Reviewerly/exHarmony huggingface dataset:
Galois Hull Dimensions of Gabidulin Codes

For a prime power q, an integer m and 0 ≤ e ≤ m − 1 we study the e-Galois hull dimension of Gabidulin codes G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> (α) of length m and dimension k over ${\mathbb{F}_{{q^m}}}$. Using a self-dual basis α of ${\mathbb{F}_{{q^m}}}$ over ${\mathbb{F}_q}$, we first explicitly compute the hull dimension of G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> (α). Then a necessary and sufficient condition of G <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> (α) to be linear complementary dual (LCD), self-orthogonal and self-dual will be provided. We prove the existence of e-Galois (where $e = \frac{m}{2}$) self-dual Gabidulin codes of length m for even q, which is in contrast to the known fact that Euclidean self-dual Gabidulin codes do not exist for even q. As an application, we construct two classes of MDS entangled-assisted quantum error-correcting codes (MDS EAQECCs) whose parameters have more flexibility compared to known codes in this context.
TSDN: Two-Stage Raw Denoising in the Dark

Denoising is one of the most significant procedures in the image processing pipeline. Nowadays, deep-learning-based algorithms have achieved superior denoising quality than traditional algorithms. However, the noise becomes severe in the dark environment, where even the SOTA algorithms fail to achieve satisfactory performance. Besides, the high computational complexity of deep-learning-based denoising algorithms makes them hardware unfriendly and difficult to process high-resolution images in real-time. To address these issues, a novel low-light RAW denoising algorithm Two-Stage-Denoising (TSDN), is proposed in this paper. In TSDN, denoising consists of two procedures: noise removal and image restoration. Firstly, in the noise-removal stage, most noise is removed from the image, and an intermediate image that is easier for the network to recover the clean image is obtained. Then, in the restoration stage, the clean image is restored from the intermediate image. The TSDN is designed to be light-weight for real-time and hardware friendly. However, the tiny network will be insufficient for satisfactory performance if directly trained from scratch. Therefore, we present an Expand-Shrink-Learning (ESL) method to train the TSDN. In the ESL method, firstly, the tiny network is expanded to a larger one with similar architecture but more channels and layers, which enhances the learning ability of the network because of more parameters. Secondly, the larger network is shrunk and restored to the original small network in fine-grained learning procedures, including Channel-Shrink-Learning (CSL) and Layer-Shrink-Learning (LSL). Experimental results demonstrate that the proposed TSDN achieves better performance (PSNR and SSIM) than other SOTA algorithms in the dark environment. Besides, the model size of TSDN is one-eighth of that of the U-Net for denoising (a classical denoising network).
Distance-Aware Hierarchical Federated Learning in Blockchain-Enabled Edge Computing Network

Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-preserving distributed machine learning in the Internet of Things (IoT). However, the communication overhead caused by partial model aggregations will increase the model training latency. In this article, a multilayer blockchain-enabled hierarchical FL (HFL) network is proposed for low-latency model training while ensuring data security. Meanwhile, we theoretically analyze the bottleneck of the model accuracy with the total data distance due to the imbalanced data distribution. Moreover, the mathematical expression of the model error with respect to IoT devices (IDs) association and local data distribution is provided, then the upper bound of the model error is represented by the total data distance. To further improve the learning performance, the distance-aware HFL (DAHFL) algorithm is investigated, which optimizes ID association strategy based on dual-distance, and allocates computing and communication resources alternatively. Finally, the working process of the blockchain-enabled HFL system is exhibited by the blockchain simulation platform and the efficiency of the proposed DAHFL algorithm is demonstrated by the simulation results.
A Multi-Fidelity Bayesian Approach to Safe Controller Design

Safely controlling unknown dynamical systems is one of the biggest challenges in the field of control systems. Oftentimes, an approximate model of a system’s dynamics exists which provides beneficial information for control design. However, differences between the approximate and true systems present challenges as well as safety concerns. We propose an algorithm called SAFESLOPE to safely evaluate points from a Gaussian process model of a function when its Lipschitz constant is unknown. We establish theoretical guarantees for the performance of SAFESLOPE and quantify how multi-fidelity modeling improves the algorithm’s performance. Finally, we present a case where SAFESLOPE achieves lower cumulative regret than a naive sampling method by applying it to find the control gains of a linear time-invariant system.
Classification of Text Writing Proficiency of L2 Learners

In this study, we present a novel system for the automatic classification of text complexity in the Italian language, focusing on the phraseological dimension. This quantitative assessment of text complexity is crucial for various applications, including text readability measurement, text simplification, and support for educators during evaluation processes. We use a dataset comprising texts written by Italian L2 learners and classified according to the levels of the Common European Framework of Reference for Languages. The dataset texts serve as a basis for calculating phraseological features, which are then used as input for multiple machine-learning classifiers to compare their performance in predicting proficiency levels. Our experimental results demonstrate that the proposed framework effectively harnesses phraseological complexity features to achieve high classification accuracy in determining proficiency levels.
SmartGraph: Static Analysis Tool for Solidity Smart Contracts

Smart contracts written in Solidity can have various vulnerabilities, which can lead to significant losses if not detected and addressed in time. Static analysis is an effective method to identify potential security issues before deploying smart contracts on the blockchain. In this paper, we present a tool for static analysis of Solidity smart contracts that uses a graph as an intermediate representation to facilitate analysis. The tool converts Solidity code into a graph structure that captures the code’s structure and dependencies, allowing analysis at a higher level of abstraction. The tool provides a set of rules for detecting various security issues such as reentrancy attacks, integer overflows, and state variables shadowing. We evaluate the tool on a set of Solidity contracts, present en experimental comparison of the tool performance compared to other existing tools, and demonstrate its effectiveness in detecting security vulnerabilities previously reported in the literature. The tool’s use of a graph-based intermediate representation enables a high level of abstraction that can capture complex relationships and dependencies between the various components of the code. Overall, the proposed tool provides an efficient and effective approach to perform static analysis of Solidity smart contracts.
An Exploration Towards Sustainable Metaverse Systems for e-Learning by Student Designers: A Meta-analysis

The metaverse has caught the imagination and investments of many. However, in developing countries, the metaverse may raise more questions, than answers. As such, this study aims to investigate first, student-designers' perceptions towards what and how they want the metaverse to be like, and whether students would prioritize information flow; second, the efficacy of triangulating 3 usability-HCI questionnaires; third, whether student-designers would be more interested, creative, and be more confident in designing and developing for interconnecting (non) metaverse (eco)systems. Findings from alpha-beta user testings of Figma prototypes indicate preference for balance between structure and freedom/creativity, enhanced by user feedback and open ecosystems. Mind maps, sequencing of chain of effects in descending order, and the derived hierarchical relationship reflect centralities in design, and continuums in notions of what sustainable interconnected systems in the metaverse may be like. They are influenced by how perspectives, objectives, utility functions, and evaluation criteria, dynamically affect shifts in these centrality of design (dy/dx) over time (dy/dt), based on demographics. Loosely coupled pointer-based systems, are thus viable complements to integration.
K-Anonymity Versus PSI3 for Depersonalization and Security Assessment of Large Data Structures

The article presents a modern approach to protecting personal data from de-anonymization. Descriptions of the k-anonymity model using entropy, with one-way publication of information, and the PSI3 technique using homomorphic encryption, with bilateral or multilateral information exchange, examples of the use of metrics for assessing the quality of depersonalization and their improvement, recommendations for using methods in the current activities of personal data operators are given.
Area-Efficient Accelerator for the Full NTRU-KEM Algorithm

Among Post Quantum Cryptography (PQC) algorithms, Nth-degree Truncated-polynomial Ring Units Key Encapsulation Mechanism (NTRU-KEM) emerged as a promising cryptosystem for key establishment. However, the algorithm requires more storage space and computation resources compared to classical cryptosystems, resulting in substantial memory and performance overheads. To mitigate these overheads, researchers have focused on enhancing the efficiency of the NTRU-KEM algorithm with dedicated hardware implementation, but have excluded the key generation function, resulting in a tenfold increase in latency when generating new keys frequently. In this paper, we aim to implement an efficient NTRU-KEM algorithm with full functionality by incorporating all functions, including key generation, using a hardware and software co-design approach. We strategically allocate functions based on their inherent parallelism and execution time to hardware or software. Our approach entails implementing hardware modules to be shared across multiple sub-functions as much as possible to achieve optimal performance improvement over hardware resources. Our implementation demonstrated a significant speedup compared to pure software implementation in the execution time of all three functions of NTRU-KEM, with a remarkable performance improvement in key generation. Our approach resulted in more than three times the area reduction effect compared to prior work focused only on encapsulation and decapsulation functions, and showed similar or better results of performance per area depending on the function.
A Decentralized Smart City Using Solid and Self-Sovereign Identity

In the Internet of Things (IoT) context, a considerable quantity of data flows from sensors to centralized servers, holding sensitive information related to users. Unfortunately, how servers store these data instances is usually poorly documented and does not offer any transparency to the users but may pave the way to possible privacy violations. Web decentralization is a prominent solution to cope with these issues and legal obligations regarding data protection so that multiple domains are progressively adopting it as the principal technological enabler. The IoT is not among them yet, as a centralized approach is still the most common one; however, moving data location away from servers to prefer gateways or directly to devices closer to users and under their direct control can realize a more decentralized approach and alleviate the issues related to performance, throughput as well as data protection. This paper aims to exploit existing data decentralization solutions, like Social Linked Data (Solid), to define a more distributed data management for IoT and propose a proof-of-concept implementation of a Smart City platform where users can store and directly manage data produced by public or private IoT devices. Despite providing decentralized data handling, Solid is still affected by a centralized identity management and authentication implementation represented by OpenID Connect. Therefore, to fulfill our vision of a decentralized IoT, we also investigate how decentralizing authentication within Solid and a new user-centric approach based on Self-Sovereign Identity (SSI) represents a promising solution.
Unsupervised TTS Acoustic Modeling for TTS With Conditional Disentangled Sequential VAE

In this paper, we propose a novel unsupervised textto-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. UTTS is a multi-speaker speech synthesizer that supports zero-shot voice cloning, it is developed from a perspective of disentangled speech representation learning. The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. We leverage recent advancements in self-supervised speech representation learning as well as speech synthesis frontend techniques for system development. Specifically, we employ our recently formulated Conditional Disentangled Sequential Variational Auto-encoder (C-DSVAE) as the backbone UTTS AM, which offers well-structured content representations given unsupervised alignment (UA) as condition during training. For UTTS inference, we utilize a lexicon to map input text to the phoneme sequence, which is expanded to the frame-level forced alignment (FA) with a speaker-dependent duration model. Then, we develop an alignment mapping module that converts FA to UA. Finally, the C-DSVAE, serving as the self-supervised TTS AM, takes the predicted UA and a target speaker embedding to generate the mel spectrogram, which is ultimately converted to waveform with a neural vocoder. We show how our method enables speech synthesis without using a paired TTS corpus in AM development stage. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations. Audio samples are available at our demo page
Classification of Malicious Websites Using Machine Learning Based on URL Characteristics

This article focuses on evaluating the efficiency of machine learning classification algorithms in detecting malicious websites based on their URL addresses. A highly reliable dataset of URL addresses is used to train the machine learning classification model. The results show that the Random Forest algorithm achieves a high accuracy of 95.68% in detecting malicious websites and provides an effective solution to this problem. At the same time, software for detecting and warning of malicious website in two forms is also implemented: web applications and browser extensions.
Efficient Blockchain-Based Electronic Medical Record Sharing With Anti-Malicious Propagation

Electronic Medical Records (EMRs) sharing enhances healthcare and biomedical discoveries but faces challenges: data provider centralization and limited interoperability. Blockchain can address these issues, but existing systems struggle with malicious EMR propagation due to challenges concerning the authenticity, non-repudiation, and integrity of the digital signatures they employ. Universal Designated Verifier Signature Proof (UDVSP) may be an intuitive solution, but existing UDVSP schemes are inefficient due to time-consuming bilinear pairing operations. In this article, we first adopt the Elliptic Curve Digital Signature Algorithm (ECDSA) to propose a more efficient and blockchain-friendly UDVSP scheme, following by a blockchain-based EMR sharing system (EMRChain). To our best knowledge, our proposed UDVSP scheme is the first bilinear pairing-free solution, and EMRChain is the first blockchain-based EMR sharing system to possess the anti-malicious propagation. Furthermore, we proceed to provide a thorough security analysis and performance evaluation of both our UDVSP scheme and EMRChain. Our UDVSP scheme is provably secure within the security model and offers significant computational cost savings (at least 86.76%) and reduced communication overhead (at least 59.37%) compared to existing UDVSP schemes. These results, along with the EMRChain prototype, effectively showcase the utility and effectiveness of EMRChain, which is built upon our UDVSP scheme.
A highly reliable encoding and decoding communication framework based on semantic information

Increasing research has focused on semantic communication, the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver. In this paper, we design a novel encoding and decoding semantic communication framework, which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels. On the sender side, the average semantic loss caused by the wrong detection is defined, and a semantic source encoding strategy is developed to minimize the average semantic loss. To further improve communication reliability, a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver. Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.
Radio Anomaly Detection Based on Improved Denoising Diffusion Probabilistic Models

Detection of radio anomalies is critical for wireless communication security. In this letter, we propose an algorithm for radio anomaly detection based on improved denoising diffusion probabilistic models (DDPMs). First, the signal’s power spectral density (PSD) is obtained. Next, an autoencoder-DDPMs (AE-DDPMs) framework is proposed by incorporating an autoencoder into DDPMs to learn the distribution of normal signals and their PSD. Finally, the signal is determined to be anomalous or not based on the error between the reconstructed data and the input data. Simulation results demonstrate that the performance of the AE-DDPMs-based algorithm outperforms state-of-the-art algorithms.
Gamma-Minimax Wavelet Shrinkage for Signals with Low SNR

In this paper, we propose a method for wavelet denoising of signals contaminated with Gaussian noise when prior information about the ${L^{2}}$-energy of the signal is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose simple, level-dependent shrinkage rules that turn out to be Γ-minimax for a suitable class of priors. The proposed methodology is particularly well suited in denoising tasks when the signal-to-noise ratio is low, which is illustrated by simulations on a battery of some standard test functions. Comparison to some commonly used wavelet shrinkage methods is provided.
Pre-Training MLM Using Bert for the Albanian Language

Abstract Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all the time (Kryeziu &amp; Shehu, 2022). Natural Language Processing is a very active field of study, where transformers have a key role. Transformers function based on neural networks and they are increasingly showing promising results. One of the first major contributions to transfer learning in Natural Language Processing was the use of pre-trained word embeddings in 2010 (Joseph, Lev, &amp; Yoshua, 2010). Pre-trained models like ELMo (Matthew, et al., 2018) and BERT (Delvin, et al., 2019) are trained on large corpora of unlabeled text and as a result learning from text representations has achieved good performance on many of the underlying tasks on datasets from different domains. Pre-training in the language model has proven that there has been an improvement in some aspects of natural language processing, based on the paper (Dai &amp; Le, 2015). In present paper, we will pre-train BERT on the task of Masked Language Modeling (MLM) with the Albanian language dataset (alb_dataset) that we have created for this purpose (Kryeziu et al., 2022). We will compare two approaches: training of BERT using the available OSCAR dataset and using our alb_dataset that we have collected. The paper shows some discrepancies during training, especially while evaluating the performance of the model.
Whole Tumor Area Estimation in Incremental Brain MRI Using Dilation and Erosion-Based Binary Morphing

Magnetic resonance imaging (MRI) technology is rapidly advancing and three-dimensional (3D) scanners started to play an important role on diagnosis. However, not every medical center has access to 3D magnetic resonance imaging (MRI) devices; therefore, it is safe to state that the majority of MRI scans are still two-dimensional. According to the setup values adjusted before any scan, there might be consistent gaps between the MRI slices, especially when the increment value exceeds the thickness. The gap causes miscalculation of the lesion volumes and misjudgments when the lesions are reconstructed in three-dimensional space due to excessive interpolation. Therefore, in this paper, we present the details of three types of conventional morphing methods, one dilation-based and two erosion-based, and compare them to figure out which one provides better solution for filling up the gaps in incremental brain MRI. Among three types of morphing methods, the highest average dice score coefficient (DSC) is calculated as %91.95, which is obtained by the multiplicative dilation morphing method for HG/0004 set of BraTS 2012.
In Dialogue with the More-than-Human: Affective Prefiguration in Encounters with Others

editorial Open Access Share on In Dialogue with the More-than-Human: Affective Prefiguration in Encounters with Others Author: Ann Light University of Sussex and Malmö University University of Sussex and Malmö UniversityView Profile Authors Info & Claims InteractionsVolume 30Issue 4July - August 2023pp 24–27https://doi.org/10.1145/3599956Published:28 June 2023Publication History 0citation397DownloadsMetricsTotal Citations0Total Downloads397Last 12 Months397Last 6 weeks397 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
A Preamble to Feminist Ecologies in HCI

editorial Free Access Share on A Preamble to Feminist Ecologies in HCI Authors: Gopinaath Kannabiran Pratt Institute Pratt InstituteView Profile , Marie Louise Juul Søndergaard The Oslo School of Architecture and Design The Oslo School of Architecture and DesignView Profile Authors Info & Claims InteractionsVolume 30Issue 4July - August 2023pp 20–23https://doi.org/10.1145/3604914Published:28 June 2023Publication History 0citation577DownloadsMetricsTotal Citations0Total Downloads577Last 12 Months577Last 6 weeks577 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF