Reviewerly/exHarmony huggingface dataset:
KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors

Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to incorrect clustering results because of high sensitivity to noise points. In this study, we propose a robust spectral clustering algorithm known as KNN-SC that can discover exact clusters by decreasing the influence of noise points. To achieve this goal, we present a novel approach that filters out potential noise points by estimating the density difference between data points using k-nearest neighbors. In addition, we introduce a novel method for generating a similarity graph in which various densities of data points are effectively represented by expanding the nearest neighbor graph. Experimental results on synthetic and real-world datasets demonstrate that KNN-SC achieves significant performance improvement over many state-of-the-art spectral clustering algorithms.
Verified Security for the Morello Capability-enhanced Prototype Arm Architecture

Abstract Memory safety bugs continue to be a major source of security vulnerabilities in our critical infrastructure. The CHERI project has proposed extending conventional architectures with hardware-supported capabilities to enable fine-grained memory protection and scalable compartmentalisation, allowing historically memory-unsafe C and C++ to be adapted to deterministically mitigate large classes of vulnerabilities, while requiring only minor changes to existing system software sources. Arm is currently designing and building Morello, a CHERI-enabled prototype architecture, processor, SoC, and board, extending the high-performance Neoverse N1, to enable industrial evaluation of CHERI and pave the way for potential mass-market adoption. However, for such a major new security-oriented architecture feature, it is important to establish high confidence that it does provide the intended protections, and that cannot be done with conventional engineering techniques. In this paper we put the Morello architecture on a solid mathematical footing from the outset. We define the fundamental security property that Morello aims to provide, reachable capability monotonicity, and prove that the architecture definition satisfies it. This proof is mechanised in Isabelle/HOL, and applies to a translation of the official Arm specification of the Morello instruction-set architecture (ISA) into Isabelle. The main challenge is handling the complexity and scale of a production architecture: 62,000 lines of specification, translated to 210,000 lines of Isabelle. We do so by factoring the proof via a narrow abstraction capturing essential properties of arbitrary CHERI ISAs, expressed above a monadic intra-instruction semantics. We also develop a model-based test generator, which generates instruction-sequence tests that give good specification coverage, used in early testing of the Morello implementation and in Morello QEMU development, and we use Arm’s internal test suite to validate our model. This gives us machine-checked mathematical proofs of whole-ISA security properties of a full-scale industry architecture, at design-time. To the best of our knowledge, this is the first demonstration that that is feasible, and it significantly increases confidence in Morello.
Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. The algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. The algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts.

Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model’s embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT’s and target language vocabulary. Our adaptation techniques offer substantial performance gains for languages with unseen scripts. We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.
A Semi-supervised Learning for Segmentation of Gigapixel Histopathology Images from Brain Tissues

Automated segmentation of grey matter (GM) and white matter (WM) in gigapixel histopathology images is advantageous to analyzing distributions of disease pathologies, further aiding in neuropathologic deep phenotyping. Although supervised deep learning methods have shown good performance, its requirement of a large amount of labeled data may not be cost-effective for large scale projects. In the case of GM/WM segmentation, trained experts need to carefully trace the delineation in gigapixel images. To minimize manual labeling, we consider semi-surprised learning (SSL) and deploy one state-of-the-art SSL method (FixMatch) on WSIs. Then we propose a two-stage scheme to further improve the performance of SSL: the first stage is a self-supervised module to train an encoder to learn the visual representations of unlabeled data, subsequently, this well-trained encoder will be an initialization of consistency loss-based SSL in the second stage. We test our method on Amyloid-β stained histopathology images and the results outperform FixMatch with the mean IoU score at around 2% by using 6,000 labeled tiles while over 10% by using only 600 labeled tiles from 2 WSIs.Clinical relevance— this work minimizes the required labeling efforts by trained personnel. An improved GM/WM segmentation method could further aid in the study of brain diseases, such as Alzheimer’s disease.
Prediction of site-specific solar diffuse horizontal irradiance from two input variables in Colombia

Accurate measurements of diffuse irradiance are essential to design a solar photovoltaic system. However, in-situ radiation measurements in Colombia, South America, can be limited by the costs of the implementation of meteorological stations equipped with a pyranometer mounted on a sun tracker with a shading device, which is required to measure diffuse irradiance. Furthermore, the databases found in Colombia contain missing data, which raises the need for implementing models that are trained with very few features. In this paper, we introduce a methodology based on simple angle calculations and a regression model to predict half-hourly diffuse horizontal solar irradiance from only the measure of global horizontal irradiance and a geographic coordinate as inputs. Using measurements taken from the national solar radiation database for 6 different sites in Colombia and state-of-the-art machine learning models for regression, we validated the accuracy prediction of the proposed methodology. The results showed a prediction error ranging from 5.86 to 9.36 [W/m2], and a coefficient of determination ranging from 0.9974 to 0.9983. The data-set used along with the feature engineering process and the deep neural network model created can be found in a Github repository referenced in the paper.
Generating Adversarial Images in Quantized Domains

Many adversarial attacks produce floating-point tensors which are no longer adversarial when converted to raster or JPEG images due to rounding. This paper proposes a method dedicated to quantize adversarial perturbations. This “smart” quantization is conveniently implemented as versatile post-processing. It can be used on top of any white-box attack targeting any model. Its principle is tantamount to a constrained optimization problem aiming to minimize the quantization error while keeping the image adversarial after quantization. A Lagrangian formulation is proposed and an appropriate search of the Lagrangian multiplier enables to increase the success rate. We also add a control mechanism of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _\infty $ </tex-math></inline-formula> -distortion. Our method operates in both spatial and JPEG domains with little complexity. This study shows that forging adversarial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">images</i> is not a hard constraint: our quantization does not introduce any extra distortion. Moreover, adversarial images quantized as JPEG also challenge defenses relying on the robustness of neural networks against JPEG compression.
Region-attentive multimodal neural machine translation

We propose a multimodal neural machine translation (MNMT) method with semantic image regions called region-attentive multimodal neural machine translation (RA-NMT). Existing studies on MNMT have mainly focused on employing global visual features or equally sized grid local visual features extracted by convolutional neural networks (CNNs) to improve translation performance. However, they neglect the effect of semantic information captured inside the visual features. This study utilizes semantic image regions extracted by object detection for MNMT and integrates visual and textual features using two modality-dependent attention mechanisms. The proposed method was implemented and verified on two neural architectures of neural machine translation (NMT): recurrent neural network (RNN) and self-attention network (SAN). Experimental results on different language pairs of Multi30k dataset show that our proposed method improves over baselines and outperforms most of the state-of-the-art MNMT methods. Further analysis demonstrates that the proposed method can achieve better translation performance because of its better visual feature use.
Blockchain-IoT peer device storage optimization using an advanced time-variant multi-objective particle swarm optimization algorithm

Abstract The integration of Internet of Things devices onto the Blockchain implies an increase in the transactions that occur on the Blockchain, thus increasing the storage requirements. A solution approach is to leverage cloud resources for storing blocks within the chain. The paper, therefore, proposes two solutions to this problem. The first being an improved hybrid architecture design which uses containerization to create a side chain on a fog node for the devices connected to it and an Advanced Time-variant Multi-objective Particle Swarm Optimization Algorithm (AT-MOPSO) for determining the optimal number of blocks that should be transferred to the cloud for storage. This algorithm uses time-variant weights for the velocity of the particle swarm optimization and the non-dominated sorting and mutation schemes from NSGA-III. The proposed algorithm was compared with results from the original MOPSO algorithm, the Strength Pareto Evolutionary Algorithm (SPEA-II), and the Pareto Envelope-based Selection Algorithm with region-based selection (PESA-II), and NSGA-III. The proposed AT-MOPSO showed better results than the aforementioned MOPSO algorithms in cloud storage cost and query probability optimization. Importantly, AT-MOPSO achieved 52% energy efficiency compared to NSGA-III. To show how this algorithm can be applied to a real-world Blockchain system, the BISS industrial Blockchain architecture was adapted and modified to show how the AT-MOPSO can be used with existing Blockchain systems and the benefits it provides.
A Blockchain-Based Framework for Distributed Agile Software Development

Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development.In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security.These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams.This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology.We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developer's payment requirements, and automatic payment distribution into the digital wallets of development teams.The execution of these smart contracts automatically assign penalties to the customers for late or nonpayments and also to the developers for overdue tasks.Moreover, we have also solved the blockchain's scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage.Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.
From public health to cyber hygiene: Cybersecurity and Canada’s healthcare sector

The COVID-19 pandemic has ushered in a wave of cyberattacks targeting the healthcare sector, including against hospitals, doctors, patients, medical companies, supply chains, universities, research laboratories, and public health organizations at different levels of jurisdiction and across the public and private sectors. Despite these concerns, cybersecurity in Canadian healthcare is significantly understudied. This article uses a series of illustrative examples to highlight the challenges, outcomes, and solutions Canada might consider in addressing healthcare cybersecurity. The article explores the various rationales by which Canadian healthcare may be targeted, unpacks several prominent types of cyberattack used against the healthcare sector, identifies the different malicious actors motivated to conduct such attacks, provides insights derived from three empirical cases of healthcare cyberattack (Boston Children’s Hospital [2014], Anthem [2015], National Health Service [2017]), and concludes with lessons for a Canadian response to healthcare cybersecurity from several international perspectives (e.g., Australia, New Zealand, the UK, Norway, and the Netherlands).
An Automated Toxicity Classification on Social Media Using LSTM and Word Embedding

The automated identification of toxicity in texts is a crucial area in text analysis since the social media world is replete with unfiltered content that ranges from mildly abusive to downright hateful. Researchers have found an unintended bias and unfairness caused by training datasets, which caused an inaccurate classification of toxic words in context. In this paper, several approaches for locating toxicity in texts are assessed and presented aiming to enhance the overall quality of text classification. General unsupervised methods were used depending on the state-of-art models and external embeddings to improve the accuracy while relieving bias and enhancing F1-score. Suggested approaches used a combination of long short-term memory (LSTM) deep learning model with Glove word embeddings and LSTM with word embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT), respectively. These models were trained and tested on large secondary qualitative data containing a large number of comments classified as toxic or not. Results found that acceptable accuracy of 94% and an F1-score of 0.89 were achieved using LSTM with BERT word embeddings in the binary classification of comments (toxic and nontoxic). A combination of LSTM and BERT performed better than both LSTM unaccompanied and LSTM with Glove word embedding. This paper tries to solve the problem of classifying comments with high accuracy by pertaining models with larger corpora of text (high-quality word embedding) rather than the training data solely.
Problems and Risks of Digitalization in Higher Education

The article offers a critical analysis of the impact of digital technologies on higher education. The authors discuss digitalization of higher education in relation to broader sociocultural and political and economic challenges: globalization, commercialization, socio-economic inequality, and ethical issues of technology application. Using this approach, the authors demonstrate that first of all, the rapid digitalization of higher education during the pandemic has activated some of the existing tension points and problematic trends: ‘McDonaldization’ of education, managerialism in higher education governance, the consumerist attitude to learning, the development of the elite education model, etc. Secondly, digitalization has introduced new risks related to the growing influence of global technology companies, online modes of the commodification of learning, the digital divide as a factor of educational inequality, and new ethical challenges. If these risks are not timely addressed, it may jeopardize the creative self-organization of educators and students, hinder the development of diverse and ethically responsible practices of technology use, and further make the higher education system unbalanced and more dependent on commercial technology companies. To mitigate the risks, the authors recommend that the academic community scrutinize the educational principles and ideas that are currently guiding the development of educational digital technologies. Furthermore, the academic community should take a proactive stance on how these technologies should work, and which pedagogical and ethical principles should inform their design. It is likewise essential to support the development of alternative models of digital technologies for education to be designed in partnership with all stakeholders in higher education.
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition

Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons.However, the inner composition of entity mentions in characterlevel Chinese NER has been rarely studied.Actually, most mentions of regular types have strong name regularity.For example, entities end with indicator words such as "公司 (company) " or "银行 (bank)" usually belong to organization.In this paper, we propose a simple but effective method for investigating the regularity of entity spans in Chinese NER, dubbed as Regularity-Inspired reCOgnition Network (RICON).Specifically, the proposed model consists of two branches: a regularity-aware module and a regularity-agnostic module.The regularity-aware module captures the internal regularity of each span for better entity type prediction, while the regularity-agnostic module is employed to locate the boundary of entities and relieve the excessive attention to span regularity.An orthogonality space is further constructed to encourage two modules to extract different aspects of regularity features.To verify the effectiveness of our method, we conduct extensive experiments on three benchmark datasets and a practical medical dataset.The experimental results show that our RICON significantly outperforms previous state-of-the-art methods, including various lexicon-based methods.
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation.In DST, modelling the relations among domains and slots is still an under-studied problem.Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations.It also uses the schemata to facilitate knowledge transfer to new domains.DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder.Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1,and MultiWOZ2.2),show that DSGFNet outperforms existing methods.
Distributed Edge Computing with Blockchain Technology to Enable Ultra-Reliable Low-Latency V2X Communications

Vehicular communication is a promising technology that has been announced as a main use-case of the fifth-generation cellular system (5G). Vehicle-to-everything (V2X) is the vehicular communication paradigm that enables the communications and interactions between vehicles and other network entities, e.g., road-side units (RSUs). This promising technology faces many challenges related to reliability, availability and security of the exchanged data. To this end, this work aims to solve the scientific problem of building a vehicular network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The proposed work provides a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels and an energy-efficient offloading algorithm to manage traffic offloading to the MEC server. The main applications of the blockchain and MEC technology in the developed system are discussed. Furthermore, the developed system, with the introduced sub-systems and algorithms, was evaluated over a reliable environment, for different simulation scenarios, and the obtained results are discussed.