Reviewerly/exHarmony huggingface dataset:
Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents ( Ophionyssus natricis , Blastocystis sp , Oxiurdo egg , Rhytidoides similis , Strongyloides , Taenia ) or the absence of such parasites from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through MobileNet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 94.26% across the seven classes (i.e., six parasitic agents and the absence of parasites), which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.
Bias and Unfairness of Collaborative Filtering Based Recommender Systems in MovieLens Dataset

Recommender Systems have become one of the most important tools for streaming and marketplace systems in recent years. Their increased use has revealed clear bias and unfairness against minorities and underrepresented groups. This paper seeks the origin of these biases and unfairness. To this end, it analyzes the demographic characteristics of a gold standard dataset and its prediction performance when used in a multitude of Recommender Systems. In addition, this paper proposes Soft Matrix Factorization (SoftMF), which tries to balance the predictions of different types of users to reduce the present inequality. The experimental results show that those biases and unfairness are not introduced by the different recommendation models and that they come from the socio-psychological and demographic characteristics of the used dataset.
TripJudge

Robust test collections are crucial for Information Retrieval research. Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, however these tasks often lack a reliable test collection with human-annotated relevance assessments following the Cranfield paradigm. In the medical domain, the TripClick collection was recently proposed, which contains click log data from the Trip search engine and includes two click-based test sets. However the clicks are biased to the retrieval model used, which remains unknown, and a previous study shows that the test sets have a low judgement coverage for the Top-10 results of lexical and neural retrieval models. In this paper we present the novel, relevance judgement test collection TripJudge for TripClick health retrieval. We collect relevance judgements in an annotation campaign and ensure the quality and reusability of TripJudge by a variety of ranking methods for pool creation, by multiple judgements per query-document pair and by an at least moderate inter-annotator agreement. We compare system evaluation with TripJudge and TripClick and find that that click and judgement-based evaluation can lead to substantially different system rankings.
Could engineers work in the metaverse?

This article discusses the metaverse as a 3D immersive professional world for engineers and technologists to work together in real-time. Industry applications include automobile and aerospace manufacturing and production processes, medical/surgical technology and training, and manufacturing, product design and product development in general. Topics covered include virtual collaboration and 3D simulation, physical prototype designs, virtual factories, digital twins, virtual robotics, virtual design testing, implant and prothesis design, 3D printing (including organs), advanced software, haptic investment, augmented reality devices, virtual studio tools, XR (extended reality) and VR, computational modelling and immersive design tools. It also looks at the challenges of the engineering metaverse including the integration of different software, connecting engineering tools and processes and security and privacy issues.
Reasoning on conflicting information: An empirical study of Formal Argumentation

According to the Argumentative Theory, human reasoning has an argumentative function, which consists of devising and evaluating arguments for and against various claims. It is however unclear how humans handle conflicting claims they face in everyday life (i.e., "Bob is telling me that Alice is at the library" vs. "Charles is telling me that Alice is at home"). We here investigate human argumentative reasoning in the light of Formal Argumentation, a research field that develops formal methods to give a normative account of argumentation and reasoning about conflicting information. In Formal Argumentation, multiple argumentation semantics that allow selecting sets of jointly acceptable arguments have been proposed. Nonetheless, it is unclear which of these semantics predicts best how humans evaluate the acceptability of conflicting arguments. We conducted an empirical study in which 130 young adults judged natural language arguments. We instructed them to draw the attack relation between the given arguments and to evaluate the acceptability of each of these arguments. Our results show that human judgments on the existence and directionality of attacks between the arguments conform to theoretical predictions from Formal Argumentation. We further found out that some less well-known argumentation semantics predicted human evaluation better than the most well-known semantics. These findings support the cognitive plausibility of variants of Formal Argumentation and bring new insights into reasoning about conflicting information.
High-quality Task Division for Large-scale Entity Alignment

Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be applied to large-scale real-life KGs due to their excessive consumption of GPU memory and time. One promising solution is to divide a large EA task into several subtasks such that each subtask only needs to match two small subgraphs of the original KGs. However, it is challenging to divide the EA task without losing effectiveness. Existing methods display low coverage of potential mappings, insufficient evidence in context graphs, and largely differing subtask sizes. In this work, we design the DivEA framework for large-scale EA with high-quality task division. To include in the EA subtasks a high proportion of the potential mappings originally present in the large EA task, we devise a counterpart discovery method that exploits the locality principle of the EA task and the power of trained EA models. Unique to our counterpart discovery method is the explicit modelling of the chance of a potential mapping. We also introduce an evidence passing mechanism to quantify the informativeness of context entities and find the most informative context graphs with flexible control of the subtask size. Extensive experiments show that DivEA achieves higher EA performance than alternative state-of-the-art solutions.
Integrated Guidance and Gimbal Control for Coverage Planning With Visibility Constraints

Coverage path planning with unmanned aerial vehicles (UAVs) is a core task for many services and applications including search and rescue, precision agriculture, infrastructure inspection and surveillance. This work proposes an integrated guidance and gimbal control coverage path planning (CPP) approach, in which the mobility and gimbal inputs of an autonomous UAV agent are jointly controlled and optimized to achieve full coverage of a given object of interest, according to a specified set of optimality criteria. The proposed approach uses a set of visibility constraints to integrate the physical behavior of sensor signals (i.e., camera-rays) into the coverage planning process, thus generating optimized coverage trajectories that take into account which parts of the scene are visible through the agent's camera at any point in time. The integrated guidance and gimbal control CPP problem is posed in this work as a constrained optimal control problem which is then solved using mixed integer programming (MIP) optimization. Extensive numerical experiments demonstrate the effectiveness of the proposed approach.
Photovoltaic/Wind Hybrid System Power Stations to Produce Electricity in Adrar Region

This current work, which is implemented on hybrid power stations to produce electricity and use it to cover the increasing demand for electricity in public lighting projects, so that this study is carried out in the Adrar region. To achieve this goal, in the first part, we introduced the components of the hybrid photovoltaic system with wind generators to produce electricity with renewable energy sources that are not harmful to the ocean. After that, we simulated the system using the homer pro program, which allows us to take a comprehensive view in terms of the natural data, the capacity and cost of energy production, as well as the characteristics of each component that enters into the composition of the hybrid system. Production capacity and cost are discussed. The results obtained in the framework of this study showed that this Adrar region is considered an energy treasure as it contains very significant climatic data that can be used in converting solar energy and wind energy into electrical energy. Also, the Homer program confirmed to us through simulation results that the system the considered hybrid is successful.
Pigeon-inspired optimisation-based cooperative target searching for multi-UAV in uncertain environment

In this paper, the multi-UAV cooperative target searching problem is investigated and a close loop path planning method is developed for UAVs in uncertain environment. The proposed method includes two consecutive parts, the multi-UAV cooperative target search algorithm developed based on cooperative pigeon-inspired optimisation (CPIO) and the base returning algorithm for each UAV based on artificial potential field (APF) method. Firstly, a concerned regional environment and the initial search probability map models are established. Then, by applying the rolling prediction strategy, the cooperative target search paths for multiple UAVs are generated by utilising the proposed CPIO. With this method, UAVs can reinforce target search in the key areas in a cooperative way and avoid flying into the no-fly zones. In the meanwhile, the Bayesian theorem is used to constantly update the search probability map in each search step. Finally, at the end of the target search phase, an optimised safe path is generated for each UAV returning back to its original by using the APF method. Simulations are performed and the results demonstrate that the proposed approach is effective for multiple UAVs carrying out cooperative target search task in a complex environment.
Deep Idempotent Network for Efficient Single Image Blind Deblurring

Single image blind deblurring is highly ill-posed as neither the latent sharp image nor the blur kernel is known. Even though considerable progress has been made, several major difficulties remain for blind deblurring, including the trade-off between high-performance deblurring and real-time processing. Besides, we observe that current single image blind deblurring networks cannot further improve or stabilize the performance but significantly degrades the performance when re-deblurring is repeatedly applied. This implies the limitation of these networks in modeling an ideal deblurring process. In this work, we make two contributions to tackle the above difficulties: (1) We introduce the idempotent constraint into the deblurring framework and present a deep idempotent network to achieve improved blind non-uniform deblurring performance with stable re-deblurring. (2) We propose a simple yet efficient deblurring network with lightweight encoder-decoder units and a recurrent structure that can deblur images in a progressive residual fashion. Extensive experiments on synthetic and realistic datasets prove the superiority of our proposed framework. Remarkably, our proposed network is nearly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.5\times $ </tex-math></inline-formula> smaller and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.4\times $ </tex-math></inline-formula> faster than the state-of-the-art while achieving comparable high performance.
Towards Federated Learning against Noisy Labels via Local Self-Regularization

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labeled data require expensive human intelligence and efforts, data with incorrect labels (called noisy labels) are ubiquitous in reality, which inevitably cause performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the purpose of alleviating performance degradation yielded by noisy labels meanwhile guaranteeing data privacy. Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. Experimental results demonstrate that our proposed method can achieve notable resistance against noisy labels in various noise levels on three benchmark datasets. In addition, we integrate our method with existing state-of-the-arts and achieve superior performance on the real-world dataset Clothing1M. The code is available at https://github.com/Sprinter1999/FedLSR.
Challenges in Virtual Reality Training for CRBN Events

The re-emergence of chemical, biological, radioactive, and nuclear (CBRN) threats as a key area of focus for military (as well as civilian) actors, paired with the early stage of CBRN VR training, create a strong opportunity for future research. Improvement in-game engine technology and Virtual Reality hard and software can improve CBRN training and simulation for military and civilian responders to CBRN events. Therefore, in this work, we discussed the challenges of developing a European virtual reality-based CBRN training. By standardizing CBRN training on a European Level interoperability between different actors (military and civilian) and European nationalities shall be increased. We presented the main cornerstones for a VR CBRN training that shall be tackled in the VERTIgO project: (1) the Exercise Simulation Platform (2) Scenario Creator, and (3) a CBRN VR Mask.
The influence of social media affordances on drug dealer posting behavior across multiple social networking sites (SNS)

Social media has been documented as widely used for initiating online sales of illicit drugs such as opioids. However, not much is known about how affordances of social networking sites (SNS) influence how dealers advertise their supplies. To explore this topic, social media posts across 5 online platforms (Google Groups, Instagram, Twitter, Reddit, and Tumblr) were collected during 2020–2021. Biterm topic modeling (BTM) was used to identify signal posts specifically associated with the illegal online sale of opioids from drug selling social media accounts. Posts were analyzed by conducting a word count for drug names or slang terms associated with 5 categories: Opioids, Non-Opioid Prescription Controlled Drugs (e.g., Xanax, Valium), Other Illicit Drugs (e.g., Meth, Cocaine), Synthetic Opioids (Fentanyl), and Synthetic Marijuana. Number of mentions per post were calculated for each drug category and compared across platforms. Identifiers (e.g., publicly available email address) associated with posts were used to track dealers across different user accounts. Platforms with affordances for longer messages (e.g., Tumblr) had higher concentrations of drug mentions per post and higher variety of drug type mentions compared to SNS platforms Instagram and Twitter. Google Groups had the most drug mentions per post across all 5 categories. Additionally, each identifier was associated with multiple user accounts on a given platform. These results indicate that affordances of anonymity and message length may influence how drug dealers advertise their services on different platforms. Public health implications and strategies to counteract drug dealers and illicit drug diversion via SNS are also discussed.
Learning the Effect of Registration Hyperparameters with HyperMorph

We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, modality, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at &lt;a href='http://hypermorph.voxelmorph.net'&gt;http://hypermorph.voxelmorph.net&lt;/a&gt;
Beyond “fairness”

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As Artificial Intelligence—in education and beyond—may contribute to inequitable outcomes for marginalized communities, approaches have been developed to evaluate and mitigate AI’s harmful impacts. However, we argue in this chapter that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the systemic inequities that educational AI systems (re)produce. We draw on lenses of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely studied and adopted categories of educational AI; and we explore how they are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models’ performance. We close with alternative visions for a more equitable future for educational AI.
A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval

Every hour, huge amounts of visual contents are posted on social media and user-generated content platforms. To find relevant videos by means of a natural language query, text-video retrieval methods have received increased attention over the past few years. Data augmentation techniques were introduced to increase the performance on unseen test examples by creating new training samples with the application of semantics-preserving techniques, such as color space or geometric transformations on images. Yet, these techniques are usually applied on raw data, leading to more resource-demanding solutions and also requiring the shareability of the raw data, which may not always be true, e.g. copyright issues with clips from movies or TV series. To address this shortcoming, we propose a multimodal data augmentation technique which works in the feature space and creates new videos and captions by mixing semantically similar samples. We experiment our solution on a large scale public dataset, EPIC-Kitchens-100, and achieve considerable improvements over a baseline method, improved state-of-the-art performance, while at the same time performing multiple ablation studies. We release code and pretrained models on Github at https://github.com/aranciokov/FSMMDA_VideoRetrieval.
Efficient 3D measurement of a HDR surface based on adaptive fringe projection

3D measurement methods of a high-dynamic-range (HDR) surface based on adaptive fringe projection have aroused extensive research interest. They tend to pixel-wise adjust the fringe projection intensity to ensure full-field phase quality in light or dark regions, which has two problems: (1) traditional image intensity-based temporal phase unwrapping (TPU) is susceptible to noise in dark regions, and (2) it is time-consuming to project orthogonal fringe patterns for coordinate mapping and 3D reconstruction. Aiming to address these issues, we present an efficient adaptive fringe projection method where misaligned Gray code patterns are adopted to remove the phase error induced by low-frequency fringe patterns. Compared with traditional image intensity-based TPU, misaligned Gray-code-based TPU provides a better noise-suppression effect in dark regions, as Gray codes are generally better preserved than image intensity. Moreover, the images captured in the coordinate mapping process are shared for optimal projection intensity calculation and 3D reconstruction to reduce the number of total projection patterns, thus improving measurement efficiency. Extensive contrast experiments are conducted to demonstrate that the proposed method retrieves the 3D shapes of micro-scale HDR surfaces with high accuracy and a minimum number of projection patterns on the premise of high measurement integrity.