Reviewerly/exHarmony huggingface dataset:
Predict the Transformed Spatial Distance for a Pair of Image Patches using Densely Connected Convolutional Networks

Image feature matching is a critical task in computer vision. In this paper, we propose to utilize the densely connected convolutional network for image feature matching. Unlike previous approaches that design the network from a classification perspective, our network can estimate the transformed spatial distance (TSD) from a pair of image patches. This spatial distance can not only determine the correctness of a match but also provide additional information about the match. To verify the applicability of our system in practical scenarios, we also introduce a test set from real images. Experimental results demonstrate that for simulated test data, our network achieves an average root mean square error of approximately 1.52 pixels, while for the real image test data, the root mean square error is around 1.98 pixels. These results surpass the previous research that used basic CNN architectures for TSD estimation. Overall, our research shows promising improvements in TSD estimation using the densely connected convolutional network, both on simulated and real datasets.
A Text Block Refinement Framework For Text Classification and Object Recognition From Academic Articles

With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically extracting specific patterns in complex and unstructured layout documents. Current data mining methods for academic articles employ rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods incurs a high coding cost for complex typesetting articles. On the other hand, simply using machine learning methods requires annotation work for complex content types within the paper, which can be costly. Furthermore, only using machine learning can lead to cases where patterns easily recognized by rule-based methods are mistakenly extracted. To overcome these issues, from the perspective of analyzing the standard layout and typesetting used in the specified publication, we emphasize implementing specific methods for specific characteristics in academic articles. We have developed a novel Text Block Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid. We used the well-known ACL proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% classification accuracy and 90% detection accuracy for tables and figures.
A Consortium Model for Managing Nodes in Smart Factories Based on Blockchain and Smart Contracts

Industry 4.0 integrates advanced information and communication technologies into industrial systems to enhance efficiency. Cyber-Physical Systems (CPS) are a vital component, merging computer control and physical processes in manufacturing. Managing intelligent nodes in a CPS becomes intricate with new additions, necessitating effective security mechanisms. Blockchain technology provides a decentralized solution for transaction security, and smart contracts automate management. This article thoroughly examines intelligent node management within a CPS, proposing an innovative solution that employs a Blockchain consortium and smart contracts for automation. Specific challenges of smart factories are also addressed. Our solution is adaptable and generic. A preliminary study showcased the effectiveness of our approach, with future projects planned. Preliminary results indicate a significant enhancement in intelligent node management through Blockchain technology and smart contracts.
A fully automatic Bayesian model for adaptive activation functions in artificial neural networks

There is a significant focus in deep neural networks on finding activation functions that can enhance network performance. Scientists are also interested in developing flexible activation functions that can adapt during training to avoid overfitting and provide reliable parameter learning.This paper presents a Bayesian algorithm that employs Markov Chain Monte Carlo (MCMC) to estimate activation function parameters and model weights. This algorithm accelerates convergence through effective sampling and evaluates its effectiveness on two datasets, achieving high accuracy with low complexity.
Deep Reinforcement Learning-based Load Balancer using Kubernetes

The performance of irregular scientific applications can be easily affected by an uneven distribution of loads between computing resources. In this context, load balancing is one of the most important solutions to improve resource utilization. Yet many heuristic and metaheuristic algorithms have been proposed in the literature for dynamic load balancing, choosing the best performing load balancing algorithm for a given application is not a trivial task especially in situations where applications have a dynamic behavior.This paper proposes a new approach of Load Balancing using Deep Reinforcement Learning. The proposed agent schedules tasks to the cluster’s node using state of the art scheduling algorithms. Based on the user’s tasks and the available nodes, the agent must choose an efficient scheduling algorithm as an action from the action space and use it to schedule the pending tasks to cluster’s nodes. The agent’s action space is composed of state of the art scheduling algorithms. This approach optimizes both the average execution time and the resource utilization while keeping a low Imbalance Degree in the cluster.
Pre-Trained Deep Convolutional Neural Network Architectures for Breast Cancer Diagnosis in Mammography: Current State-Of-The-Art

Breast cancer is a prevalent and life-threatening disease affecting many women worldwide. Early diagnosis plays a crucial role in improving survival rates and allowing effective treatment. Mammography is recognized as the most effective imaging modality for breast cancer diagnosis. To achieve early diagnosis, researchers have developed intelligent systems based on Deep Learning (DL) models, specifically Convolutional Neural Networks (CNNs), which are well-suited for analyzing medical images. Training deep CNN models for breast cancer diagnosis poses challenges, mainly due to the limited size of publicly available mammography datasets commonly used by researchers. This scarcity of data can lead to overfitting issues. Obtaining a large-scale breast mammogram dataset is a time-consuming and expensive process in clinical practice. Researchers have successfully employed Transfer Learning (TL) to overcome this issue. In this scenario, models are initially trained on large datasets from other domains, generating pre-trained models that capture general image representations. These models are then fine-tuned on a breast cancer image dataset. This approach leverages the learned knowledge from the first dataset to improve the performance of DL models in the breast cancer diagnosis task. This survey aims to present the latest research findings in the realm of TL architectures applied to breast cancer diagnosis using limited mammography datasets. It covers the following areas: (i) The structure of CNNs, (ii) Essential background knowledge on TL, (iii) Diverse strategies for implementing TL, (iv) Commonly employed pre-trained CNN architectures, (v) Well-known publicly available mammography datasets along with their key characteristics and strengths, and (vi) A summary of the current state-of-the-art pretrained CNNs specifically applied to breast cancer diagnosis. The survey focuses on the performance and key findings of these models, showcasing their effectiveness in enhancing diagnostic accuracy. This survey seeks to summarize the current trends in TL for breast cancer diagnosis using mammography images, intending to inspire researchers to actively contribute to the progress of transfer learning in the mammogram image analysis field.
Context Sensitive Detection of Long-term Elderly Behavior Change

The detection of behavior changes by activities of daily living applications is very relevant in health care monitoring systems, particularly for older persons. Behavior change detection can be of crucial importance and which may traduce the early stage of a disease. Our research objectives are focused on building a model to conduct a continuous and long-term analysis of elderly's behavior in order to detect slow changes. The originality of this work is to use rich contextual information such as weather conditions, holidays and seasons to identify and learn routine behavior patterns. These patterns are updated throughout the aging process of the elderly person by proposing a novel dynamic behavior clustering method through an iterative process using a moving-window mechanism. The routine behavior patterns are then used for comparing each observed day's behavior to the associated routine behavior pattern in preceding period to define a similarity score. By plotting the daily score along time, we exhibit the period when the score changes over time. The observations on a use case with real datasets are promising.
CROWDPRED: Privacy-Preserving Approach for locations on Decentralized Crowdsourcing Application

The continuous disclosure of location information in the context of Mobile CrowdSourcing (MCS) raises privacy concerns due to the potential correlation among different location reports. Existing approaches primarily focus on protecting the privacy of individual location data points without considering the risks posed by tracking attacks resulting from the ongoing disclosure of workers’ locations to MCS service providers. Furthermore, these approaches fail to address the potential threats arising from continuously disclosing location information during the execution of various sensing tasks. To address these issues, it becomes essential to maintain a comprehensive historical record of workers who have performed diverse sensing tasks and leverage this historical information to accurately quantify location-privacy disclosure. In this paper, we propose a novel approach that integrates blockchain technology to enhance privacy protection in MCS. We investigate how an attacker could infer the true location by exploiting noisy location data shared on the blockchain, along with their basic knowledge of users and frequently visited places. By employing prediction techniques, we aim to mitigate the potential risks associated with continuous location disclosure. Our approach offers improved privacy management, enhances MCS efficiency, and maintains a balance between privacy preservation and task fulfillment.
Official Document Text Extraction using Templates and Optical Character Recognition

Documents have been used across history ever since civilized societies first began appearing. Documents are used everywhere today in our daily activities and were affected by technological leaps. From documents written on paper, we switched to digital documents. One of the technological fields that are dealing with documents is Computer Vision, specifically OCR, or optical character recognition. OCR is the process in which an image containing text is converted into digital text format [1]. Because computers are used everywhere nowadays, systems have already been designed for working with documents. In many systems that deal with documents, there is still a need for manual work. This paper proposes a way in which OCR can be applied to official documents for the extraction of their text.
Evaluation of Deep Neural Network Quality by CleanAI Coverage Metrics Library

The increasing use of AI-based systems in critical domains necessitates the assessment of their quality and reliability. However, there is a lack of readily available tools for analyzing metrics related to the structure, security, safety, and quality control of Deep Neural Network (DNN) models. To address this gap, CleanAI is introduced as a white-box testing library that utilizes coverage metrics to evaluate the structural analysis, quality, and reliability parameters of DNN models. The library incorporates eleven coverage test methods and enables developers to perform essential model analyses and generate output reports. By using CleanAI, developers can effectively evaluate the quality of DNNs and make informed decisions. The library's effectiveness was demonstrated through testing popular models like ResNet50, providing insights into neuron coverage, threshold value coverage, and other coverage metrics. The adoption of CleanAI can significantly evaluate the quality of AI-based systems and result in time and labor cost savings for companies and developers in the industry.
Book Reckon - The Use of Virtual Reality in the Creation of Libraries of the Future

In recent years, the way we make the decision to read a book has diversified a lot. From simple methods based on friends’ recommendations or the desire to read a book after watching a movie based on that book, to more complex methods that use artificial intelligence algorithms that build increasingly advanced user profiles. In this context, our project comes with a solution in virtual reality, where the user receives various types of information about books (textual, visual, auditory) and recommendations based on the advanced profiles we build for them. Thus, he can make the decision to read a book based on more information that he accesses within an interesting experience given by the virtual space where he interacts with them. The experiments carried out showed the readers’ interest and desire to explore other methods different from the traditional ones to find the desired book. They appreciated the fact that different types of information can be brought in the same application and can contribute to their final decision in choosing the desired book.
A Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus

Data corpus tend to be heterogeneous presenting a significant challenge in extracting meaningful knowledge from, especially with the rapid growth of digital data. Traditional approaches lack when it comes to handling enormous volumes of unstructured data existing across various sources. Knowledge Graphs, becoming a more and more trendy topic, offer advanced modelization that can help cover such gap. Knowingly, data labeled graph and RDF triplestores are data management approaches that are built on modeling, storing, and querying graph-like data. Despite this fundamental idea, each have unique characteristics that hamper database interoperability. While some methods exist to convert databases to RDF graph or to property graphs and vice versa, they still lack consistency and solid formal foundation. This paper describes Novel Approach for Extracting Summarized RDF Graph from Heterogeneous Corpus.
ACCP-MC-U-Net: Automatic Corpus Callosum Parcellation from brain MRI scans using MultiClass U-Net

Accurate segmentation of the Corpus Callosum (CC) plays a crucial role in studying brain connectivity and understanding neurological disorders. However, limited availability of annotated data poses a significant challenge for developing robust segmentation models. In order to deal with this issue, we propose in the study an effective approach that combines one-shot learning and a modified multiclass U-Net architecture. The proposed approach represents the first attempt in this context, to the best of our knowledge. We begin by generating additional Ground Truth (GT) data using one-shot learning, effectively expanding the limited annotated dataset. This approach leverages the inherent generalization capability of one-shot learning to predict segmentation for unlabeled data, which are then validated and refined by domain experts. The refined segmentation serves as new GT data, enhancing the training process. To further improve parcellation accuracy, we modify the U-Net architecture to handle the complex task of multiclass CC parcellation. The modified multiclass U-Net effectively captures the intricate features and spatial dependencies within the CC, enabling precise parcellation into distinct sub-regions. The framework has been tested and evaluated on two challenging datasets that are publicly available. The obtained results are promising and show the performance of the proposed solution against geometric methods from the state of the art.
A metaplastic neural network technique for human activity recognition for Alzheimer’s patients

Artificial Intelligence technology has made a huge leap forward. However, the current systems still fall short when faced with the catastrophic challenge of forgetting. Deep neural networks face huge limitations, including the tendency to forget learned tasks as they perform new tasks. While neuroscience research suggests that the human brain synapses can overcome this challenge through the ability to adapt based on historical information, applying similar mechanisms to deep neural networks to prevent forgetting previous data proves to be a monumental task.In this study, our focus lies on using these improved deep networks to provide assistance to people afflicted with Alzheimer’s disease, a prevalent condition among the elderly. Due to cognitive impairments, individuals with Alzheimer’s often require ongoing support to carry out daily activities necessary for their well-being. To tackle the issue of forgetfulness in deep neural networks, specifically binary neural networks, and grant them the ability of multitasking and continuous learning, we propose an effective training method. This can be achieved by treating the hidden weights as semi-modifiable variables and adapting the training methodology accordingly. The proposed remote monitoring system consists of two main components. The first component is the Human Activity and Patient Behavior Monitoring (HAR) Identification Unit, which focuses on monitoring and identifying patterns in the activities and behaviors of Alzheimer’s patients. The second component is a support unit that detects anomalies and behavioral problems and provides appropriate warnings. This system is based on augmentative learning technology. As a result of our research, we have developed auxiliary systems specifically designed for Alzheimer’s patients. Activity details are as follows. We achieved excellent results for Activity 1 (Task 1) with an accuracy of 99.51% and Activity 2 (Task 2) was also well-addressed by our system, reaching an accuracy of 96.63%. Finally, we conducted a comparative study, comparing our system’s performance complexity with previous systems using the Dem@care Dataset.
Arabic-Latin Scene Text Detection based on YOLO Models

Machine learning and artificial intelligence have led to notable progress in the field of deep learning and the identification of text within natural scene images in the past few years. Despite notable advancements, the effectiveness of deep learning and text detection in natural scene images, particularly for Arabic language, is frequently constrained by the scarcity of comprehensive datasets containing various multilingual scripts. YOLO (You Only Look Once) is a widely used deep learning neural network that has gained immense popularity for its versatility in handling diverse machine learning tasks, primarily in the field of computer vision. The YOLO algorithm has progressively garnered recognition for its exceptional performance to solving a complex problems, noisy data, as well as overcoming various challenges encountered in real-world scenarios. Our experiments provide a concise examination of text detection algorithms based on convolutional neural networks (CNNs), especially different versions of the YOLO models using a data augmentation technique applied to "SYPHAX" dataset, our new dataset of multilingual scripts in the wild. The objective of this paper is to offer insights into the future of YOLO in the field, highlighting potential research directions that can enhance text detection systems.
Attention-based Network for Image/Video Salient Object Detection

The goal of video or image salient object detection is to identify the most important object in the scene, which can be helpful in many computer vision-based tasks. As the human vision framework has a successful capacity to effortlessly perceive locales of interest from complex scenes, salient object detection mimics a similar concept. However, the salient object detection (SOD) of complex video scenes is a challenging task. This paper mainly focuses on learning from channel and Spatiotemporal representations for image/video salient object detection. The proposed method consists of three levels, the frontend, the attention models, and the backend. While the frontend consists of VGG backbone which ultimately learns the representation of the common and the discrimination features. After that, both Attention, Channel-wise, and Spatiotemporal models are applied to highlight the significant object using a feature detector and to calculate the spatial attention. Then the output features are fused to obtain the final saliency result. Experimental investigation evaluations confirm that our proposed model has proved its validity and effectiveness compared with the state-of-the-art methods.
A Comparison of Machine Learning Algorithms for Multilingual Phishing Detection

With phishing emails being a major problem worldwide which is only getting larger by the year, there needs to exist solutions to combat them as they can cause tremendous harm to society. This research aims to compare numerous machine learning models and transformers for multilingual spam detection using English, French, and Russian emails. We evaluate the models using accuracy as two of the three experiments have nearly balanced test data. Our results show that, on average, XLM-Roberta performs the best out of all of the tested models in terms of accuracy.
Towards a Framework for Developing Verified Assemblers for the ELF Format

Abstract Most of the existing work on verified compilation leaves unverified the translation of assembly programs into binary code in object file formats (e.g., the Executable and Linkable Format or ELF). The challenges of developing verified assemblers come from the intrinsic complexities in low-level assembling processes caused by the need to support different computer architectures and their details, such as encoding a large number of instructions and verifying its correctness. We present a framework that overcomes the above challenges. It works as a template which may be instantiated to generate verified assemblers for different architectures targeting ELF object files. For this, it is parameterized over the implementation and verification of architecture-dependent assembling processes through well-defined interfaces. By plugging the architecture-dependent parts into the template, we get complete verified assemblers. To manage the complexity in developing and verifying encoding of instructions, we integrate into our framework the CSLED framework for automatically generating verified instruction encoders and decoders from declarative instruction specifications. To show the effectiveness of our framework, we have applied it to generate verified assemblers for the complete X86 and RISC-V assembly languages in CompCert.