Reviewerly/exHarmony huggingface dataset:
Towards the Integration of Emerging Technologies as Support for the Teaching and Learning Model in Higher Education

The pandemic caused by COVID-19 impacted the entire world, but the significant challenges to be faced during this crisis opened an opportunity for organizations to evolve toward a digital transformation. Educational institutions were a concrete example of the use of technologies, which were abruptly incorporated into the teaching–learning model. Although this initiative was initially a challenge for teachers and students, it has now become a tool for new innovative teaching models, such as hybrid, online, and flexible models. The impact of technology used in education has been beneficial due to emerging technologies (virtual reality, augmented reality, games, web applications, mobile applications, etc.), which have served as tools to facilitate and motivate studying. These educational trends contribute directly to the fourth Sustainable Development Goal (SDG). This research analyzes whether the use of a web application, as a support in the educational model, can make students better understand the subjects of network infrastructure and be more efficient when configuring equipment in a data network. Therefore, this research is based on the design of an educational web application based on Python libraries, which allows the configuration of networking equipment based on the concept of network automation with the application of a graphical user interface (GUI). The web application can be deployed with communication equipment or in conjunction with the GNS3 simulator. This versatility allows this web tool to be applied to the teaching of network equipment configuration in any mode of study (classroom, online, hybrid, or flexible). The results obtained in this research are encouraging and open the way for the implementation of network automation and Python libraries for educational applications that can be important tools within the teaching and learning models of higher education.
Efficient text-image semantic search: A multi-modal vision-language approach for fashion retrieval

In this paper, we address the problem of multi-modal retrieval of fashion products. State-of-the-art (SOTA) works proposed in literature use vision-and-language transformers to assign similarity scores to joint text-image pairs, then used for sorting the results during a retrieval phase. However, this approach is inefficient since it requires coupling a query with every record in the dataset and computing a forward pass for each sample at runtime, precluding scalability to large-scale datasets. We thus propose a solution that overcomes the above limitation by combining transformers and deep metric learning to create a latent space where texts and images are separately embedded, and their spatial proximity translates into semantic similarity. Our architecture does not use convolutional neural networks to process images, allowing us to test different levels of image-processing details and metric learning losses. We vastly improve retrieval accuracy results on the FashionGen benchmark (+18.71% and +9.22% [email protected] on Image-to-Text and Text-to-Image, respectively) while being up to 512x faster. Finally, we analyze the speed-up obtainable by different approximate nearest neighbor retrieval strategies—an optimization precluded to current SOTA contributions. We release our solution as a web application available at https://disi-unibo-nlp.github.io/projects/fashion_retrieval/.
Giving Historical Photographs a New Perspective: Introducing Camera Orientation Parameters as New Metadata in a Large-Scale 4D Application

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.
DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms

Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa, a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases.DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs.The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0.95 and 0.89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results.To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies.
Waste Detection System Based on Data Augmentation and YOLO_EC

The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection.
Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency

Optical and Synthetic Aperture Radar (SAR) images are highly complementary, and their registrations are a fundamental task for other remote sensing applications. Traditional feature-matching algorithms fail to solve the significant nonlinear radiation difference (NRD) caused by different sensors. To address this problem, a robust registration algorithm with the multi-scale orientated map of phase congruency (MSPCO) is proposed. First, a nonlinear diffusion scale space is established to obtain the scale invariance of feature points. Compared with the linear Gaussian scale space, the nonlinear diffusion scale space can better preserve the edge and texture information. Second, to ensure the quantity and repeatability of features, corner points and edge points are detected on the moment map of phase congruency, respectively, which is the foundation to the next feature matching. Third, the MSPCO descriptor is constructed via the orientation of phase congruency (PCO). PCO is highly robust to NRD, and the different scales of PCOs enhance the robustness of the descriptor. Finally, a feature-matching strategy based on an effective scale ratio is proposed, which reduces the number of comparisons among features and improves computational efficiency. The experimental results show that the proposed method is better than the existing feature-based methods in terms of the number of correct matches and registration accuracy. The registration accuracy is only inferior to that of the most advanced template matching method, and the accuracy difference is within 0.3 pixels, which fully demonstrates the robustness and accuracy of the proposed method in optical and SAR image registration.
RareAnom: A Benchmark Video Dataset for Rare Type Anomalies

Existing video anomaly detection methods and datasets suffer from restricted anomaly categories containing single-source (CCTV) videos recorded in controlled environment, inadequate annotations, and lack of adequate supervision. To mitigate these problems, we introduce a new dataset (RareAnom) containing 17 rare types of real-world anomalies (2200 videos) recorded using multiple sources (e.g., CCTV, handheld cameras, dash-cams, and mobile phones) with rich temporal annotations. A new fully unsupervised anomaly detection and classification method has been proposed. It has three stages: training of a 3D Convolution Autoencoder using pseudo-labelled video segments, anomaly detection using latent features, and classification. Unlike the existing datasets, we have benchmarked RareAnom using three levels of supervision: fully, weakly, and unsupervised. It has been compared with UCF-Crime and XD-Violence datasets. The proposed anomaly detection and classification method beats the latest unsupervised methods by 4.49%, 8.66%, and 6.77% on RareAnom, UCF-Crime, and XD-violence datasets, respectively.
Efficient Reversible Data Hiding Using Two-Dimensional Pixel Clustering

Pixel clustering is a technique of content-adaptive data embedding in the area of high-performance reversible data hiding (RDH). Using pixel clustering, the pixels in a cover image can be classified into different groups based on a single factor, which is usually the local complexity. Since finer pixel clustering seems to improve the embedding performance, in this manuscript, we propose using two factors for two-dimensional pixel clustering to develop high-performance RDH. Firstly, in addition to the local complexity, a novel factor was designed as the second factor for pixel clustering. Specifically, the proposed factor was defined using the rotation-invariant code derived from pixel relationships in the four-neighborhood. Then, pixels were allocated to the two-dimensional clusters based on the two clustering factors, and cluster-based pixel prediction was realized. As a result, two-dimensional prediction-error histograms (2D-PEHs) were constructed, and performance optimization was based on the selection of expansion bins from the 2D-PEHs. Next, an algorithm for fast expansion-bin selection was introduced to reduce the time complexity. Lastly, data embedding was realized using the technique of prediction-error expansion according to the optimally selected expansion bins. Extensive experiments show that the embedding performance was significantly enhanced, particularly in terms of improved image quality and reduced time complexity, and embedding capacity also moderately improved.
Focalized contrastive view-invariant learning for self-supervised skeleton-based action recognition

Learning view-invariant representation is a key to improving feature discrimination power for skeleton-based action recognition. Existing approaches cannot effectively remove the impact of viewpoint due to the implicit view-dependent representations. In this work, we propose a self-supervised framework called Focalized Contrastive View-invariant Learning (FoCoViL), which significantly suppresses the view-specific information on the representation space where the viewpoints are coarsely aligned. By maximizing mutual information with an effective contrastive loss between multi-view sample pairs, FoCoViL associates actions with common view-invariant properties and simultaneously separates the dissimilar ones. We further propose an adaptive focalization method based on pairwise similarity to enhance contrastive learning for a clearer cluster boundary in the learned space. Different from many existing self-supervised representation learning work that rely heavily on supervised classifiers, FoCoViL performs well on both unsupervised and supervised classifiers with superior recognition performance. Extensive experiments also show that the proposed contrastive-based focalization generates a more discriminative latent representation.
Blockchain-Driven Supply Chain Management and Open Innovation

Advanced networking systems among consumers, goods, and value imposing major challenges for industries. Which comprises a change in consumers’ outlooks and forces on producing low-cost merchandise, arrangement, and supply chain. These changes demand advancements concerning businesses to adapt to modern system by encouraging and embracing advanced and innovative solutions for the challenges. Which highlights the overall modification of industries which is described as ‘industries 4.0′, which enforces the need for digital technologies, networking in industries, and associated practices. The blockchain technological domain is strongly incorporated by industries over the years. The usefulness of blockchain is not only accepted for the monetary sector but at the same time proven highly effective for supply chain management as well. Blockchain is an advanced technological domain that refines overall efficacy, transparency, and unambiguity. Currently, logistics and supply change are highly persuaded by the innovativeness, latest technological aspects, change in processes, and competitively that enhance challenges, which demands change and applying technological aspects for the modern supply chain as a multilevel system.
Robust UKF Relative Positioning Approach for Tightly Coupled Vehicle Ad Hoc Networks Based on Adaptive M-Estimation

Huber M-estimation, as an estimation method based on mixed norm as cost function, provides an effective method for robust filtering to deal with measurement outliers. Based on the statistical linear regression model approximating nonlinear measurement model, an M-estimation algorithm is used to realize measurement update of states. However, when the measurement noise pollution rate is high, the filtering performance will be seriously degraded. In order to further improve the actual performance of data fusion under abnormal measurement noise, a vehicle cooperative positioning scheme based on adaptive M-estimation robust unscented Kalman filter (AMRUKF) was proposed. This algorithm combines Huber’s linear regression problem with covariance matching method and calculates the adaptive matrix through the innovation covariance estimator based on fading memory index weighting to adaptively adjust the measurement noise covariance used in the Huber M-estimation method. The analytical and experimental results of tightly coupled vehicle relative positioning show that the proposed AMRUKF method can effectively improve the robustness and accuracy of relative positioning and provide a practical control scheme for vehicle cooperative positioning.
Enhancing Semantic Code Search with Deep Graph Matching

The job of discovering appropriate code snippets against a natural language query is an important task for software developers. Appropriate code retrieval increases software productivity and quality as well. In contrast to traditional information retrieval techniques, code search necessitates bridging the semantic breach between programming languages and natural language to search code fragments. Deep neural networks for search codes have recently been a hot topic in research. The standard neural code quest approaches present source code and query in the form of text as independent embedding, then calculate the semantic similarity between them using vector distance (e.g., using cosine similarity). Although recent research utilized query and code snippets during code search, it overlooked the contained rich semantic information and deep structural features between them. In this study, we are also dealing with the problem of code search by providing a deep neural solution that facilitates software developers during software development. Our proposed model effectively used neural graph matching and a searching approach for semantic code retrieval. It first converts both query and code fragments in graph format and then the semantic matching module is used to facilitate the process of matching that will retrieve the best-matched code snippets. It not only exploits the enriched semantic meanings and features, but it also uses the cross-attention mechanism to learn the fine-grained similarity that exists between query and code. The proposed model’s evaluation is done using the Codesearchnet dataset with six representative programming languages. It provides comparatively good results as compared to existing baselines. It enables users to find required code snippets, and ranking is used to retrieve top 10 results. The accuracy of the proposed system is approximately 97%.
CBASH: A CareBot-Assisted Smart Home System Architecture to Support Aging-in-Place

Powered by IoT technologies, smart home systems effectively support aging-in-place. However, they have some limitations in comprehensive event perception and timely appropriate action. Homecare robot systems have been proven to be effective in homecare task executions but still have significant technological challenges. To address this problem, this paper proposes a smart home system architecture integrating a mobile robot with better event perception and task execution performance. To support the proposed system architecture, an ontology of the smart home is built to address the data heterogeneity issue. Then an event perception method is built upon multi-data integration. To perform complex tasks, this work implements an improved genetic algorithm for task planning. Finally, simulations and physical experiments are conducted to validate the feasibility of the proposed system architecture.
SysFlow: Toward a Programmable Zero Trust Framework for System Security

Zero Trust, as an emerging trend of cybersecurity paradigms in modern infrastructure (e.g., enterprise, cloud, edge, IoT, and 5G), is moving security defenses from static and perimeter-based control systems to focus on users and resources with no assumption of implicit trust. However, the current Zero Trust Architecture (ZTA) mainly focuses on the network security and lacks in-depth considerations on system-level security policies and abstractions, which leaves the realization of the principle incomplete. To bridge the gap, we propose an innovative <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">programmable</i> system security framework called SYSFLOW to enable unified, dynamic, and fine-grained Zero Trust security control for system resources. SYSFLOW introduces a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system flow</i> abstraction to model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system activities</i> across the entire infrastructure, and provides a system-level data plane and control plane separation and abstraction. The new logically centralized controller accommodates a unified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">programmable</i> Policy Decision Point (PDP) that acquires a holistic view of system behaviors for controlling system resource accesses by translated programmable security policies into system flow rules. The SYSFLOW data plane, acting as Policy Enforcement Point (PEP), enforces translated system flow rules, which can be updated dynamically and facilitate fine-grained responsive actions. Our extensive evaluations demonstrate the effectiveness and scalability of SYSFLOW, which addresses the security issues in various scenarios with a minor performance overhead.
Semi-MapGen: Translation of Remote Sensing Image Into Map via Semisupervised Adversarial Learning

Online maps play an essential role in modern life. The convenience of acquiring remote sensing images provides reliable geographic information sources for the compilation of online maps. Some existing works have used the idea of domain mapping to translate remote sensing images into maps directly, which is of great prospect for application. However, many of the current remote sensing image-to-map translation works are performed in an unsupervised manner that would lead to problems such as distortion and local detail inaccuracy. Although the fully-supervised method is effective, it requires plenty of paired as well as matched data for training. Paired remote sensing images and maps with consistent spatial locations can be easily accessed through online map services, whereas many pairs of samples in which some geographic element information is not accurately and completely matched. Supervised learning-based translation models are often confused by these unmatched data. Accurate and complete matched data has to be selected deliberately by humans, and the manual selection process is time-consuming and laborious, which brings new challenges. Therefore, we propose a novel remote sensing image-to-map translation model named Semi-MapGen based on semi-supervised generative adversarial networks (GAN), which requires only a small set of accurate and complete matched data and plenty of unpaired data. In this model, we apply a knowledge extension-based learning strategy that can improve the accuracy of translated maps. In addition, we design Expansion loss and Channel-wise loss to learn the information from massive unpaired data in an unsupervised manner. Qualitative and quantitative experiment results on three datasets demonstrate that the proposed model outperforms state-of-the-art semi-supervised and supervised methods.