Reviewerly/exHarmony huggingface dataset:
Writer Dependent Offline Signature Verification Based on Cluster-Specific Classifier

In this paper, we propose a writer dependent approach for offline signature verification based on writer-specific features and cluster-specific classifiers. In this work, writer-dependency is exploited at three levels: features, classifiers, and clusters. Initially, a template signature is selected for each writer from the training samples of that writer. This template signature serves as a representative signature of the respective writer. The relevant features for each writer are chosen using a filter-based feature selection method. The writers are then clustered based on their similar characteristics using the k-means algorithm. After clustering, a cluster-specific classifier is identified. This classifier is then set as the default classifier for all the writers in that cluster. During verification, writer-specific features and cluster-specific classifiers of the claimed writer are used to verify the authenticity of the given test signature. The approach is verified on three benchmarking offline signature datasets: CEDAR, MCYT, and GPDS-960.
Deep Neural Networks Pruning via the Structured Perspective Regularization

In machine learning, artificial neural networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, and therefore a large number of parameters, which makes training, storage, and inference expensive. This motivated a stream of research about compressing the original networks into smaller ones without excessively sacrificing performances. Among the many proposed compression approaches, one of the most popular is pruning, whereby entire elements of the ANN (links, nodes, channels, …) and the corresponding weights are deleted. Since the nature of the problem is inherently combinatorial (what elements to prune and what not), we propose a new pruning method based on operational research tools. We start from a natural mixed-integer-programming model for the problem, and we use the perspective reformulation technique to strengthen its continuous relaxation. Projecting away the indicator variables from this reformulation yields a new regularization term, which we call the structured perspective regularization, that leads to structured pruning of the initial architecture. We test our method on some ResNet architectures applied to CIFAR-10, CIFAR-100, and ImageNet datasets, obtaining competitive performances w.r.t. the state of the art for structured pruning.
A Historical and Current Review of Extended Reality Technologies and Applications

&lt;p&gt;This paper provides a review of Extended Reality (ER) technologies and applications. The different technologies related to ER, such as Virtual Reality, Augmented Reality and Mixed Reality are introduced and explained, following an historical perspective. The basic technical concepts behind these technologies are briefly explained. Current applications are presented, including references to actual tools and systems, in order to clarify the concepts and the potential of the technologies. &lt;/p&gt;
Exploding AI-Generated Deepfakes and Misinformation: A Threat to Global Concern in the 21st Century

&lt;p&gt;Deepfakes the term was coined in 2018 by a Reddit user who created a Reddit forum dedicated to the creation and use of deep learning software for synthetically face swapping female celebrities into pornographic videos. According to Sumsub's research in 2023, the top-5 identity fraud types in 2023 are AI-powered fraud, money muling networks, fake IDs, account takeovers, and forced verification. The country most attacked by deepfakes is Spain; the most forged document worldwide is the UAE passport, whereas Latin America is the region where fraud has increased in every country. On November 24, 2023, the Union Government of India issued an advisory to social media intermediaries to identify misinformation and deepfakes.&lt;/p&gt; &lt;p&gt;A deepfake refers to a specific kind of synthetic media where a person in an image or video is swapped with another person's likeness. AI-generated deepfakes have emerged as a complex and pervasive challenge in today's digital landscape, enabling the creation of remarkably convincing yet falsified multimedia content. This review paper examines the multifaceted landscape of deepfakes, encompassing their technological underpinnings, societal implications, detection methodologies, and ethical considerations.&lt;/p&gt; &lt;p&gt;The review aggregates and synthesizes a broad array of scholarly articles, studies, and reports to elucidate the diverse typologies of deepfakes, including face-swapping, voice cloning, and synthetic media, while delineating the intricate methodologies employed in their fabrication. This review culminates in an overview of future directions and recommendations, advocating for proactive measures to counter the escalating threat posed by AI-generated deepfakes.&lt;/p&gt;
An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures.&#x0D; Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples.&#x0D; Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase.&#x0D; Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates.&#x0D; Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases.
Advances in Sentiment Analysis in Deep Learning Models and Techniques

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation.
Harnessing Data-Driven Insights: Predictive Modeling for Diamond Price Forecasting using Regression and Classification Techniques

In the multi-faceted world of gemology, understanding diamond valuations plays a pivotal role for traders, customers, and researchers alike. This study delves deep into predicting diamond prices in terms of exact monetary values and broader price categories. The purpose was to harness advanced machine learning techniques to achieve precise estimations and categorisations, thereby assisting stakeholders in informed decision-making. The research methodology adopted comprised a rigorous data preprocessing phase, ensuring the data's readiness for model training. A range of sophisticated machine learning models were employed, from traditional linear regression to more advanced ensemble methods like Random Forest and Gradient Boosting. The dataset was also transformed to facilitate classification into predefined price tiers, exploring the viability of models like Logistic Regression and Support Vector Machines in this context. The conceptual model encompasses a systematic flow, beginning with data acquisition, transitioning through preprocessing, regression, and classification analyses, and culminating in a comparative study of the performance metrics. This structured approach underscores the originality and value of our research, offering a holistic view of diamond price prediction from both regression and classification lenses. Findings from the analysis highlighted the superior performance of the Random Forest regressor in predicting exact prices with an R2 value of approximately 0.975. In contrast, for classification into price tiers, both Logistic Regression and Support Vector Machines emerged as frontrunners with an accuracy exceeding 95%. These results provide invaluable insights for stakeholders in the diamond industry, emphasising the potential of machine learning in refining valuation processes.
EFEKTIVITAS METODE COOPERATIVE LEARNING PADA HASIL BELAJAR BAHASA INDONESIA MATERI PUISI SISWA KELAS X SMA AL-IKHLAS KOTA BEKASI

Learning the Indonesian language and literature still needs to be optimal. Learning needs to get the attention of teachers and educational observers. Good learning makes students happy and comfortable and gets optimal learning results. This study aimed to determine the effectiveness of cooperative learning in class X high school students with poetry material. The method used is an experimental method with the type of pretest-posttest. Respondents who participated in the study were 42 students. Research activities were carried out from January to March 2019. The study's results stated an increase of 4.7464 when students used cooperative learning. The learning process begins with forming groups and interactions between students and teachers. This method can provide added value before and after using the method.
<i>A<sup>2</sup>Pt</i>: Anti-Associative Prompt Tuning for Open Set Visual Recognition

Multi-modality pre-trained models (PTMs) have considerably boosted the performance on a broad range of computer vision topics. Still, they have not been explored purposefully in open set recognition (OSR) scenarios when applying PTMs to downstream recognition tasks. Directly fine/prompt tuning PTMs on closed-set classification tasks will inevitably suffer from data bias and always learn more or less target class-irrelevant cooccurring contextual information, which leads to over-confident predictions on unknown samples. In this paper, we propose a simple yet effective approach, termed Anti-Associative Prompt Tuning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(A<sup>2</sup>Pt)</i> , toward learning compact and accurate class-related representation with few class-irrelevant associations from context using multi-modal priors. Specifically, a cross-modal guided activation module is adopted to refine the class-aware representation and suppress the associations from co-occurring contexts by involving text-modal information. We further design an anti-association calibration module to obtain compact class-aware and class-irrelevant representations, respectively, by introducing two additional object functions. Extensive experiments on publicly available benchmarks, including CIFAR series, Tiny-ImageNet, and ImageNet-21K-P, show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(A<sup>2</sup>Pt)</i> achieves substantial and consistent performance gains compared with both SOTA OSR and PTM prompt tuning approaches.
AN IMPROVED MASK R-CNN: EXTRACTION OF DOOR AND WINDOW INSTANCES ON VILLAGE BUILDING FAÇADE IMAGES

Abstract. Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The development of computer vision technology provides new ideas and tools for identifying and extracting basic structures of housing buildings. Considering that the original Mask R-CNN ignores the spatial association and relationship of door and window elements, an advanced deep learning model based on Mask R-CNN network is proposed in this paper to detect and segment the door and window structure from the façade images. The improved network architectures integrate the attention mechanism with the original network, containing an improved Coordinate Attention(CA) module and a relationship module-based head network. The experimental results show that the Average Precision(AP) value of the backbone combined with the improved CA module is increased by 0.7% and 0.7% on regression and segmentation tasks respectively, compared with the original Mask R-CNN network. In the head network based on the relationship module, the calculation strategy of the relational module proposed in this paper increases the AP values of detection and segmentation from 76.7% and 77.7% to 80.6% and 80.0%, respectively.
Support Vector Machine

This chapter introduces three classes of Support Vector Machines (SVMs), kernel functions, and a fast learning algorithm—the Sequential Minimum Optimization (SMO) algorithm.
The new changes. Education and today's world.

The changes brought about by the COVID19 pandemic have been significant, affecting all areas of life as we know it. The efforts to adapt are varied and important; in the case of education, these changes are more profound and complex. Historically, education has had to adapt to constant and changing demands for its very reason for being, to educate to meet the needs of the environment; today, a global environment that seeks comprehensive and skilled individuals for the world and in this way achieve a positive impact on society.&#x0D; Latin America has approximately 156 million students (United Nations, 2021; ECLAC, 2020) and 68 million affected educators (Aleman Vilca, Alarcon Saravia, Monzon Alvarez &amp; Pastor Xespe, 2021) during and post-pandemic, which widens the inequality gap for access to education. Clearly the world was not prepared for such a global health situation and the solution in the case of education was to migrate towards virtual, distance and remote face-to-face education; deteriorating interpersonal relationships (Moya et al., 2020) and the psycho-pedagogical care of the training process (Argandoña Mendoza et al., 2020).&#x0D; The use of digital platforms to which not everyone had or has access and/or knowledge in their use, made things even more difficult, hindering the effective educational inclusion that must be achieved (Quintana and Vida de León, 2022), bringing varied consequences such as: increase in forced school dropout; stress, depression and anxiety; learning depends on adults at home; use of multiple digital platforms; others (Gómez-Gómez, Hijón-Neira, Santacruz-Valencia and Pérez-Marín, 2022).
SAVING LIVES FROM ABOVE: PERSON DETECTION IN DISASTER RESPONSE USING DEEP NEURAL NETWORKS

Abstract. This paper focuses on person detection in aerial and drone imagery, which is crucial for various operations such as situational awareness, search and rescue, and safe delivery of supplies. We aim to improve disaster response efforts by enhancing the speed, safety, and effectiveness of the process. Therefore, we introduce a new person detection dataset comprising 311 annotated aerial and drone images, acquired from helicopters and drones in different scenes, including urban and rural areas, and for different scenarios, such as estimation of damage in disaster-affected zones, and search and rescue operations in different countries. The amount of data considered and level of detail of the annotations resulted in a total of 10,050 annotated persons. To detect people in aerial and drone images, we propose a multi-stage training procedure to improve YOLOv3’s ability. The proposed procedure aims at addressing challenges such as variations in scenes, scenarios, people poses, as well as image scales and viewing angles. To evaluate the effectiveness of our proposed training procedure, we split our dataset into a training and a test set. The latter includes images acquired during real search and rescue exercises and operations, and is therefore representative for the challenges encountered during operational missions and suitable for an accurate assessment of the proposed models. Experimental results demonstrate the effectiveness of our proposed training procedure, as the model’s average precision exhibits a relevant increase with respect to the baseline value.
Crosstalk-Robust Quantum Control in Multimode Bosonic Systems

Universal control of multimode bosonic modes typically relies on a dispersively coupled transmon ancilla. However, its frequency undergoes a Stark shift that depends on the state of each coupled bosonic mode, leading to a frequency mismatch in the ancilla drive and ultimately reducing the fidelity of multimode gates
Exploration of superconducting multi-mode cavity architectures for quantum computing

on the analysis of the interaction between a nonlinear superconducting circuit known as the transmon and the cavity. This parametric design optimization is structured to serve as a blueprint for future studies on similar systems.
Security Challenges in Autonomous Systems Design

Autonomous systems are emerging in many application domains. With the recent advancements in artificial intelligence and machine learning, sensor technology, perception algorithms and robotics, scenarios previously requiring strong human involvement can be handled by autonomous systems. With the independence from human control, cybersecurity of such systems becomes even more critical as no human intervention in case of undesired behavior is possible. In this context, this paper discusses emerging security challenges in autonomous systems design which arise in many domains such as autonomous incident response, risk assessment, data availability, systems interaction, trustworthiness, updatability, access control, as well as the reliability and explainability of machine learning methods. In all these areas, this paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions to address these challenges for developing secure autonomous systems.
Event-driven Real-time Retrieval in Web Search

Information retrieval in real-time search presents unique challenges distinct from those encountered in classical web search. These challenges are particularly pronounced due to the rapid change of user search intent, which is influenced by the occurrence and evolution of breaking news events, such as earthquakes, elections, and wars. Previous dense retrieval methods, which primarily focused on static semantic representation, lack the capacity to capture immediate search intent, leading to inferior performance in retrieving the most recent event-related documents in time-sensitive scenarios. To address this issue, this paper expands the query with event information that represents real-time search intent. The Event information is then integrated with the query through a cross-attention mechanism, resulting in a time-context query representation. We further enhance the model's capacity for event representation through multi-task training. Since publicly available datasets such as MS-MARCO do not contain any event information on the query side and have few time-sensitive queries, we design an automatic data collection and annotation pipeline to address this issue, which includes ModelZoo-based Coarse Annotation and LLM-driven Fine Annotation processes. In addition, we share the training tricks such as two-stage training and hard negative sampling. Finally, we conduct a set of offline experiments on a million-scale production dataset to evaluate our approach and deploy an A/B testing in a real online system to verify the performance. Extensive experimental results demonstrate that our proposed approach significantly outperforms existing state-of-the-art baseline methods.
Do androids dream of fictional references? A bibliographic dialogue with ChatGPT3.5

This article focuses on bibliographic references generated by the ChatGPT3.5 tool. Using this tool based on the trained GPT generation model ChatGPT3.5, developed by the company OpenAI, we explored six different themes and analyzed a sample of references generated by the model, in French and English. The results revealed high percentages of fictitious references in several fields, underlining the importance of carefully checking these references before using them in research work. An improvement in results was nevertheless noted between May and July with regard to English references for themes on which ChatGPR3.5 has been particularly trained, but the situation remains unsatisfactory in French, for example. It should also be pointed out that much of the text in this article was generated by ChatGPT in a joint effort with the human author.