Reviewerly/exHarmony huggingface dataset:
A Machine-Learning Based Approach for Detecting Phishing URLs

This research’s focus is to utilize different machine learning classification models to predict whether a given URL is a legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users. Finally, the proposed model yielded a 97% detection accuracy using the XGBoost classifier and the random forest classifier.
Can Badges Foster a More Welcoming Culture on Q&amp;A Boards?

Thriving online communities rely on a steady stream of newcomers to contribute new content. However, retaining newcomers has proven challenging. In this paper, we measure the success of an intervention used by Stack Exchange question-answering communities to create a more welcoming environment for newcomers. That intervention consisted in highlighting contributions by new users with a special indicator. We hypothesize that Stack Exchange's new policy would reduce negative reactions to new users and, ultimately, increase new user retention. We leverage causal modeling to assess the introduction of the so-called “new contributor indicator”, and we find it did not counter user retention decline in the short- and long-terms. However, our results indicate it did reduce unwelcoming reactions towards newcomers in the short-term. Our work has practical implications for online community managers aiming to improve their onboarding processes.
SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation

&lt;abstract&gt; &lt;p&gt;Automatic segmentation of tumor-infiltrating lymphocytes (TILs) from pathological images is essential for the prognosis and treatment of cancer. Deep learning technology has achieved great success in the segmentation task. It is still a challenge to realize accurate segmentation of TILs due to the phenomenon of blurred edges and adhesion of cells. To alleviate these problems, a squeeze-and-attention and multi-scale feature fusion network (SAMS-Net) based on codec structure, namely SAMS-Net, is proposed for the segmentation of TILs. Specifically, SAMS-Net utilizes the squeeze-and-attention module with the residual structure to fuse local and global context features and boost the spatial relevance of TILs images. Besides, a multi-scale feature fusion module is designed to capture TILs with large size differences by combining context information. The residual structure module integrates feature maps from different resolutions to strengthen the spatial resolution and offset the loss of spatial details. SAMS-Net is evaluated on the public TILs dataset and achieved dice similarity coefficient (DSC) of 87.2% and Intersection of Union (IoU) of 77.5%, which improved by 2.5% and 3.8% compared with UNet. These results demonstrate the great potential of SAMS-Net in TILs analysis and can further provide important evidence for the prognosis and treatment of cancer.&lt;/p&gt; &lt;/abstract&gt;
Peeler: Learning to Effectively Predict Flakiness without Running Tests

Regression testing is a widely adopted approach to expose change-induced bugs as well as to verify the correctness/robustness of code in modern software development settings. Unfortunately, the occurrence of flaky tests leads to a significant increase in the cost of regression testing and eventually reduces the productivity of developers (i.e., their ability to find and fix real problems). State-of-the-art approaches leverage dynamic test information obtained through expensive re-execution of test cases to effectively identify flaky tests. Towards accounting for scalability constraints, some recent approaches have built on static test case features, but fall short on effectiveness. In this paper, we introduce Peeler, a new fully static approach for predicting flaky tests through exploring a representation of test cases based on the data dependency relations. The predictor is then trained as a neural network based model, which achieves at the same time scalability (because it does not require any test execution), effectiveness (because it exploits relevant test dependency features), and practicality (because it can be applied in the wild to find new flaky tests). Experimental validation on 17,532 test cases from 21 Java projects shows that Peeler outperforms the state-of-the-art FlakeFlagger by around 20 percentage points: we catch 22% more flaky tests while yielding 51% less false positives. Finally, in a live study with projects in-the-wild, we reported to developers 21 flakiness cases, among which 12 have already been confirmed by developers as being indeed flaky.
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs with Language Structures via Dependency Relationships

Understanding realistic visual scene images together with language descriptions is a fundamental task towards generic visual understanding. Previous works have shown compelling comprehensive results by building hierarchical structures for visual scenes (e.g., scene graphs) and natural languages (e.g., dependency trees), individually. However, how to construct a joint vision-language (VL) structure has barely been investigated. More challenging but worthwhile, we introduce a new task that targets on inducing such a joint VL structure in an unsupervised manner. Our goal is to bridge the visual scene graphs and linguistic dependency trees seamlessly. Due to the lack of VL structural data, we start by building a new dataset VLParse. Rather than using labor-intensive labeling from scratch, we propose an automatic alignment procedure to produce coarse structures followed by human refinement to produce high-quality ones. Moreover, we benchmark our dataset by proposing a contrastive learning (CL)-based framework VLGAE, short for Vision-Language Graph Autoencoder. Our model obtains superior performance on two derived tasks, i.e., language grammar induction and VL phrase grounding. Ablations show the effectiveness of both visual cues and dependency relationships on fine-grained VL structure construction.
Underwater Image Enhancement Method Based on Feature Fusion Neural Network

Aiming at the problems of uneven illumination of underwater image caused by supplementary illumination in deep-sea and night waters, image noise, low contrast and color deviation caused by suspended particles in water, a new underwater image enhancement method under non-uniform illumination is proposed.The heterogeneous feature fusion module is designed to fuse different levels and different levels of features, so as to improve the overall perception ability of the network to detail information and semantic information.Secondly, a new feature attention mechanism is designed to improve the traditional channel attention mechanism, and the improved channel attention and pixel attention mechanism are added to the heterogeneous feature fusion process to strengthen the ability of the network to extract pixel features with different turbidity.Then, the dynamic feature enhancement module is designed to adaptively expand the receptive field to improve the adaptability of the network to the image distortion scene and the ability of model conversion, and strengthen the network's learning of the region of interest.Finally, the color loss function is designed, and the absolute error loss and structural similarity loss are jointly minimized to correct the color deviation on the basis of maintaining the image texture.A multi-scale feature extraction module is designed to extract different levels of features at the beginning of the network, and the output results are obtained through the convolution layer with jump connection and the attention module.The experimental results on several data sets show that this method can have good results in processing synthetic underwater images and real underwater images, and can better restore the image color and texture details compared with the existing methods.It conforms to the characteristics of human vision, and the visual effect is better than the existing underwater image enhancement algorithms.
Progressive Dual-Branch Network for Low-Light Image Enhancement

Images captured in low light and backlit conditions are characterized by low brightness, low contrast, and varying degrees of degradation. Simply enhancing image contrast will fully expose hidden noise and color distortion, affecting people’s subjective visual perception and performance in other application scenarios. In order to improve the loss of details, color imbalance and artifacts in the enhanced images, we propose a progressive dual branch network(PDBNet) for low-light image enhancement. In this paper, an assisted recovery module(ARM) is designed by exploiting the hybrid correlation and feature complementarity between the inverted image and the low-light image. Feature information at different scales is progressively extracted by cascading multiple ARMs. Considering the network execution efficiency and the amount of parameters, we use depthwise separable convolution(DSC) and asymmetric assisted recovery module(ARM) to improve the computational efficiency of the model. To reduce the degradation caused by enhancing image contrast, the introduction of the large kernel attention(LKA) block allows the network to emphasize hidden low-light information regions, effectively suppressing noise and improving color imbalance. In order to effectively fuse the feature information between the inverse image and the low-light image, an attention fusion block(AFB) is designed. AFB can effectively acquire global feature information and re-encode semantic dependencies between channels. Finally, a fusion reconstruction module(FRM) is designed to further refine the feature information and enhance the information flow between networks. After sufficient qualitative and quantitative experiments in publicly available low-light image datasets, it is known that our method has better visual quality and metric evaluation scores than other state-of-the-art low-light image enhancement methods.
Ontological Analysis and Redesign of Security Modeling in ArchiMate

Enterprise Risk Management and security have become a fundamental part of Enterprise Architecture, so several frameworks and modeling languages have been designed to support the activities associated with these areas. ArchiMate’s Risk and Security Overlay is one of such proposals, endorsed by The Open Group. We investigate the capabilities of the proposed security-related constructs in ArchiMate with regard to the necessities of enterprise security modeling. Our analysis relies on a well-founded reference ontology of security to uncover ambiguity, missing modeling elements, and other deficiencies of the security modeling capabilities in ArchiMate. Based on this ontologically-founded analysis, we propose a redesign of security aspects of ArchiMate to overcome its original limitations.
Multi-Objective Big Data View Materialization Using NSGA-III

Present day applications process large amount of data that is being produced at brisk rate and is heterogeneous with levels of trustworthiness. This Big data largely consists of semi-structured and unstructured data, which needs to be processed in admissible time so that timely decisions are taken that benefit the organization and society. Such real time processing would require Big data view materialization that would enable faster and timely processing of decision making queries. Several algorithms exist for Big data view materialization. These algorithms aim to select Big data views that minimize the total query processing cost for the query workload. In literature, this problem has been articulated as a bi-objective optimization problem, which minimizes the query evaluation cost along with the update processing cost. This paper proposes to adapt the reference point based non-dominated sorting genetic algorithm, to design an NSGA-III based Big data view selection algorithm (BDVSANSGA-III) to address this bi-objective Big data view selection problem. Experimental results revealed that the proposed BDVSANSGA-III was able to compute diverse non-dominated Big data views and performed better than the existing algorithms..
Quantum Encryption and Decryption in IBMQ Systems using Quantum Permutation Pad

We present a functioning implementation of Kuang et al.’s Quantum Permutation Pad (QPP) using the Qiskit developmental kit on the currently available International Business Machines (IBM) quantum computers. For this implementation, we use a pad with 28 2-qubit permutation gates that provides 128 bits of entropy. In this implementation, we divide the plaintext into blocks of 2-bits each. Each such block is encrypted one at a time. For any given block of plaintext, a quantum circuit is created with qubits initialized according to the given plaintext 2-bit block. The plaintext qubits are then acted on with 2-qubit permutation operators chosen from a 28- permutation QPP pad. Due to the inability to send qubits directly, the ciphertext qubits are measured and transmitted to the decrypting side over a classical channel. The decryption can be performed on either a classical or quantum computer. The decryption uses an inverse Quantum Permutation Pad with the Hermitian conjugates of the corresponding permutation gates used for the encryption. We are currently working on advancing the implementation of QPP to include additional steps for security and efficiency.
Cybersecurity Risk Management Framework for Blockchain Identity Management Systems in Health IoT

Blockchain (BC) has recently paved the way for developing Decentralized Identity Management (IdM) systems for different information systems. Researchers widely use it to develop decentralized IdM systems for the Health Internet of Things (HIoT). HIoT is considered a vulnerable system that produces and processes sensitive data. BC-based IdM systems have the potential to be more secure and privacy-aware than centralized IdM systems. However, many studies have shown potential security risks to using BC. A Systematic Literature Review (SLR) conducted by the authors on BC-based IdM systems in HIoT systems showed a lack of comprehensive security and risk management frameworks for BC-based IdM systems in HIoT. Conducting a further SLR focusing on risk management and supplemented by Grey Literature (GL), in this paper, a security taxonomy, security framework, and cybersecurity risk management framework for the HIoT BC-IdM systems are identified and proposed. The cybersecurity risk management framework will significantly assist developers, researchers, and organizations in developing a secure BC-based IdM to ensure HIoT users’ data privacy and security.
How Research Data Management Plans Can Help in Harmonizing Open Science and Approaches in the Digital Economy

Abstract Within this perspective article, we intend to summarise definitions and terms that are often used in the context of open science and data‐driven R&amp;D and we discuss upcoming European regulations concerning data, data sharing and handling. With this background in hand, we take a closer look at the potential connections and permeable interfaces of open science and digital economy, in which data and resulting immaterial goods can become vital pieces as tradeable items. We believe that both science and the digital economy can profit from a seamless transition and foresee that the scientific outcomes of publicly funded research can be better exploited. To close the gap between open science and the digital economy, and to serve for a balancing of the interests of data producers, data consumers, and an economy around services and the public, we introduce the concept of generic research data management plans (RDMs), which have in part been developed through a community effort and which have been evaluated by academic and industry members of the NFDI4Cat consortium. We are of the opinion that in data‐driven research, RDMs do need to become a vital element in publicly funded projects.
A Cloud-Native Web Application for Assisted Metadata Generation and Retrieval: THESPIAN-NER

Within the context of the Competence Centre for the Conservation of Cultural Heritage (4CH) project, the design and deployment of a platform-as-a-service cloud infrastructure for the first European competence centre of cultural heritage (CH) has begun, and some web services have been integrated into the platform. The first integrated service is the INFN-CHNet web application for FAIR storage of scientific analysis on CH: THESPIAN-Mask. It is based on CIDOC-CRM-compatible ontology and CRMhs, describing the scientific metadata. To ease the process of metadata generation and data injection, another web service has been developed: THESPIAN-NER. It is a tool based on a deep neural network for named entity recognition (NER), enabling users to upload their Italian-written report files and obtain labelled entities. Those entities are used as keywords either to serve as (semi)automatically custom queries for the database, or to fill (part of) the metadata form as a descriptor for the file to be uploaded. The services have been made freely available in the 4CH PaaS cloud platform.
Honeycomb Artifact Removal Using Convolutional Neural Network for Fiber Bundle Imaging

We present a new deep learning framework for removing honeycomb artifacts yielded by optical path blocking of cladding layers in fiber bundle imaging. The proposed framework, HAR-CNN, provides an end-to-end mapping from a raw fiber bundle image to an artifact-free image via a convolution neural network (CNN). The synthesis of honeycomb patterns on ordinary images allows conveniently learning and validating the network without the enormous ground truth collection by extra hardware setups. As a result, HAR-CNN shows significant performance improvement in honeycomb pattern removal and also detailed preservation for the 1961 USAF chart sample, compared with other conventional methods. Finally, HAR-CNN is GPU-accelerated for real-time processing and enhanced image mosaicking performance.
DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN

Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% F1 scores on sentiment140.
SMAM: Self and Mutual Adaptive Matching for Skeleton-Based Few-Shot Action Recognition

This paper focuses on skeleton-based few-shot action recognition. Since skeleton is essentially a sparse representation of human action, the feature maps extracted from it, through a standard encoder network in the few-shot condition, may not be sufficiently discriminative for some action sequences that look partially similar to each other. To address this issue, we propose a self and mutual adaptive matching (SMAM) module to convert such feature maps into more discriminative feature vectors. Our method, named as SMAM-Net, first leverages both the temporal information associated with each individual skeleton joint and the spatial relationship among them for feature extraction. Then, the SMAM module adaptively measures the similarity between labeled and query samples and further carries out feature matching within the query set to distinguish similar skeletons of various action categories. Experimental results show that the SMAM-Net outperforms other baselines on the large-scale NTU RGB + D 120 dataset in the tasks of one-shot and five-shot action recognition. We also report our results on smaller datasets including NTU RGB + D 60, SYSU and PKU-MMD to demonstrate that our method is reliable and generalises well on different datasets. Codes and the pretrained SMAM-Net will be made publicly available.
Multi-modal unsupervised domain adaptation for semantic image segmentation

We propose a novel multi-modal-based Unsupervised Domain Adaptation (UDA) method for semantic segmentation. Recently, depth has proven to be a relevent property for providing geometric cues to enhance the RGB representation. However, existing UDA methods solely process RGB images or additionally cultivate depth-awareness with an auxiliary depth estimation task. We argue that geometric cues that are crucial to semantic segmentation, such as local shape and relative position, are challenging to recover from an auxiliary depth estimation task with mere color (RGB) information. In this paper, we propose a novel multi-modal UDA method named MMADT, which relies on both RGB and depth images as input. In particular, we design a Depth Fusion Block (DFB) to recalibrate depth information and leverage Depth Adversarial Training (DAT) to bridge the depth discrepancy between the source and target domain. Besides, we propose a self-supervised multi-modal depth estimation assistant network named Geo-Assistant (GA) to align the feature space of RGB and depth and shape the sensitivity of our MMADT to depth information. We experimentally observed significant performance improvement in multiple synthetic to real adaptation benchmarks, i.e., SYNTHIA-to-Cityscapes, GTA5-to-Cityscapes and SELMA-to-Cityscapes. Additionally, our multi-modal UDA scheme is easy to port to other UDA methods with a consistent performance boost.