Reviewerly/exHarmony huggingface dataset:
Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer

Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes. Automatic diagnosis methods based on artificial intelligence have shown promising results in the oral cancer field, but the accuracy still needs to be improved for realistic diagnostic scenarios. Vision Transformers (ViT) have outperformed learning CNN models recently in many computer vision benchmark tasks. This study explores the effectiveness of the Vision Transformer and the Swin Transformer, two cutting-edge variants of the transformer architecture, for the mobile-based oral cancer image classification application. The pre-trained Swin transformer model achieved 88.7% accuracy in the binary classification task, outperforming the ViT model by 2.3%, while the conventional convolutional network model VGG19 and ResNet50 achieved 85.2% and 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional convolutional neural networks in terms of oral cancer image classification, and underscore the potential of the ViT and the Swin Transformer in advancing the state of the art in oral cancer image analysis.
Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, a heuristic discrimination method is engaged in the path search phase, whereby the issue of sluggish search velocity is tackled by evaluating whether sampled points reside at “bridging locations” within a free space, and by assessing the spatial–geometric relationships between proximate obstacles and auxiliary points. Subsequently, by leveraging extended speed, additional sampling points are generated in the vicinity of existing points to augment the search’s efficacy. Ultimately, the path is optimized and pruned by synthesizing the local curvature of the sampling points and the proximity to obstacles, assigning varied priorities to nodes, thus ensuring that the path’s quality and smoothness is upheld.
Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%-90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.
Using LSTM to translate Thai sign language to text in real time

Abstract Between 2019 and 2022, as the Covid-19 pandemic unfolded, numerous countries implemented lockdown policies, leading most corporate companies to permit employees to work from home. Communication and meetings transitioned to online platforms, replacing face-to-face interactions. This shift posed challenges for deaf or hearing-impaired individuals who rely on sign language, using hand gestures for communication. However, it also affected those who can hear clearly but lack knowledge of sign language. Unfortunately, many online meeting platforms lack sign language translation features. This study addresses this issue, focusing on Thai sign language. The objective is to develop a model capable of translating Thai sign language in real-time. The Long Short-Term Memory (LSTM) architecture is employed in conjunction with MediaPipe Holistic for data collection. MediaPipe Holistic captures keypoints of hand, pose, and head, while the LSTM model translates hand gestures into a sequence of words. The model’s efficiency is assessed based on accuracy, with real-time testing achieving an 86% accuracy, slightly lower than the performance on the test dataset. Nonetheless, there is room for improvement, such as expanding the dataset by collecting data from diverse individuals, employing data augmentation techniques, and incorporating an attention mechanism to enhance model accuracy.
Camouflage: Utility-Aware Obfuscation for Accurate Simulation of Sensitive Program Traces

Trace-based simulation is a widely used methodology for system design exploration. It relies on realistic traces that represent a range of behaviors necessary to be evaluated, containing a lot of information about the application, its inputs and the underlying system on which it was generated. Consequently, generating traces from real-world executions risk leakage of sensitive information. To prevent this, traces can be obfuscated before release. However, this can undermine their ideal utility, i.e., how realistically a program behavior was captured. To address this, we propose Camouflage, a novel obfuscation framework, designed with awareness of the necessary architectural properties required to preserve trace utility , while ensuring secrecy of the inputs used to generate the trace. Focusing on memory access traces, our extensive evaluation on various benchmarks shows that camouflaged traces preserve the performance measurements of the original execution, with an average τ correlation of 0.66. We model input secrecy as an input indistinguishability problem and show that the average security loss is 7.8%, which is better than traces generated from the state-of-the-art.
Whisper-based spoken term detection systems for search on speech ALBAYZIN evaluation challenge

Abstract The vast amount of information stored in audio repositories makes necessary the development of efficient and automatic methods to search on audio content. In that direction, search on speech (SoS) has received much attention in the last decades. To motivate the development of automatic systems, ALBAYZIN evaluations include a search on speech challenge since 2012. This challenge releases several databases that cover different acoustic domains (i.e., spontaneous speech from TV shows, conference talks, parliament sessions, to name a few) aiming to build automatic systems that retrieve a set of terms from those databases. This paper presents a baseline system based on the Whisper automatic speech recognizer for the spoken term detection task in the search on speech challenge held in 2022 within the ALBAYZIN evaluations. This baseline system will be released with this publication and will be given to participants in the upcoming SoS ALBAYZIN evaluation in 2024. Additionally, several analyses based on some term properties (i.e., in-language and foreign terms, and single-word and multi-word terms) are carried out to show the Whisper capability at retrieving terms that convey specific properties. Although the results obtained for some databases are far from being perfect (e.g., for broadcast news domain), this Whisper-based approach has obtained the best results on the challenge databases so far so that it presents a strong baseline system for the upcoming challenge, encouraging participants to improve it.
Multipartite Entanglement: A Journey through Geometry

Genuine multipartite entanglement is crucial for quantum information and related technologies, but quantifying it has been a long-standing challenge. Most proposed measures do not meet the "genuine" requirement, making them unsuitable for many applications. In this work, we propose a journey toward addressing this issue by introducing an unexpected relation between multipartite entanglement and hypervolume of geometric simplices, leading to a tetrahedron measure of quadripartite entanglement. By comparing the entanglement ranking of two highly entangled four-qubit states, we show that the tetrahedron measure relies on the degree of permutation invariance among parties within the quantum system. We demonstrate potential future applications of our measure in the context of quantum information scrambling within many-body systems.
Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy

Summary Due to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures.
Governing Chinese technologies: TikTok, foreign interference, and technological sovereignty

TikTok bans have been presented as one solution to threats to national security, data security, foreign interference, child safety, and foreign espionage.In this article we investigate four countries/regions -Australia, the United Kingdom, the United States, and the European Unionthat have banned or attempted to govern TikTok, examining the policy and legal bases for such restrictions.Our analysis is conceptually informed by legal and political narrations of foreign interference and technological sovereignty.We approach this with particular attention to countries with existing intelligence and data sharing agreements (i.e. three members of the Five Eyes alliance and the trilateral AUKUS alliance) and the European Union given its regulatory approach to data protection.This research makes significant and timely contributions to the geopolitics of TikTok and foreign interference in an international context.It informs inconsistencies in regulatory and legal approaches relating to foreign interference and data sovereignty, beyond "China threat" narratives.We argue that the European Union regulation presents an approach that attempts to protect citizens and citizen data rather than attack platforms and governments that challenge Western technological hegemony.
Extrinsic Calibration for a Modular 3D Scanning Quality Validation Platform with a 3D Checkerboard

Optical 3D scanning applications are increasingly used in various medical fields. Setups involving multiple adjustable systems require repeated extrinsic calibration between patients. Existing calibration solutions are either not applicable to the medical field or require a time-consuming process with multiple captures and target poses. Here, we present an application with a 3D checkerboard (3Dcb) for extrinsic calibration with a single capture. The 3Dcb application can register captures with a reference to validate measurement quality. Furthermore, it can register captures from camera pairs for point-cloud stitching of static and dynamic scenes. Registering static captures from TIDA-00254 to its reference from a Photoneo MotionCam-3D resulted in an error (root mean square error ± standard deviation) of 0.02 mm ± 2.9 mm. Registering a pair of Photoneo MotionCam-3D cameras for dynamic captures resulted in an error of 2.2 mm ± 1.4 mm. These results show that our 3Dcb implementation provides registration for static and dynamic captures that is sufficiently accurate for clinical use. The implementation is also robust and can be used with cameras with comparatively low accuracy. In addition, we provide an extended overview of extrinsic calibration approaches and the application's code for completeness and service to fellow researchers.
Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction

Due to its significant solar energy generation, solar PV power facilities have recently been utilized. Although PV power stations are highly preferable, the state’s fundamental drawback is that its output current qualities are unpredictable. Consequently, to ensure a balance and complete functioning, it would be crucial to build systems that allow accurate future projections of solar PV production in the short or medium term. Research postulates a strategy for using deep learning to estimate the short-term electricity generated by solar photovoltaic facilities. This study offers a novel method for predicting photovoltaic systems output power utilizing a Hybrid Deep Neural Network framework, making significant advancements in the field of deep learning applications to transmission system prediction issues. CNN and LSTM are combined in the postulated HDNN paradigm. Traditional deep learning techniques are employed in the initial stage. The effectiveness assessments of these techniques are instead presented in greater detail after they have been trained using firefly optimization techniques. The method with the highest reliability is chosen out of all the techniques used in previous research. Deep learning and power efficiency create a combination that appears to have a successful future, predominantlyin improving sustainable management and the digitalization of the electrical sector.
A clustering effectiveness measurement model based on merging similar clusters

This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the "rough clustering" process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two clusters and the average inter-cluster similarity in the entire sample set to decrease the maximum number of clusters Kmax. Then, a new scheme is proposed to calculate intra-cluster compactness, inter-cluster relative density, and inter-cluster overlap coefficient. On the basis of this new method, several internal evaluation indices based on intra-cluster cohesion and inter-cluster dispersion are designed. Results of experiments show that the proposed model can perform clustering and classification correctly and provide accurate ranges for clustering using public UCI and NSL-KDD datasets, and it is significantly superior to the three improved clustering algorithms compared with it in terms of intrusion detection indices such as detection rate and false positive rate (FPR).
Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.
The Role of Sulfuric Acid, Abiotic–Organic Acids, and Biotic Acids on Serpentinite Dissolution and Trace Metal Release

Organic acids produced by biota have been shown to accelerate the dissolution of minerals, possibly creating biosignatures in either reacting solutions or the solid materials. We tested aqueous alteration of serpentinite in three groups of solutions: inorganic acids, organic acids created through abiotic processes (termed “abiotic–organics”), and organic acids created through biotic processes (termed “biotic acids”) over a range of temperatures relevant to conditions on Mars and Europa. A total of 48 batch reactor experiments were carried out at 0 °C, 22 °C, and 62 °C in 16 different acids at pH 2.6 over 28 days. Additional experiments were conducted in sulfuric acid solutions to assess aqueous alteration in sulfate-rich environments. These results show that biotic acids accelerate serpentinite dissolution compared to the control inorganic acid, whereas abiotic–organic acids have little or no effect. Sulfuric acid enhances serpentinite dissolution over nitric acid. Secondary precipitates found in the presence of biotic acids were consistently enhanced in Mn, Ti, and W. We propose that these preferentially released elements and secondary minerals may be potential biosignatures. We also show that the release of the rock-forming elements Mg and Si is correlated with stability constants for the metal–acid aqueous complex, providing a possible mechanistic interpretation of the observed results.
A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).
A Novel NLP-Driven Dashboard for Interactive CyberAttacks Tweet Classification and Visualization

The pervasive reach of social media like the X platform, formerly known as Twitter, offers unique opportunities for real-time analysis of cyberattack developments. By parsing and classifying tweets related to cyberattacks, we can glean valuable insights into their type, location, impact, and potential mitigation strategies. However, with millions of daily tweets, manual analysis is inefficient and time-consuming. This paper proposes an interactive and automated dashboard powered by natural language processing to effectively address this challenge. First, we created the CybAttT dataset, which contains 36,071 manually labeled English cyberattack tweets. We experimented with different classification algorithms. Following that, the best model was deployed and integrated into the streaming pipeline for real-time classification. This dynamic dashboard makes use of four different visualization formats: a geographical map, a data table, informative tiles, and a bar chart. Users can readily access crucial information about attacks, including location, timing, and perpetrators, enabling a swift response and mitigation efforts. Our experimental results demonstrated the dashboard’s promising visualization capabilities, highlighting its potential as a valuable tool for organizations and individuals seeking an intuitive and comprehensive overview of cyberattack events.
Using Super-Resolution for Enhancing Visual Perception and Segmentation Performance in Veterinary Cytology

The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional contribution was the development of a novel dataset aimed at improving imaging quality in the presence of inaccurate focus. Our experimental results demonstrate that the integration of SR techniques into the segmentation pipeline can lead to a significant improvement of up to 25% in the mean average precision (mAP) metric. These findings suggest that leveraging SR architectures holds great promise for advancing the state-of-the-art in cytology image analysis.