Reviewerly/exHarmony huggingface dataset:
Investigation of Early-Stage Breast Cancer Detection using Quantum Neural Network

aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN.
GeoCamera: Telling Stories in Geographic Visualizations with Camera Movements

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements.
Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks

Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.
HAT - A new corpus for experimental stylometric evaluation in Arabic

Stylometry is a research field of NLP dealing with the identification of the actual author of a piece of text.Even though it does exist several datasets for some occidental languages, it is quite difficult to find an adequate corpus in Arabic.This problem motivated us to build a natural Arabic corpus dedicated to the task of experimental stylometric evaluation.Our corpus is composed of 100 groups of Arabic texts that are extracted from different Arabic books, where the main topic is "Travel".The books are written by 100 different authors and each group contains 3 different texts that are written by the same author.We called this corpus "HAT" (Hundred of Arabic Travelers).Furthermore, we propose it as a free corpus that may represent a reference dataset for author style analysis in Arabic, which could be used for a purpose of experimental evaluation.To evaluate our dataset, we conducted some baseline experiments using an SVM classifier with a Leave-One-Out cross validation technique.
Auditing Metaverse Requires Multimodal Deep Learning

Metaverse is an integration of many different technologies like blockchain, AI, edge computing, virtual reality, and many more, simulating the physical world in a virtual environment with holograms and avatars representing individuals. The metaverse is still in the early developmental stages as various tech companies propose exciting and promising features the technology can offer. Despite the plethora of benefits, this can bring to its users, for it to be able to achieve its potential, the security of the digital infrastructure and assets needs to be carefully addressed. In this paper, we present the metaverse and the collection of technologies it comprises. We focus on the integrated auditing methodology of the metaverse in detail that requires multimodal deep learning, which incorporates blockchain-based smart contracts as well. We provide attention to identifying major security threats and vulnerabilities in the decentralized metaverse, which, in our opinion, may hinder the success of the metaverse. Finally, we conclude by proposing mitigation strategies for the discussed security threats via integrated multimodal deep learning and smart contract audit for the presented security vulnerabilities for the metaverse.
Automated Self-Supervised Learning for Recommendation

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at https://github.com/HKUDS/AutoCF.
Supervised learning of random quantum circuits via scalable neural networks

Abstract Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are built using either a universal gate set or a continuous set of rotations plus an entangling gate, and they are represented via properly designed encodings of these gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform these quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.
Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing

We propose a quantum annealing protocol that more effectively probes the dynamics of a single qubit on D-Wave's quantum annealing hardware. This protocol uses D-Wave's h-gain schedule functionality, which allows the rapid quenching of the longitudinal magnetic field at arbitrary points during the anneal. This features enables us to distinguish between open and closed system dynamics as well as the presence or absence of longitudinal magnetic field noise. We show that both thermal and magnetic field fluctuations are key sources of noise that need to be included in an open quantum system model to reproduce the output statistics of the hardware.
The development and application of the zakat collection blockchain system

Zakat in Islam has historically been a vital institution in fulfilling religious needs and social welfare. Its effectiveness in medieval and modern Islam is evident. However, this institution is seemingly not without inefficiencies. Ineffectiveness distribution process and lack of transparency in the zakat management are commonly cited as key impediments (bin Khatiman et al., 2021). Blockchain is expected to solve these issues due to its ability to record and trace every transaction, allowing administrators to discharge their accountability to zakat stakeholders. Thus, this study intended to develop a zakat collection blockchain system with the aim to provide economical and integrated continuous real-time zakat transactions, transparency and traceability by developing smart contracts in zakat management. The study employed four stages of process based on waterfall model starting from the interview session and analyse of the standard operating procedure (SOP) on zakat collection in the Zakat Collection Centre or Pusat Pungutan Zakat-Majlis Agama Islam Wilayah Persekutuan (PPZ-MAIWP). Then, design requirement specification (DRS) is employed after being reviewed and approved by PPZ-MAIWP. The results show that the development of blockchain in the zakat collection system could be eradicating extreme poverty and boost shared prosperity among the community in the country. The study contributes through a transparent and reliable environment for exchanging data and carrying out transactions through a decentralized digital ledger technology to record anything of value and as a result able to ensure transparency, reliability, trust ability and traceability of the zakat transactions.
GaPT: Gaussian Process Toolkit for Online Regression with Application to Learning Quadrotor Dynamics

Gaussian Processes (GPs) are expressive models for capturing signal statistics and expressing prediction uncertainty. As a result, the robotics community has gathered interest in leveraging these methods for inference, planning, and control. Unfortunately, despite providing a closed-form inference solution, GPs are non-parametric models that typically scale cubically with the dataset size, hence making them difficult to be used especially on onboard Size, Weight, and Power (SWaP) constrained aerial robots. In addition, the integration of popular libraries with GPs for different kernels is not trivial. In this paper, we propose GaPT, a novel toolkit that converts GPs to their state space form and performs regression in linear time. GaPT is designed to be highly compatible with several optimizers popular in robotics. We thoroughly validate the proposed approach for learning quadrotor dynamics on both single and multiple input GP settings. GaPT accurately captures the system behavior in multiple flight regimes and operating conditions, including those producing highly nonlinear effects such as aerodynamic forces and rotor interactions. Moreover, the results demonstrate the superior computational performance of GaPT compared to a classical GP inference approach on both single and multi-input settings especially when considering large number of data points, enabling real-time regression speed on embedded platforms used on SWaP-constrained aerial robots.
Enhanced and authenticated cipher block chaining mode

Due to the increased attacks on different applications, data security has become crucial. Many modes can be used to operate the advanced encryption standard (AES), some of which provide integrity, and some outperform other modes in security and simplicity. In this paper, the chain block cipher (CBC) mode has been modified to provide more security to the encrypted data by making it robust against the bit-flipping attack and adding an integrity approach using the keyedhash function. In addition, using the keyd-hash function increases the number of keys needed in CBC-AES to two keys, and this can make the proposed model more secure against bruteforce attacks and Grover’s quantum search algorithm.
EDPNet: An Encoding–Decoding Network with Pyramidal Representation for Semantic Image Segmentation

This paper proposes an encoding–decoding network with a pyramidal representation module, which will be referred to as EDPNet, and is designed for efficient semantic image segmentation. On the one hand, during the encoding process of the proposed EDPNet, the enhancement of the Xception network, i.e., Xception+ is employed as a backbone to learn the discriminative feature maps. The obtained discriminative features are then fed into the pyramidal representation module, from which the context-augmented features are learned and optimized by leveraging a multi-level feature representation and aggregation process. On the other hand, during the image restoration decoding process, the encoded semantic-rich features are progressively recovered with the assistance of a simplified skip connection mechanism, which performs channel concatenation between high-level encoded features with rich semantic information and low-level features with spatial detail information. The proposed hybrid representation employing the proposed encoding–decoding and pyramidal structures has a global-aware perception and captures fine-grained contours of various geographical objects very well with high computational efficiency. The performance of the proposed EDPNet has been compared against PSPNet, DeepLabv3, and U-Net, employing four benchmark datasets, namely eTRIMS, Cityscapes, PASCAL VOC2012, and CamVid. EDPNet acquired the highest accuracy of 83.6% and 73.8% mIoUs on eTRIMS and PASCAL VOC2012 datasets, while its accuracy on the other two datasets was comparable to that of PSPNet, DeepLabv3, and U-Net models. EDPNet achieved the highest efficiency among the compared models on all datasets.
Noisy intermediate-scale quantum computing algorithm for solving an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -vertex MaxCut problem with log( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> ) qubits

Quantum computers are devices, which allow more efficient solutions of problems as compared to their classical counterparts. As the timeline to developing a quantum-error corrected computer is unclear, the quantum computing community has dedicated much attention to developing algorithms for currently available noisy intermediate-scale quantum computers (NISQ). Thus far, within NISQ, optimization problems are one of the most commonly studied and are quite often tackled with the quantum approximate optimization algorithm (QAOA). This algorithm is best known for computing graph partitions with a maximal separation of edges (MaxCut), but can easily calculate other problems related to graphs. Here, I present a novel quantum optimization algorithm, which uses exponentially less qubits as compared to the QAOA while requiring a significantly reduced number of quantum operations to solve the MaxCut problem. Such an improved performance allowed me to partition graphs with 32 nodes on publicly available 5 qubit gate-based quantum computers without any preprocessing such as division of the graph into smaller subgraphs. These results represent a 40% increase in graph size as compared to state-of-art experiments on gate-based quantum computers such as Google Sycamore. The obtained lower bound is 54.9% on the solution for actual hardware benchmarks and 77.6% on ideal simulators of quantum computers. Furthermore, large-scale optimization problems represented by graphs of a 128 nodes are tackled with simulators of quantum computers, again without any predivision into smaller subproblems and a lower solution bound of 67.9% is achieved. The study presented here paves way to using powerful genetic optimizer in synergy with quantum computers.Received 2 March 2022Accepted 20 December 2022DOI:https://doi.org/10.1103/PhysRevResearch.5.L012021Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum Information
Red piranha optimization (RPO): a natural inspired meta-heuristic algorithm for solving complex optimization problems

An optimization algorithm is a step-by-step procedure which aims to achieve an optimum value (maximum or minimum) of an objective function. Several natural inspired meta-heuristic algorithms have been inspired to solve complex optimization problems by utilizing the potential advantages of swarm intelligence. In this paper, a new nature-inspired optimization algorithm which mimics the social hunting behavior of Red Piranha is developed, which is called Red Piranha Optimization (RPO). Although the piranha fish is famous for its extreme ferocity and thirst for blood, it sets the best examples of cooperation and organized teamwork, especially in the case of hunting or saving their eggs. The proposed RPO is established through three sequential phases, namely; (i) searching for a prey, (ii) encircling the prey, and (iii) attacking the prey. A mathematical model is provided for each phase of the proposed algorithm. RPO has salient properties such as; (i) it is very simple and easy to implement, (ii) it has a perfect ability to bypass local optima, and (iii) it can be employed for solving complex optimization problems covering different disciplines. To ensure the efficiency of the proposed RPO, it has been applied in feature selection, which is one of the important steps in solving the classification problem. Hence, recent bio-inspired optimization algorithms as well as the proposed RPO have been employed for selecting the most important features for diagnosing Covid-19. Experimental results have proven the effectiveness of the proposed RPO as it outperforms the recent bio-inspired optimization techniques according to accuracy, execution time, micro average precision, micro average recall, macro average precision, macro average recall, and f-measure calculations.
Detecting and Extracting Information of Medicines from a Medical Prescription Using Deep Learning and Computer Vision

Usually, reading any person's handwriting becomes slightly a challenging task for one. Similarly, when it comes to a doctor's handwriting in their medical prescription it becomes the most challenging task to the patients, general people and few medical related workers encountering this as an issue, in certain cases, it heads towards wrong concerns or results due to incorrect decoding of any medical prescription written by a doctor. Out of all things the main reason one cannot interpret a doctor's handwriting in their medical prescription isthat doctors use the Greek and other foreign medical terms andabbreviations that any person won't recognize or understand. This paper establishes how Long Short-Term Memory (LSTM) based Convolutional Neural Network (CNN) is used to develop a model that can distinguish doctor's handwriting in their medical prescriptions. Utilizing the Deep Convolution Recurrent Neural Network (RNN) to train this supervising model, input pictures are segmented using Otsu segmentation and handled to identify the letters and words and categorize them into the 56 various defined characters
Towards secure and reliable aggregation for Federated Learning protocols in healthcare applications

Federated Learning (FL) is an AI framework that enables collaborative and distributed training across multiple users to learn a global model while preserving the privacy of the data held locally at different sites. However, the aggregation process of FL that relies on a centralized server to update the global model parameters exposes the protocol to several vulnerabilities. Thus, the privacy and security concerns in FL systems need to be further investigated to fully leverage the capabilities of this protocol, especially in industries involving highly sensitive data, such as healthcare. As part of this study, We emphasize the security challenges in the FL systems and propose a conceptual solution for a secure and efficient FL protocol based on defensive and compression mechanisms, respectively. Our work hopes to properly highlight the susceptible adversaries and attacks that need to be considered. Furthermore, our proposal constitutes a significant step towards a reliable aggregation method specifically designed for healthcare.
Leveraging Self-Sovereign Identity in Decentralized Data Aggregation

Data aggregation has been widely implemented as an infrastructure of data-driven systems. However, a centralized data aggregation model requires a set of strong trust assumptions to ensure security and privacy. In recent years, decentralized data aggregation has become realizable based on distributed ledger technology. Nevertheless, the lack of appropriate centralized mechanisms like identity management mechanisms carries risks such as impersonation and unauthorized access. In this paper, we propose a novel decentralized data aggregation framework by leveraging self-sovereign identity, an emerging identity model, to lift the trust assumptions in centralized models and eliminate identity-related risks. Our framework formulates the aggregation protocol regarding data persistence and acquisition aspects, considering security, efficiency, flexibility, and compatibility. Furthermore, we demonstrate the applicability of our framework via a use case study where we concretize and apply our framework in a decentralized neuroscience data aggregation scenario.
Virtual reality – from technology to the art

Purpose of Article. The study is related to the consideration of virtual reality as a new trendin the development of audiovisual arts. Methodology of investigation consists in analyzing thedevelopment of virtualization technologies in their historical, technological and aesthetic aspects.Scientific Novelty of the work is that analyzes the technologies of virtual reality in theirrelationship with the development of screen audiovisual arts. Conclusions. The growing power ofcomputers and the development of software allow today to create ever more realistic virtual worldsthat blur the line between real and virtual. Virtual reality itself is becoming a new aesthetic andartistic instrument, a new reality for audiovisual arts, which requires artists to adopt newapproaches and professional mastering of this powerfu ltool to influence the audience.Key words: virtual reality, augmented reality, mixed reality, computer special effects