Reviewerly/exHarmony huggingface dataset:
Robust Fourier Watermarking for Print-Cam Process using Convolutional Neural Networks

Nowadays, with the development of technology and the popularity of smartphones, dealing with captured documents has become an inevitable necessity. The print-cam process produces serious attacks during capturing images, especially perspective distortions. In this paper, we have improved the Fourier watermarking method proposed by Gourrame et al. [1] for Print-Cam attacks which is based on a specific correction preprocess. This correction consists of a frame-based perspective rectification for the captured images. Effectively, Instead of applying a classical Hough detecting-based technique to restore the geometric distortions, we have implemented a new deep learning neural architecture that enables us to segment the image more accurately. According to the tests carried out, this architecture allowed us to improve the robustness of the global watermarking method.
Not all edges are peers: Accurate structure-aware graph pooling networks

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in graph-related tasks. For graph classification task, an elaborated pooling operator is vital for learning graph-level representations. Most pooling operators derived from existing GNNs generate a coarsen graph through ordering the nodes and selecting some top-ranked ones. However, these methods fail to explore the fundamental elements other than nodes in graphs, which may not efficiently utilize the structure information. Besides, all edges attached to the low-ranked nodes are discarded, which destroys graphs’ connectivity and loses information. Moreover, the selected nodes tend to concentrate on some substructures while overlooking information in others. To address these challenges, we propose a novel pooling operator called Accurate Structure-Aware Graph Pooling (ASPool), which can be integrated into various GNNs to learn graph-level representation. Specifically, ASPool adaptively retains a subset of edges to calibrate the graph structure and learns the abstracted representations, wherein all the edges are viewed as non-peers instead of simply connecting nodes. To preserve the graph’s connectivity, we further introduce the selection strategy considering both top-ranked nodes and dropped edges. Additionally, ASPool performs a two-stage calculation process to promise that the sampled nodes are distributed throughout the graph. Experiment results on 9 widely used benchmarks show that ASPool achieves superior performance over the state-of-the-art graph representation learning methods.
Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

Traversability prediction enables safe and efficient autonomous rover operation on deformable planetary surfaces. Revealing spatial distribution from terrain geometry to rover slip behavior is key to assessing prospective traversability, but is hindered by insufficient in situ measurements on hazardous states due to conservative rover traverses. To achieve a more accurate prediction, this letter proposes a framework that actively learns latent traversability by exploring informative terrain under the constraints of stochastic rover slip. With a Gaussian process (GP) modeling the spatial distribution, we devise an iterative two-stage framework that gradually refines the model estimation, combining risk-aware informative path planning and GP updates by taking in situ measurements. The path planning stage employs our designed sampling-based algorithm to generate informative trajectories with fault-tolerant risk inference, while the GP is cautiously updated with traverse data to avoid rover immobilization. Chance constraint formulation is exploited in the framework to infer the stochastic reachability of informative regions. Through GP estimates reducing uncertainty, the algorithm incrementally reaches informative yet hazardous states along feasible trajectories. Simulation studies in rough terrain environments demonstrate that the proposed framework gathers informative traverse data while averting rover stuck situations to estimate the latent traversability model.
Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery

Semantic segmentation is the task of clustering together parts of an image that belong to the same object class. Semantic segmentation of webpages is important for inferring contextual information from the webpage. This study examines and compares deep learning methods for classifying webpages based on imagery that is obscured by semantic segmentation. Fully convolutional neural network architectures (UNet and FCN-8) with defined hyperparameters and loss functions are used to demonstrate how they can support an efficient method of this type of classification scenario in custom-prepared webpage imagery data that are labeled multi-class and semantically segmented masks using HTML elements such as paragraph text, images, logos, and menus. Using the proposed Seg-UNet model achieved the best accuracy of 95%. A comparison with various optimizer functions demonstrates the overall efficacy of the proposed semantic segmentation approach.
Linking the Center for International Blood and Marrow Transplant Research Registry to the California Cancer Registry and California Hospital Patient Discharge Data

Advances in hematopoietic cell transplantation (HCT) have substantially improved patient survival, increasing the importance of studying outcomes and long-term adverse effects in the rapidly growing population of HCT survivors. Large-scale registry data from the Center for International Blood and Marrow Transplant Research (CIBMTR) are a valuable resource for studying mortality and late effects after HCT, providing detailed data reported by HCT centers on transplantation-related factors and key outcomes. This study was conducted to evaluate the robustness of CIBMTR outcome data and assess health-related outcomes and healthcare utilization among HCT recipients. We linked data from the CIBMTR for California residents with data from the population-based California Cancer Registry (CCR) and hospitalization information from the California Patient Discharge Database (PDD). In this retrospective cohort study, probabilistic and deterministic record linkage used key patient identifiers, such as Social Security number, ZIP code, sex, birth date, hematologic malignancy type and diagnosis date, and HCT type and date. Among 22,733 patients registered with the CIBMTR who underwent autologous or allogeneic HCT for hematologic malignancy between 1991 and 2016, 89.0% were matched to the CCR and/or PDD (n = 17,707 [77.9%] for both, n = 1179 [5.2%] for the CCR only, and n = 1342 [5.9%] for the PDD only). Unmatched patients were slightly more likely to have undergone a first autologous HCT than an allogeneic HCT (12.6% versus 9.0%), to have a larger number of missing linkage identifiers, and to have undergone HCT prior to 2010. Among the patients reported to the CIBMTR who matched to the CCR, 85.7% demonstrated concordance of both hematologic malignancy type and diagnosis date across data sources. This linkage presents unparalleled opportunities to advance our understanding of HCT practices and patient outcomes.
Developing a blockchain-based digitally secured model for the educational sector in Saudi Arabia toward digital transformation

New universities and educational organizations are increasing in Saudi Arabia with the increase in the need for high-quality education. This increased the need for a fast transformation to digitise the educational system in Saudi Arabia, which is one of the important pillars of the Saudi Vision 2030. The students who study in these organizations suffer the verification of academic records and other educational documents. Students who want to study at universities abroad also face the challenge of academic records and certificates verification. A secure, fast, and transparent model is required in the education sector in order to verify academic certificates issued by various educational organizations. Blockchain technology can be used with high data security to empower the educational sector of Saudi Arabia in the digital transformation and to help the educational organizations in verifying academic documents. In order to avoid any document fraud and forgery, along with the ease of verification of academic records and educational documents for the students. This research focuses on developing a model which will be helpful in achieving digital transformation in academic document verification by blockchain technology.
C2M: a maturity model for the evaluation of communication in distributed software development

Communication is essential in any software development project, particularly those globally distributed where geographical, temporal, and cultural distance may hinder the effectiveness of communication. The challenges imposed by distance often characterize communication as still one of the main drawbacks of globally distributed projects. Therefore, establishing communication processes and practices is relevant to support a team’s work. These processes and practices need to be updated and aligned with the team’s needs. Thus, assessing and evaluating the maturity of such communication processes and practices is paramount. This article presents a Communication Maturity Model called C2M which aims to help organizations identify the maturity of communication-related aspects by providing an approach for revealing what practices need to be improved. The model is composed of 4 levels of maturity (causal, partially managed, managed and reflective) and 4 areas of maturity (people, project, organizational and engineering) which are organized into 15 maturity factors, each factor comprising a set of practices. The model has 58 practices and each has its specific objectives. The model was empirically developed and evaluated in three well-defined phases. In the conception phase, methodological procedures (Tertiary Study, Systematic Literature Review, and Interviews) were carried out in order to gather relevant information for designing the first version of the C2M model (alpha version). Then, in the refinement phase, two focus group meetings were held in two organizations in order to identify how effectively the model attends its purpose. The results led to a second version of the C2M model (beta version), analyzed by a survey with experts who assessed the representation of the third version of the C2M model—omega version (evaluation phase). All results achieved so far suggest that the model can assist in discovering the maturity level of the communication processes and practices in globally distributed projects. Future works will focus on developing a software tool to help with self-assessment.
Nonlinear phase estimation via nonlinear-linear hybrid interferometers

Estimating nonlinear phase shifts is useful to analyze many dynamical processes. In this work, with respect to the second-order nonlinear phase shifts, we propose an estimation scheme with a coherent state as the input and balanced homodyne detection as the readout strategy. The measurement setup is a nonlinear-linear hybrid interferometer composed of an optical parametric amplifier and a beam splitter. We analyze the precision and determine the optimal parameters of our scheme. The precision is compared with the quantum Cramér–Rao bound as well as the precision of a conventional nonlinear interferometer. In view of the fact that photon loss is ubiquitous, the effects of transmission loss and detection loss on the precision are addressed. At low gain, our scheme is superior to the scheme using a conventional nonlinear interferometer, and the precision can saturate the quantum Cramér–Rao bound.
Railway Foreign Object Intrusion Detection based on Deep Learning

To detect foreign objects in the intrusion orbit and prevent railway safety accidents caused by foreign matter intrusion, the railway foreign objects intrusion detection algorithms based on neural network were studied in the paper. First, according to the common the railway foreign body intrusion condition, the foreign body intrusion image data were collected, cleaned and labeled. The railway foreign body intrusion data set was constructed. Then the YOLOv5 detection model of deep learning was built and trained on the established data set. Finally the performance of the detection model was verified by the test data set and compared with the popular network model. The experiment results show a good mean average accuracy score is obtained, which basically meets the safety requirements of train operation. The algorithm has important academic value and reference significance for the application of railway track foreign object detection.
Rigel: Transforming Tabular Data by Declarative Mapping

We present Rigel, an interactive system for rapid transformation of tabular data. Rigel implements a new declarative mapping approach that formulates the data transformation procedure as direct mappings from data to the row, column, and cell channels of the target table. To construct such mappings, Rigel allows users to directly drag data attributes from input data to these three channels and indirectly drag or type data values in a spreadsheet, and possible mappings that do not contradict these interactions are recommended to achieve efficient and straightforward data transformation. The recommended mappings are generated by enumerating and composing data variables based on the row, column, and cell channels, thereby revealing the possibility of alternative tabular forms and facilitating open-ended exploration in many data transformation scenarios, such as designing tables for presentation. In contrast to existing systems that transform data by composing operations (like transposing and pivoting), Rigel requires less prior knowledge on these operations, and constructing tables from the channels is more efficient and results in less ambiguity than generating operation sequences as done by the traditional by-example approaches. User study results demonstrated that Rigel is significantly less demanding in terms of time and interactions and suits more scenarios compared to the state-of-the-art by-example approach. A gallery of diverse transformation cases is also presented to show the potential of Rigel's expressiveness.
InCulture: A Collaborative Platform for Intangible Cultural Heritage Narratives

Intangible Culture Heritage (ICH) is defined as the collection of oral traditions and expressions such as epics, fairy tales, stories, arts, social practices, rituals and celebrations, events, knowledge, and practices related to nature and the universe, traditional medicine, folk medicine, traditional handcrafts, as well as personal experiences related to important historical events or cultural activities that shaped the historical and local identity. Under the UNESCO Convention for the Safeguarding of the ICH, nations are committed to developing inventories of ICH and working with local communities, groups, and individuals to preserve these traditions. In this paper, a platform is introduced that facilitates the collection of intangible ICH data, the formation of story-based narratives, and their presentation to the public via a web and mobile application, which offers Augmented Reality (AR) experiences. The platform aims to support the formation of user communities sharing common interests and to provide them with the appropriate tools for collecting pieces of ICH. Collected ICH resources and created narratives are modeled using semantic web technologies so that information can be perceived by third-party systems too. Furthermore, towards the dissemination of the platform, a real-world use case took place on the island of Rhodes focusing on the recent history of the island between 1912 and 1948 (WWII). The platform was implemented to support the goals of the project InCulture, funded by the EPAnEK Greek national co-funded operational program “Competitiveness Entrepreneurship and Innovation”.
A brief note on building augmented reality models for scientific visualization

Augmented reality (AR) has revolutionized the video game industry by providing interactive, three-dimensional visualization. Interestingly, AR technology has only been sparsely used in scientific visualization. This is, at least in part, due to the significant technical challenges previously associated with creating and accessing such models. To ease access to AR for the scientific community, we introduce a novel visualization pipeline with which they can create and render AR models. We demonstrate our pipeline by means of finite element results, but note that our pipeline is generally applicable to data that may be represented through meshed surfaces. Specifically, we use two open-source software packages, ParaView and Blender. The models are then rendered through the ¡model-viewer¿ platform, which we access through Android and iOS smartphones. To demonstrate our pipeline, we build AR models from static and time-series results of finite element simulations discretized with continuum, shell, and beam elements. Moreover, we openly provide python scripts to automate this process. Thus, others may use our framework to create and render AR models for their own research and teaching activities.
Cyber Bullying Detection Using Machine Learning

Cyber bullying has evolved as a severe problem hurting children, teenagers, and young adults as a result of the increasing use of social media. Automatic detection of bullying communications in social media is now possible, thanks to machine learning techniques, which could aid in the creation of a healthy and safe social media environment. One major issue in this important research area is robust and discriminative numerical representation learning of text messages. To address this challenge, we offer a new representation learning method in this study. The Semantic-Enhanced Marginalized Denoising Auto-Encoder (SMSDA) is a semantic enhancement of the popular deep learning model stacked denoising Auto-Encoder. The semantic extension is made up of semantic dropout noise and sparsity constraints, with the semantic dropout noise being the most important.
Extraction of the association rules from artificial neural networks based on the multiobjective optimization

Artificial Neural Network (ANN) is one of the powerful techniques of machine learning. It has shown its effectiveness in both prediction and classification problems. However, in some fields there is still some reticence towards their use mainly the fact that they do not justify their answers. The lack of transparency on how ANN makes decisions motivated us to develop our rule extraction algorithm that extracts comprehensible rules with high accuracy and high fidelity. The aim is to generate a set of rules that mimic the decision of ANN and cover a larger set of patterns. The obtained rule sets should satisfy a well-balanced trade-off between the fidelity, the accuracy and the comprehensibility. The proposed algorithm consists of a three steps: ANN learning phase, rule extraction phase and rule simplification phase. The rule extraction phase is based on the extraction of the association rules while the rules simplification procedure is based on the laws of Boolean algebra. To evaluate the performance of our algorithm, the system has been studied using four datasets, and then compared with other rule extraction methods. The results show that our proposal offers a small set of rules having the highest accuracy and fidelity values.
Getting The Best of Particle and Kalman Filters: GNSS Sensor Fusion using Rao-Blackwellized Particle Filter

In this paper, we develop a hybrid Bayesian filter for GNSS and visual odometry (VO) fusion in urban environments that combines the tracking efficiency of Kalman filter with the superior uncertainty modeling of particle filter. Our filter design employs Rao-Blackwellization to decouple the state into a non-linearly tracked position and linearly tracked orientation, velocity and carrier phase integer ambiguities. This factorization allows our filter to efficiently track the state along with a rich probability distribution of the position. Moreover, we utilize the tracked position probability distribution to quantify the uncertainty in situations where the tracking is erroneous. We evaluate our approach for state and uncertainty estimation using GNSS-VO fusion on real-world data and demonstrate that our filter exhibits comparable computation efficiency and improved state and uncertainty estimates over other Bayesian filter baselines.
DPIS

Nowadays, differential privacy (DP) has become a well-accepted standard for privacy protection, and deep neural networks (DNN) have been immensely successful in machine learning. The combination of these two techniques, i.e., deep learning with differential privacy, promises the privacy-preserving release of high-utility models trained with sensitive data such as medical records. A classic mechanism for this purpose is DP-SGD, which is a differentially private version of the stochastic gradient descent (SGD) optimizer commonly used for DNN training. Subsequent approaches have improved various aspects of the model training process, including noise decay schedule, model architecture, feature engineering, and hyperparameter tuning. However, the core mechanism for enforcing DP in the SGD optimizer remains unchanged ever since the original DP-SGD algorithm, which has increasingly become a fundamental barrier limiting the performance of DP-compliant machine learning solutions.
Interpretable click-through rate prediction through distillation of the neural additive factorization model

An accurate estimation of the click-through rate (CTR), that is, the probability of clicking on a recommended advertisement item online, is crucial for advertising agencies to make appropriate suggestions to users and maximise their income. However, the existing CTR prediction techniques exhibit two major limitations in estimating accurate CTRs. First, they lack interpretability as they are unable to justify and explain the outcomes of the models. Irrelevant recommendations of products and services may have severe consequences on the personal and financial safety of a user. Second, the high computational complexity of deep-learning models hinders deployment in real-time recommender systems. Thus, this study proposes an interpretable, accurate, and efficient CTR estimator based on the neural additive factorization model (NAFM). The NAFM leverages the model distillation framework, where it effectively learns from a gated ensemble of existing CTR models. The NAFM provides interpretable insights into features and their interactions with a reduced computational cost. The effectiveness and efficiency of the proposed NAFM was verified by applying it on two public click-through datasets, namely, Criteo and Avazu. Unlike post-hoc explainable models, the NAFM avoids an additional phase of training to generate an interpretable model, and its explanations are truthful to the model.