Reviewerly/exHarmony huggingface dataset:
Quasiorthogonal Dimension

An interval approach to the concept of dimension is presented. The concept of quasiorthogonal dimension is obtained by relaxing exact orthogonality so that angular distances between unit vectors are constrained to a fixed closed symmetric interval about $$\pi /2$$. An exponential number of such quasiorthogonal vectors exist as the Euclidean dimension increases. Lower bounds on quasiorthogonal dimension are proven using geometry of high-dimensional spaces and a separate argument is given utilizing graph theory. Related notions are reviewed.
A privacy‐preserving multifactor authentication system

Abstract In recent years, there has been a significant number of works on the development of multifactor authentication (MFA) systems. Traditionally, behavioral biometrics (eg, keystroke dynamics) have been known to have the best usability because they do not require one to know or possess anything—they simply communicate “how you type” to an authenticator. However, though highly usable, MFA approaches that are based on biometrics are highly intrusive, and users' sensitive information is exposed to untrusted servers. To address this privacy concern, in this paper, we present a privacy‐preserving MFA system for computer users, called PINTA. In PINTA, the second factor is a hybrid behavioral profile user, while the first authentication factor is a password. The hybrid profile of the user includes host‐based and network flow‐based features. Since the features include users' sensitive information, it needs to be protected from untrusted parties. To protect users' sensitive profiles and to handle the varying nature of the user profiles, we adopt two cryptographic methods: Fuzzy hashing and fully homomorphic encryption (FHE). Our results show that PINTA can successfully validate legitimate users and detect impostors. Although the results are promising, the trade‐off for privacy preservation is a slight reduction in performance compared with traditional identity‐based MFA techniques.
Structuring Techlaw

Technological breakthroughs challenge core legal assumptions and generate regulatory debates. Practitioners and scholars usually tackle these questions by examining the impacts of a particular technology within conventional legal subjects — say, by considering how drones should be regulated under privacy law, property law, or the law of armed conflict. While individually useful, these siloed analyses mask the repetitive nature of the underlying questions and necessitate the regular reinvention of the regulatory wheel. An overarching framework — one which can be employed across technologies and across subjects — is needed.The fundamental challenge of tech-law is not how to best regulate novel technologies, but rather how to best address familiar forms of uncertainty in new contexts. Accordingly, we construct a three-part framework, designed to encourage a more thoughtful resolution of tech-law questions. It: (1) delineates the three types of tech-fostered legal uncertainty, which facilitates recognizing common issues; (2) requires a considered selection between permissive and precautionary approaches to technological regulation, given their differing distributive consequences; and (3) highlights tech-law-specific considerations when extending extant law, creating new law, or reassessing a legal regime.This structure emphasizes the possibility of considered and purposeful intervention in the iterative and co-constructive relationship between law and technology. By making it easier to learn from the rich history of prior dilemmas and to anticipate future issues, this framework enables policymakers, judges, and other legal actors to make more just and effective regulatory decisions going forward.
Implications of AI in National Security: Understanding the Security Issues and Ethical Challenges

National security is the security and defence of a country. In the UK, national security provides coordination on security and intelligence issues of strategic importance across a government []. In the midst of the fourth industrial revolution, many countries including the UK have adopted Artificial Intelligence (AI) to achieve optimal national security for its citizens, economy, and institutions. In fact, AI, with its evolving intelligent behaviours, has become a major priority for defense for many governments. AI has the potential to support a number of domestic and international security initiatives, from online security to counter-terrorism. It also has the potential to change everything about the way government offices relate to each other and the rest of the world, how they work and ultimately get on together. Therefore, the more pressing question centres around how AI will shape national security? AI encompasses a series of complex issues that cut across security and ethical boundaries. This book chapter will discuss these themes in relation to national security. In detail, it will examine the security issues and ethical challenges of AI for the operational and strategic levels of national Security.
Robust Label Rectifying With Consistent Contrastive-Learning for Domain Adaptive Person Re-Identification

Domain adaptive person re-identification (Re-ID) is challenging due to the domain gap between the source and target domains. Existing methods have recently shown great promise by training models with contrastive learning and assigning pseudo labels by clustering, in which a memory bank is utilized to keep features for contrast. However, two main problems lead to sub-optimal generalization ability for existing methods. First, there is no constraint on the updating for memory kept features in existing methods, resulting in inaccurate contrastive learning. Second, the inevitable noisy labels during clustering are usually ignored. To alleviate these problems, we propose a Label Rectifying with Consistent Contrastive-learning (LRCC) framework with two strategies. (1) The consistent contrastive-learning (CC) strategy works with a memory bank which stores the source domain class centroids and all the target domain image features. With the CC strategy, the contrast is conducted across the source and target domains simultaneously. More specifically, we design and maintain consistent clustering during model iteration, thus the classes of memory kept target features are invariable in one epoch. (2) The label rectifying (LR) strategy introduces an auxiliary classifier into the LRCC framework. Thus the pseudo labels are rectified by minimizing the prediction variance between the primary classifier and the auxiliary classifier. To verify the effectiveness of LRCC, we conduct experiments on three public person Re-ID datasets under the domain adaptive setting, DukeMTMC-reID, Market-1501, and MSMT17. The experimental results demonstrate that the proposed LRCC can obtain reliable pseudo labels and achieves state-of-the-art adaptation performance.
Security-enrichment of an asymmetric optical image encryption-based devil’s vortex Fresnel lens phase mask and lower upper decomposition with partial pivoting in gyrator transform domain

An asymmetric optical cryptosystem to encrypt images using devil’s vortex Fresnel lens (DVFLs) phase masks and lower upper decomposition with partial pivoting (LUDPP) is proposed in gyrator transform domain. The proposed cryptosystem utilizes DVFLs which are the complex phase masks designed using the combination of a phases of devil’s lens, vortex lens, and Fresnel lens. LUDPP is an operation used to decompose the matrix and is utilized to supersede the phase-truncation task in the traditional phase-truncated Fourier transform (PTFT). Hence, the proposed method is immune to the attacks to which the PTFT-based cryptosystems are vulnerable. The cryptosystem is asymmetric as both the encryption and decryption processes are different along with different keys. The private keys generated during the encryption process are utilised in the decryption process to retrieve the original image. The encryption and decryption process can be realised with both the digital and the modified optical architecture. In order to show the strength and robustness of the proposed encryption, a conspire numerical simulation was performed.
Data Science

Abstract The presence of new science does not necessarily occur just like that. Every science starts from interests, discussion, and looks for a basic foundation, but in general the main foundation of science is mathematics. Data science includes structured and systematic knowledge about data. However, many other sciences that has a relationship with the data in question, ranging from statistics to computer science. This paper aims to reveal the obstacle and limitations of other science into a data science completely, on that basis the definition of data sciences needs to be elaborated, then confirm data science as new science and not depend directly on several other sciences.
Individualisation of Education in Terms of E‑learning: Experience and Prospects

The problem of individualisation in education in terms of e-learning remains relevant despite a lot of research done in the area. The present article is intended to review existing education practice individualisation in e-learning and to determine the prospects of development of adaptive e-learning courses that provide the student with an individual educational environment content which focuses on the individual characteristics of students. The authors arrive at the conclusion that building an individual educational trajectory in the adaptive e-learning course has a significant advantage in organising education in the electronic environment for both the student and the teacher. The main feature of these courses becomes the selection of educational content that best matches the individual abilities, needs and needs of the student in an electronic environment. The results of the research are universally applicable and can serve as a basis for organising adaptive web-based teaching not only for mathematics, but also for other disciplines in institutions of secondary, higher and further education
Energy Efficient Merkle Trees for Blockchains

Blockchain-powered smart systems deployed in different industrial applications promise operational efficiencies and improved yields, while mitigating significant cybersecurity risks pertaining to the main application. Associated tradeoffs between availability and security arise at implementation, however, triggered by the additional resources (e.g., memory, computation) required by each blockchain-enabled host. This paper applies an energy-reducing algorithmic engineering technique for Merkle Tree root calculations, a principal element of blockchain computations, as a means to preserve the promised security benefits but with less compromise to system availability. Using pyRAPL, a python library to measure computational energy, we experiment with both the standard and energy-reduced implementations of the Merkle Tree for different input sizes (in bytes). Our results show up to 98% reduction in energy consumption is possible within the blockchain's Merkle Tree construction module, such reductions typically increasing with larger input sizes. The proposed energy-reducing technique is similarly applicable to other key elements of blockchain computations, potentially affording even “greener” blockchain-powered systems than implied by only the Merkle Tree results obtained thus far.
Data

Abstract Towards understanding the big data conceptually, it is necessary to understand data entirely. Understanding of data is not only directly related to the relationship between the sample and the population, but also to determine the form of approach and the process used. This is based on data dimensions that require in-depth consideration to be able to be the useful information in decision making, and become the helpful knowledge in the direction of policy in the Industry 4.0 era. Therefore this paper is built to briefly describe data.
MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue

Abstract Mitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a $$68.7\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>68.7</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> F1-score and $$49.0\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>49.0</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists’ decision.
FPGA-based electronic system for the control and readout of superconducting quantum processors

Electronic systems for qubit control and measurement serve as a bridge between quantum programming language and quantum information processors. With the rapid development of superconducting quantum circuit technology, synchronization in a large-scale system, low-latency execution, and low noise are required for electronic systems. Here, we present a field-programmable gate array (FPGA)-based electronic system with a distributed synchronous clock and trigger architecture. The system supports synchronous control of qubits with jitters of ∼5 ps. We implement a real-time digital signal processing system in the FPGA, enabling precise timing control, arbitrary waveform generation, in-phase and quadrature demodulation for qubit state discrimination, and the generation of real-time qubit-state-dependent trigger signals for feedback/feedforward control. The hardware and firmware low-latency design reduces the feedback/feedforward latency of the electronic system to 125 ns, significantly less than the decoherence times of the qubit. Finally, we demonstrate the functionalities and low-noise performance of this system using a fluxonium quantum processor.
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult.We study interactive weakly-supervised learning-the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model.Our proposed model, named PR-BOOST, achieves this goal via iterative promptbased rule discovery and model boosting.It uses boosting to identify large-error instances and then discovers candidate rules from them by prompting pre-trained LMs with rule templates.The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model.Experiments on four tasks show PRBOOST outperforms state-of-the-art WSL baselines up to 7.1%, and bridges the gaps with fully supervised models.Our Implementation is available at
Implementation of Quantum Annealing: A Systematic Review

Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.
Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence

A patient’s health information is generally fragmented across silos because it follows how care is delivered: multiple providers in multiple settings. Though it is technically feasible to reunite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization for this purpose. Machine learning can be conducted in a federated manner on patient datasets with the same set of variables but separated across storage. But federated learning cannot handle the situation where different data types for a given patient are separated vertically across different organizations and when patient ID matching across different institutions is difficult. We call methods that enable machine learning model training on data separated by two or more dimensions “confederated machine learning”, which we aim to develop in this study. We propose and evaluate confederated learning for training machine learning models to stratify the risk of several diseases among silos when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching. The confederated learning method can be intuitively understood as a distributed learning method with representation learning, generative model, imputation method and data augmentation elements. Our confederated learning method achieves AUCROC (Area Under The Curve Receiver Operating Characteristics) of 0.787 for diabetes prediction, 0.718 for psychological disorders prediction, and 0.698 for Ischemic heart disease prediction using nationwide health insurance claims. Our proposed confederated learning method successfully trained machine learning models on health insurance data separated by two or more dimensions.
IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures.
Deep Motion Prior for Weakly-Supervised Temporal Action Localization

Weakly-Supervised Temporal Action Localization (WSTAL) aims to localize actions in untrimmed videos with only video-level labels. Currently, most state-of-the-art WSTAL methods follow a Multi-Instance Learning (MIL) pipeline: producing snippet-level predictions first and then aggregating to the video-level prediction. However, we argue that existing methods have overlooked two important drawbacks: 1) inadequate use of motion information and 2) the incompatibility of prevailing cross-entropy training loss. In this paper, we analyze that the motion cues behind the optical flow features are complementary informative. Inspired by this, we propose to build a context-dependent motion prior, termed as motionness. Specifically, a motion graph is introduced to model motionness based on the local motion carrier (e.g., optical flow). In addition, to highlight more informative video snippets, a motion-guided loss is proposed to modulate the network training conditioned on motionness scores. Extensive ablation studies confirm that motionness efficaciously models action-of-interest, and the motion-guided loss leads to more accurate results. Besides, our motion-guided loss is a plug-and-play loss function and is applicable with existing WSTAL methods. Without loss of generality, based on the standard MIL pipeline, our method achieves new state-of-the-art performance on three challenging benchmarks, including THUMOS'14, ActivityNet v1.2 and v1.3.
Model Degradation Hinders Deep Graph Neural Networks

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which limits their expressive power and exploitation of deep neighborhoods. Most recent studies attribute the performance degradation of deep GNNs to the over-smoothing issue. In this paper, we disentangle the conventional graph convolution operation into two independent operations: Propagation (P) and Transformation (T). Following this, the depth of a GNN can be split into the propagation depth (Dp) and the transformation depth (Dt). Through extensive experiments, we find that the major cause for the performance degradation of deep GNNs is the model degradation issue caused by large Dt rather than the over-smoothing issue mainly caused by large Dp. Further, we present Adaptive Initial Residual (AIR), a plug-and-play module compatible with all kinds of GNN architectures, to alleviate the model degradation issue and the over-smoothing issue simultaneously. Experimental results on six real-world datasets demonstrate that GNNs equipped with AIR outperform most GNNs with shallow architectures owing to the benefits of both large DD_p$ and Dt, while the time costs associated with AIR can be ignored.