similarity between two different images of the same class. Calculated cosine similarity passes to MLP head for further classification. The proposed study uses Indian and American Sign Language detests for simulation. The proposed methodology successfully achieve 74.59% of accuracy. Authors have also demonstrated the impact of other self-supervised deep learning models for sign language recognition.
Towards the Detectability of Image Steganography Using NNPRTOOL (Neural Network for Pattern Recognition)

The term steganography represent the science of hiding secret information in a carrier medium like Image, Audio, Video, etc. This field has been participated in multidisciplinary applications and different sectors. For instance, in Healthcare, E-commerce, Access control, and Databases. According to literature, some of those applications, and their analytical, classification data and results are studied. This include current challenges of detectability which can be done with or without using a machine learning classifier. In this research, an additional experiment of Neural Network Pattern Recognition (NNPRTOOL) is applied to classify original and stego images belong to two main classes and directories. This experiment contributes in the robustness of steganography in general. Moreover, it highlights the evolution of image steganalysis research using a machine learning classifier. However, the potential of such tools like NNPRTOOL, Support Vector Machine (SVM), and ENSEMBLE Classifier have shown significant results in image classifications.
Deep Layout Analysis of Multi-lingual and Composite Documents

It is crucial to accurately analyze the layout to convert document images to high-quality text. With the emergence of publicly available, large ground-truth datasets, deep-learning models have demonstrated their effectiveness in detecting and segmenting document layouts. This study presents a deep learning technique for document structure analysis, an important stage in the optical character recognition (OCR) system. Our method employs the YOLOv7 (Only Look Once version 7) model, a highly efficient and precise object detection model trained on the DocLayNet database. The trained YOLOv7 model quickly and efficiently identified and categorized different document components, such as caption, list item, text, table, section header, and picture. Regarding accuracy and efficiency, our evaluation demonstrates that the suggested method beats existing strategies, with strong generalization ability for diverse document layouts, text styles, and scripts.
Breast Cancer Radiogenomics Data Generation Using Combined Generative Adversarial Networks GANs

Radiogenomics is a newly emerging field that integrates genomics with medical imaging data. The aim of this field is to elucidate the associations between gene expression data and imaging phenotypes, especially in cancer. However, radiogenomics is hindered by the expensive cost of genetic screening tests which leads to the unavailability of numerous big datasets for paired imaging and genetic data. Big data is crucial for training machine learning-based techniques for analysis relating to radiogenic studies. Currently, fake data is generated on only one of the two radiogenomic types; genomic or medical imaging data are generated separately. In this paper, we propose a deepfake approach implemented by combining two Generative Adversarial Networks (GANs) to create fake image data that are hardly differentiable from the original to improve Breast cancer diagnosis. To evaluate the model, a survey is developed and distributed among the participants to measure their ability to differentiate the original from the Deepfakes images. The results showed that the model-generated fake images cannot be distinguished from the authentic images and are relatively satisfying using the PyTorch framework.
Identifying Discourse Markers in French Spoken Corpora: Using Machine Learning and Rule-Based Approaches

The objective of this work is to study the identification of French discourse markers (DM), in particular the polyfunctional occurrences such as ‘attetion’, bon, quoi, la preuve. A number of words identified as DM, and traditionally considered as adverbs or interjections, are also, for instance, adjectives or nouns. For example bon can be a DM or an adjective, ‘attetion’ can be a DM or a noun, etc. Hand annotation is in general robust but time consuming. The main difficulty with automatic identification is to take the context of the DM candidate correctly into account. To do that, a mechanisms based on rule-based and machine learning approaches was built, in order to reach an acceptable level of performance and reduce the expert effort. This study will provide a comprehensive use case of a machine learning algorithm, which has proved a good efficiency in dealing with such linguistic phenomena. In addition, an evaluation was done for the Unitex platform in order to determine the efficiency and drawbacks of this platform when dealing with such type of tasks.