Reviewerly/exHarmony huggingface dataset:
The maximum entropy principle for compositional data

Compositional systems, represented as parts of some whole, are ubiquitous. They encompass the abundances of proteins in a cell, the distribution of organisms in nature, and the stoichiometry of the most basic chemical reactions. Thus, a central goal is to understand how such processes emerge from the behaviors of their components and their pairwise interactions. Such a study, however, is challenging for two key reasons. Firstly, such systems are complex and depend, often stochastically, on their constituent parts. Secondly, the data lie on a simplex which influences their correlations.To resolve both of these issues, we provide a general and data-driven modeling tool for compositional systems called Compositional Maximum Entropy (CME). By integrating the prior geometric structure of compositions with sample-specific information, CME infers the underlying multivariate relationships between the constituent components. We provide two proofs of principle. First, we measure the relative abundances of different bacteria and infer how they interact. Second, we show that our method outperforms a common alternative for the extraction of gene-gene interactions in triple-negative breast cancer.CME provides novel and biologically-intuitive insights and is promising as a comprehensive quantitative framework for compositional data.
Investigating digital skills among Russian higher education students

Learning in the digital age is a pervasive idea that encompasses all aspects of a person's life, including work and leisure. As a result of the development of new teaching and learning tools, an increasing number of students are acquiring knowledge on the Internet- connected to the Internet. Therefore, all citizens must develop digital literacy as a lifelong learning skill. Studies have been conducted on students' digital skills in higher education institutions. In this context, this study aimed to investigate the skills of college students. The participants were students from a university in the Kazan region of Russia who volunteered to participate. Three hundred and eighty students completed the questionnaire online. The scale consists of a total of 25 questions and six dimensions. Since our independent variables are binary values, we applied the Bayesian t-test. We obtained the values of the Bayes factor (BF10) for each dimension and the total scale. In general, students' digital skills are well-developed. However, it was found that creating and using digital information requires fewer skills than in other areas. The hypothesis that there is no difference based on student gender was supported to a higher degree but not to a very high degree. The hypothesis that there is no difference based on students' fields of study was supported to a greater extent, but only to a moderate extent.
Feasibility and Acceptance of Virtual Reality Reusable e- Resources Embedded in Healthcare Curricula

The use of Virtual Reality in healthcare education is still uncommon. Measuring the feasibility of such resources into formal training is critical for quality, satisfaction, and ultimately improved efficacy over other methods. The Co-creation of Virtual Reality reusable e-Resources for European Healthcare Education (CoViRR) team used a proven development framework to co-create and analyze the feasibility and acceptance of 3 Virtual Reality Reusable e-Resources (VRReRs). The co-created VRReRs have been evaluated on usability, user acceptance and pedagogical acceptability using several formal and ad-hoc instruments. The results demonstrated that co-created VRReRs were on-par with other IT products in terms of usability. Also, users appeared ready to accept reusable VR e-resources as useful skills training instruments. Qualitative evaluation revealed that the user base is ready to accept VRReRs but require high technical fidelity and human-centric interaction schemes, supporting seamless integration of user activities in the virtual world, without technical obstacles. Ultimately, CoViRR analysis on feasibility and acceptance of virtual reality reusable e-resources will act as an example by other higher education institutions and tutors in techniques and topics for effective resource creation.
Performance Comparison of Various YOLO Architectures on Object Detection of UAV Images

Today, the rapid development of deep learning offers an extraordinary opportunity to enhance the performance and efficiency of various industries, including business, the military, medicine, and transportation. Using deep learning algorithms in the transportation industry, for instance, makes UAVs vital and efficient in this industry. Current Unmanned Aerial Vehicles (UAVs) applications in transportation systems encourage the development of object detection methods to collect real-time traffic data using UAVs. Due to the versatility and portability of UAVs, particularly drones, individuals require systems that operate with UAVs to identify objects in real-time for military, safety observation, and protection. The culmination of the evolution of computer vision technology is the development of sophisticated algorithms centered on extensive training and testing datasets. This research aims to compare the performance of object detection of UAV images using various YOLO architectures. Tiny YOLOv3 and YOLOv5s models were implemented to extract the object’s features and classify them into the dataset’s multiple classes. This paper selected the VisDrone2019 dataset for its various object classes: pedestrian, person, bicycle, car, van, truck, tricycle, awning-tricycle, bus, and motor. Results demonstrated that YOLOv5s have acceptable precision and processing speed.
Deep-Separation Guided Progressive Reconstruction Network for Semantic Segmentation of Remote Sensing Images

The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RSIs of complex scenes is challenging, and (2) feature reconstruction for accurate segmentation is difficult. To solve these problems, we propose a deep-separation-guided progressive reconstruction network that achieves accurate RSI segmentation. First, we design a decoder comprising progressive reconstruction blocks capturing detailed features at various resolutions through multi-scale features obtained from various receptive fields to preserve accuracy during reconstruction. Subsequently, we propose a deep separation module that distinguishes various classes based on semantic features to use deep features to detect objects of different scales. Moreover, adjacent middle features are complemented during decoding to improve the segmentation performance. Extensive experimental results on two optical RSI datasets show that the proposed network outperforms 11 state-of-the-art methods.
Deep Learning and Entropy-Based Texture Features for Color Image Classification

In the domain of computer vision, entropy-defined as a measure of irregularity-has been proposed as an effective method for analyzing the texture of images. Several studies have shown that, with specific parameter tuning, entropy-based approaches achieve high accuracy in terms of classification results for texture images, when associated with machine learning classifiers. However, few entropy measures have been extended to studying color images. Moreover, the literature is missing comparative analyses of entropy-based and modern deep learning-based classification methods for RGB color images. In order to address this matter, we first propose a new entropy-based measure for RGB images based on a multivariate approach. This multivariate approach is a bi-dimensional extension of the methods that have been successfully applied to multivariate signals (unidimensional data). Then, we compare the classification results of this new approach with those obtained from several deep learning methods. The entropy-based method for RGB image classification that we propose leads to promising results. In future studies, the measure could be extended to study other color spaces as well.
Optimizing Face Recognition Inference with a Collaborative Edge–Cloud Network

The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge-cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge-cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge-cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.
On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction

Interest in chaotic time series prediction has grown in recent years due to its multiple applications in fields such as climate and health. In this work, we summarize the contribution of multiple works that use different machine learning (ML) methods to predict chaotic time series. It is highlighted that the challenge is predicting the larger horizon with low error, and for this task, the majority of authors use datasets generated by chaotic systems such as Lorenz, Rössler and Mackey–Glass. Among the classification and description of different machine learning methods, this work takes as a case study the Echo State Network (ESN) to show that its optimization can lead to enhance the prediction horizon of chaotic time series. Different optimization methods applied to different machine learning ones are given to appreciate that metaheuristics are a good option to optimize an ESN. In this manner, an ESN in closed-loop mode is optimized herein by applying Particle Swarm Optimization. The prediction results of the optimized ESN show an increase of about twice the number of steps ahead, thus highlighting the usefulness of performing an optimization to the hyperparameters of an ML method to increase the prediction horizon.
Estimation and Propagation: Scene Flow Prediction on Occluded Point Clouds

With the development of 3D sensors, estimating the point movements between two consecutive point clouds is becoming increasingly attractive. Using correlation-based deep neural networks, existing solutions achieve promising performance. However, these methods couple the non-occluded points and occluded points into the same cost volume and attempt to regress the scene flows of the occluded points from those invalid matching costs directly, which severely suppress the performance of the flow predictor. As an alternative to previous scene flow estimation methods, our method adopts a subnet to predict the occlusion mask and explicitly masks those occluded points, which ensures our flow predictor to focus on estimating the motion flows of non-occluded points from those valid matching costs. Moreover, we further improve the flow estimation performance by employing a local-adaptive cost volume, which can deal with the local structure dissimilarity induced by the sparse sampling of depth sensors (LiDAR). For occluded points, we design an uncertainties-truncated propagation network to propagate the flows of non-occluded to those occluded points. We demonstrate the effectiveness of the proposed method by quantitative and qualitative comparisons with recent baseline works on the FlyingThings3D dataset and the KITTI 2015 dataset, and our results surpass all competing methods by a large margin.
Explainable Information Retrieval: A Survey

Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.
Automatic Security Assessment of GitHub Actions Workflows

The demand for quick and reliable DevOps operations pushed distributors of repository platforms to implement workflows. Workflows allow automating code management operations directly on the repository hosting the software. However, this feature also introduces security issues that directly affect the repository, its content, and all the software supply chains in which the hosted code is involved in. Hence, an attack exploiting vulnerable workflows can affect disruptively large software ecosystems. To empirically assess the importance of this problem, in this paper, we focus on the de-facto main distributor (i.e., GitHub). We developed a security assessment methodology for GitHub Actions workflows, which are widely adopted in software supply chains. We implemented the methodology in a tool (GHAST) and applied it on 50 open-source projects. The experimental results are worrisome as they allowed identifying a total of 24,905 security issues (all reported to the corresponding stakeholders), thereby indicating that the problem is open and demands further research and investigation.
Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.
Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Underwater environments can be used to explore new resources that can be employed in the fields of medical science and energy resources. Humans are dependent on the valuable resources that exist beneath Earth's surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.
Gating PatternPyramid for diversified image style transfer

In diversified image style transfer tasks, the goal is to translate an everyday photograph to diverse stylized images conditioned on the style of an artwork. Although several style transfer methods have achieved certain diversity through noise injection, they still have two unsolved problems: (1) relatively limited diversity and (2) significant degradation of quality. In this work, we introduce an effective Gating PatternPyramid block (GPP) to resolve these issues by employing a well-designed multibranch architecture. The main idea is underpinned by a finding that different network architectures capture different style patterns from the same artwork. In addition, the GPP block is compatible with many feed-forward style transfer models and empowers them with the ability of generating diverse stylization results. To the best of our knowledge, this is the first style transfer method that achieves significant diversity without injecting random noise, which provides an inspiring perspective for diverse translation research. We perform qualitative and quantitative experiments, showing the effectiveness and superiority of our method against the state-of-the-art diversified style transfer methods.
Manifold code-mixing in computer-mediated communication: The use of English in Dutch youths’ informal online writing

The pervasiveness of the English language in society and education in the Netherlands, as well as its status as online lingua franca, has caused concerns. English is a manifest aspect of oral youth language, as reflected in online written messages, but has in no way replaced the Dutch language. This paper presents a large-scale corpus analysis of computer-mediated communication by Dutch youths. These social media messages (392,169 words in total) were studied for the presence of code-mixing with English, in terms of amount and manner. They contained 7528 English elements: (parts of) words, interjections, textisms (typical of ‘digi-talk’), phrases, and sentences. We argue that the concept of ‘manifold code-mixing’, consisting of four pathways – discourse framing, insertion, alternation, and integration – is necessary to truly comprehend the complexity and social meaning of code-mixing. These pathways relate to the SUPER-functions of textisms (speechlike, understandable, playful, expressive, reduced) and reveal Dutch youths' high proficiency in English.
Focal DETR: Target-Aware Token Design for Transformer-Based Object Detection

In this paper, we propose a novel target-aware token design for transformer-based object detection. To tackle the target attribute diffusion challenge of transformer-based object detection, we propose two key components in the new target-aware token design mechanism. Firstly, we propose a target-aware sampling module, which forces the sampling patterns to converge inside the target region and obtain its representative encoded features. More specifically, a set of four sampling patterns are designed, including small and large patterns, which focus on the detailed and overall characteristics of a target, respectively, as well as the vertical and horizontal patterns, which handle the object’s directional structures. Secondly, we propose a target-aware key-value matrix. This is a unified, learnable, feature-embedding matrix which is directly weighted on the feature map to reduce the interference of non-target regions. With such a new design, we propose a new variant of the transformer-based object-detection model, called Focal DETR, which achieves superior performance over the state-of-the-art transformer-based object-detection models on the COCO object-detection benchmark dataset. Experimental results demonstrate that our Focal DETR achieves a 44.7 AP in the coco2017 test set, which is 2.7 AP and 0.9 AP higher than the DETR and deformable DETR using the same training strategy and the same feature-extraction network.
DWSC-YOLO: A Lightweight Ship Detector of SAR Images Based on Deep Learning

In the field of ship detection, most research on lightweight models comes at the expense of accuracy. This study aims to address this challenge through a deep learning approach and proposes a model DWSC-YOLO, which is inspired by YOLOv5 and MobileNetV3. The model employs a lightweight framework as the backbone network, and the activation function and attention mechanism are researched. Furthermore, to improve the accuracy of the convolutional neural network and reduce loss, heterogeneous convolutions are added to the network. Three independent experiments were carried out using the proposed model. The experiment results show that the model can achieve excellent detection results with a small number of computational resources and costs. The mAP of the model is 99.5%, the same as YOLOv5, but the volume is 2.37 M, which is 79.8% less.
Enhancement of Question Answering System Accuracy via Transfer Learning and BERT

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA.