Reviewerly/exHarmony huggingface dataset:
Efficient, Immutable and Privacy Preserving E-Healthcare Systems using Blockchain

Electronic Healthcare Record (EHR) system consists of a patient (or entity acting on behalf of it) sharing his critical medical information with specific people or organizations. Due to the sensitivity of the medical record, a privacy-preserving access control mechanism needs to be in place for building trust among entities including data owner and organizations. Another important metric to be considered is the integrity and accuracy of the outsourced shared medical record. Although existing techniques in the literature tried to address these challenges, however, they are not tolerant of certain types of attacks including collusion, tampering, etc. This research encompasses the proxy re-encryption mechanism and blockchain technology to address the security and privacy concerns of EHR systems. The performance results and security analysis depicts the effectiveness of the scheme to be adopted in practice.
Protection Mechanism against Software Supply Chain Attacks through Blockchain

Software supply chain attack is exceptionally fatal, overwhelmingly rapid, and almost effortless on the part of the attacker. A single compromise or hack can lead to multiple businesses sufferings because suppliers and providers have a vast user network. Protection against software supply chain attacks is being achieved through some outdated concepts like honeytokens and privileged pathway, but an integrated and proper mechanism is still lacking. In recent years, blockchain technology represents a fundamental shift that can replace conventional business models that rely on third parties for trust. This paper proposes a protection mechanism through validity concept with the implementation of blockchain to retain an immutable and trustworthy record of propagating payload (i.e., official update) that is transmitted across the supply chain systems. Blockchain can be used to circumvent these supply chain attacks by keeping a digital log of all propagating information. Each block contains the information regarding validity of propagating data, based on this an official update can be accepted or rejected.
Autonomous Robot with Artificial Intelligence for Taking Health Constants

Health facilities are generally short-staffed and overworked.This has a significant impact on the reliability of the acquisition of health constants required at the start of diagnosis.Manual acquisition and transmission of these constants and other data leads to delays in the execution of successive care-related tasks.What's more, the quality of service is sometimes compromised by a lack of communication between patients and staff.In pediatrics, this is compounded by the difficulty of diagnosis in the face of children's silence, intimidated by the hospital environment.Technological assistance would relieve healthcare staff of the need to perform certain repetitive tasks.The solution proposed in this document studies a robot, based on electrical, electronic, computer and artificial intelligence resources, with human-machine interaction for taking vitals and health data in health facilities.This system enables height, mass and temperature to be taken autonomously and without contact.The algorithm we've developed uses artificial intelligence to check the conditions for correct measurements, both bareheaded and barefoot.This solution also alerts you to epidemic trends such as obesity.This health data is made available in the healthcare facility on terminals such as tablets, smartphones and computers used by nursing staff.This work will help healthcare staff to take automatic health vitals without contact, and to acquire and circulate data via a computer network.
Application of the guidelines of the Agency for Digital Italy guidelines and CSA Star self-assessment: A Docustar case study

The paper shows the new particularly rigorous regulatory context introduced by the entry into force of the AGID guidelines with reference to the application of the Italian digital administration code on the formation, management and storage of electronic documents. In this regard, a concrete case study of the innovative software Docustar is presented, developed by the innovative SME Stella All in One, an ISO 27001:2013 certified company. The cloud software implemented in this way represents a real revolution both in terms of digital rights management of documents, but also in terms of AGID compliance. In addition, being a cloud software intended for use by the public administration, the system perfectly meets the requirements of the CSA Star Self-Assessment, another focal point together with the ISO 27001 standard to ensure the security of information on application systems.
An experimental annotation task to investigate annotators’ subjectivity in a Misogyny dataset

In recent years, hatred directed against women has spread exponentially, especially in online social media. Although this alarming phenomenon has given rise to many studies both from the viewpoint of computational linguistics and from that of machine learning, less effort has been devoted to analysing whether models for the detection of misogyny are affected by bias. An emerging topic that challenges traditional approaches for the creation of corpora is the presence of social bias in natural language processing (NLP). Many NLP tasks are subjective, in the sense that a variety of valid beliefs exist about what the correct data labels should be; some tasks, for example misogyny detection, are highly subjective, as different people have very different views about what should or should not be labelled as misogynous. An increasing number of scholars have proposed strategies for assessing the subjectivity of annotators, in order to reduce bias both in computational resources and in NLP models. In this work, we present two corpora: a corpus of messages posted on Twitter after the liberation of Silvia Romano on the 9th of May, 2020 and corpus of comments constructed starting from posts on Facebook that contained misogyny, developed through an experimental annotation task, to explore annotators’ subjectivity. For a given comment, the annotation procedure consists in selecting one or more chunk from each text that is regarded as misogynistic and establishing whether a gender stereotype is present. Each comment is annotated by at least three annotators in order to better analyse their subjectivity. The annotation process was carried by trainees who are engaged in an internship program. We propose a qualitative-quantitative analysis of the resulting corpus, which may include non-harmonised annotations.
Helping Pull Request Reviewer Recommendation Systems to Focus

The selection of code reviewers for a pull request can impact the quality as well as the speed of the review. In order to have the best experience both for the developer and the reviewers, there is a need for automatic reviewer recommendation systems for pull requests. Although there has been previous research in this area, it was mainly focused on smaller and medium repositories of up to around 200 developers, while larger repositories were rarely targeted. In this paper we evaluate several existing approaches on a set of 8 Microsoft repositories of different sizes, noticing that the average performance of the approaches seemed to decrease with the number of reviewers the repository has. In order to focus the existing approaches only on relevant reviewers, we propose a technique for improving their performance by scoping down the set of candidate reviewers based on multiple filters. We defined several basic filters and determined that 5 out of the 7 tested existing approaches experienced performance improvements of up to 16.24% better precision and 19.66% better recall averaged over all datasets, with the per dataset improvements peaking at 36.63% better precision and 28.63% better recall. Additionally, by combining different basic filters we were able to achieve additional improvements for 6 out of the 7 existing approaches (with a minor improvement for the remaining approach), which on average over all datasets had up to 17.60% better precision and 21.23% better recall, while the per dataset improvements peaked at 43.28% better precision and 30.94% better recall.
Iris Flower Recognition Using Four-Channels Ring Photonic Reservoir Computing Based on Unidirectional Coupled VCSELs

In this paper, a ring Four-channels reservoir computing (RC) based on unidirectional coupled vertical cavity surface emitting lasers (VCSELs) is proposed and demonstrated numerically. Here, the ring four channels are realized by four VCSELs for the first time. Such proposed RC system is employed to solve the Iris flower classification task, and the highest classification accuracy achieved at 100%. Besides, the effects of bias currents, external injection strength, frequency detuning, feedback strength, couple strength, as well as the number of virtual nodes on the recognition performance of the ring Four-channels RC based on VCSELs are also considered. The ring four-channel RC system based on VCSEL proposed may be of great significant for improving the RC system’s ability to solve more complex problems.
CRule: Category-Aware Symbolic Multihop Reasoning on Knowledge Graphs

Multihop reasoning is essential in knowledge graph (KG) research and applications. Current methods rely on specific KG entities, while human cognition operates at a more abstract level. This article proposes a category-aware rule-based (CRule) approach for symbolic multihop reasoning. Specifically, given a KG, CRule first categorizes entities and constructs a category-aware KG; it then uses rules retrieved from the categorized KG to perform multihop reasoning on the original KG. Experiments on five datasets show that CRule is simple, is effective, and combines the advantages of symbolic and neural network methods. It overcomes symbolic reasoning’s complexity limitations, can perform reasoning on KGs of more than 300,000 edges, and can be three times more efficient than neural network models.
Contrastive Video Question Answering via Video Graph Transformer

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the correct and incorrect answers, as well as the relevant and irrelevant questions respectively. With superior video encoding and QA solution, we show that CoVGT can achieve much better performances than previous arts on video reasoning tasks. Its performances even surpass those models that are pretrained with millions of external data. We further show that CoVGT can also benefit from cross-modal pretraining, yet with orders of magnitude smaller data. The results demonstrate the effectiveness and superiority of CoVGT, and additionally reveal its potential for more data-efficient pretraining.
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning

Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background. Most current methods align domains by using image or instance-level adversarial feature alignment. This often suffers due to unwanted background and lacks class-specific alignment. A straightforward approach to promote class-level alignment is to use high confidence predictions on unlabeled domain as pseudo-labels. These predictions are often noisy since model is poorly calibrated under domain shift. In this paper, we propose to leverage model's predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment. We develop a technique to quantify predictive uncertainty on class assignments and bounding-box predictions. Model predictions with low uncertainty are used to generate pseudo-labels for self-training, whereas the ones with higher uncertainty are used to generate tiles for adversarial feature alignment. This synergy between tiling around uncertain object regions and generating pseudo-labels from highly certain object regions allows capturing both image and instance-level context during the model adaptation. We report thorough ablation study to reveal the impact of different components in our approach. Results on five diverse and challenging adaptation scenarios show that our approach outperforms existing state-of-the-art methods with noticeable margins.
High-Quality Angle Prediction for Oriented Object Detection in Remote Sensing Images

Oriented object detection is a challenging task in remote sensing, where the detected objects can be represented by oriented bounding boxes (OBBs). Angle prediction in oriented object detection has been widely studied, due to its crucial role in object detection. However, the precision of angle prediction is severely limited by misalignments in most of the existing methods, including representation-, evaluation-, and optimization-based misalignments. To alleviate these misalignments, this paper presents a novel angle prediction method, called Angle Quality Estimation (AQE). Specifically, our proposed AQE transforms the angle prediction task into a distribution estimation task to address the representation misalignment problem and implicitly measure the quality of the predicted angles. Based on the estimated angle quality, we then propose a new metric to comprehensively evaluate the quality of OBBs. Then we propose an object aspect ratio based loss function to optimize angle prediction for addressing the optimization misalignment. Our proposed AQE is a plug-and-play method, which can be embedded on any existing oriented object detector. Experimental results on three public benchmarks, including DOTA, HRSC2016, and ICDAR2015 datasets, show that our method achieves better performance than the other state-of-the-art.
A Novel Secure and Privacy-Preserving Model for OpenID Connect Based on Blockchain

OpenID Connect (OIDC) is one of the most widely used delegated authentication protocols in web and mobile applications providing a single sign-on experience.It allows third-party applications, called Relying Parties (RP), to securely request and receive information about authenticated sessions and endusers from an identity provider.The OIDC specification defines several parameters, including the client_id, client_secret, authorization code, access token, id token, state, and redirect_uri, as keys to the protocol operation, with significant security and privacy implications.Therefore, securing these parameters is critical to prevent attackers from impersonating legitimate entities, gaining unauthorized access, having complete control over users' accounts, and/or violating their privacy.To enhance OIDC security and preserve its users' privacy, we propose a novel model for OIDC based on the Ethereum Blockchain and the non-fungible token (ERC721) standard.To prove the robustness and safety of the proposed system, we perform a detailed security analysis formally using the most widely accepted protocols security verification tools, AVISPA and Scyther, and informally by discussing various attacks.The analysis results show that the proposed system is resilient against well-known attacks.Furthermore, we evaluate the cost and performance of the proposed solution, confirming its affordability and assuring that our approach does not impact the user experience and performance of existing OIDC-based systems.Finally, we conduct a security and privacy comparative analysis with similar existing systems, proving the superiority and efficiency of our proposed Blockchainbased OIDC system.
Quality Assessment of Low-Light Restored Images: A Subjective Study and an Unsupervised Model

The quality assessment (QA) of restored low-light images is an important tool for benchmarking and improving low-light restoration (LLR) algorithms.While several LLR algorithms exist, the subjective perception of the restored images has been much less studied.Challenges in capturing aligned low-light and well-lit image pairs and collecting a large number of human opinion scores of quality for training, warrant the design of unsupervised (or opinion unaware) no-reference (NR) QA methods.This work studies the subjective perception of low-light restored images and their unsupervised NR QA.Our contributions are two-fold.We first create a dataset of restored low-light images using various LLR methods, conduct a subjective QA study, and benchmark the performance of existing QA methods.We then present a self-supervised contrastive learning technique to extract distortion-aware features from the restored lowlight images.We show that these features can be effectively used to build an opinion unaware image quality analyzer.Detailed experiments reveal that our unsupervised NR QA model achieves state-of-theart performance among all such quality measures for low-light restored images. INDEX TERMSContrastive learning, low-light restored images dataset, low-light image quality, quality aware feature learning I.
Text-dependent speaker verification using discrete wavelet transform based on linear prediction coding

Presenting a system for verifying the identity of people so that the data could be recorded in the simplest possible way, performing identity verification with acceptable accuracy, and being robust to attacks and noises is one of some important issues in today's era. This paper presents a text-dependent speaker verification system in which features are extracted from the data using a method called discrete wavelet transform based on linear prediction coding. In addition to the conventional classification in these systems, a new method for classification in which one binary classification will perform for all speakers of the dataset can also be done, which will be introduced in this research. Then the performance of the designed system on ASVspoof 2015, ASVspoof 2017, ASVspoof 2019, AudioMNIST, and TIMIT datasets will be evaluated using accuracy, precision, sensitivity, specificity, F1-score, and equal error rate (EER) metrics. It will be shown that the EER for the case where classification is done once for each speaker is 0.14±0.83% for the ASVspoof 2017 dataset and the accuracy and EER for the case where classification is done once for all speakers is 100±0.00% and 0.00±0.00% for all datasets.
Down Syndrome detection with Swin Transformer architecture

Down Syndrome, also known as Trisomy 21, is a severe genetic disease caused by an extra chromosome 21. For the detection of Trisomy 21, despite those statistical methods have been widely used for screening, karyotyping remains the gold standard and the first level of testing for diagnosis. Due to karyotyping being a time-consuming and labour-intensive procedure, Computer Vision methodologies have been explored to automate the karyotyping process for decades. However, few studies have focused on Down Syndrome detection with the Transformer technique. This study develops a Down-Syndrome-Detector (DSD) architecture based on the Transformer structure, which includes a segmentation module, an alignment module, a classification module, and a Down Syndrome indicator. The segmentation and classification modules are designed by homogeneous transfer learning at the model level. Transfer learning techniques enable a network to share weights learned from the source domain (e.g., millions of data in ImageNet) and optimize the weights with limited labeled data in the target domain (e.g., less than 6,000 images in BioImLab). The Align-Module is designed to process the segmentation output to fit the classification dataset, and the Down Syndrome Indicator identifies a Down Syndrome case from the classification output. Experiments are first performed on two public datasets BioImLab (119 cases) and Advanced Digital Imaging Research (ADIR, 180 cases). Our performance metrics indicate the good ability of segmentation and classification modules of DSD. Then, the DS detection performance of DSD is evaluated on a private dataset consisting of 1084 cells (including 20 DS cells from 2 singleton cases): 90.0% and 86.1% for cell-level TPR and TNR; 100% and 96.08% for case-level TPR and TNR, respectively. This study develops a pipeline based on the modern Transformer architecture for the detection of Down Syndrome from original metaphase micrographs. Both segmentation and classification models developed in this study are assessed using public datasets with commonly used metrics, and both achieved good results. The DSDproposed in this study reported satisfactory singleton case-specific DS detection results. Significance: As verified by a medical specialist, the developed method may improve Down Syndrome detection efficiency by saving human labor and improving clinical practice.
BDFL: A Blockchain-Enabled FL Framework for Edge-based Smart UAV Delivery Systems

In recent years, edge-based smart unmanned aerial vehicle (UAV) delivery systems have attracted a lot of attention by both academia and industry given its promising business value and also as an ideal testbed for many emerging technologies such as edge computing, blockchain and machine learning. At the moment, one of the critical challenges for smart UAV delivery systems is data privacy since the massive amount of data is being generated by both users and UAVs and the data is used for training machine learning models to support various smart applications such as autonomous navigation, facial recognition, and person re-identification (ReID). To tackle such a challenge, federated learning (FL) has been widely used as a promising solution since it only needs to share and update model parameters with the centralised server without transmitting the raw data. However, conventional FL still faces the issue of the single-point-of-failure. To address these issues, in this paper, we propose BDFL, a Blockchain-enabled decentralised FL framework for edge-based smart UAV delivery systems. In our framework, Blockchain provides a decentralised network for FL to eliminate the need for a centralised server and store private data in the decentralised permissioned Blockchain to avoid the single-point-of-failure. To motivate our study and analyse the privacy concerns, we employ the person ReID application in smart UAV delivery systems as a typical example. In addition, we also provide the customised proof of quality factor (cPoQF) consensus protocol to address the scalability issue of the Blockchain in order to support the increasing number of smart applications in UAV delivery system. The effectiveness of the framework is demonstrated through experiments on energy efficiency, confirmation time and throughput, with further discussion on the impact of the incentive mechanism, and the analysis of its resiliency under various security attacks.