Reviewerly/exHarmony huggingface dataset:
Measurement error mitigation in quantum computers through classical bit-flip correction

We develop a classical bit-flip correction method to mitigate measurement errors on quantum computers. This method can be applied to any operator, any number of qubits, and any realistic bit-flip probability. We first demonstrate the successful performance of this method by correcting the noisy measurements of the ground-state energy of the longitudinal Ising model. We then generalize our results to arbitrary operators and test our method both numerically and experimentally on IBM quantum hardware. As a result, our correction method reduces the measurement error on the quantum hardware by up to one order of magnitude. We finally discuss how to pre-process the method and extend it to other errors sources beyond measurement errors. For local Hamiltonians, the overhead costs are polynomial in the number of qubits, even if multi-qubit correlations are included.
Circuit Depth Reduction for Gate-Model Quantum Computers

Quantum computers utilize the fundamentals of quantum mechanics to solve computational problems more efficiently than traditional computers. Gate-model quantum computers are fundamental to implement near-term quantum computer architectures and quantum devices. Here, a quantum algorithm is defined for the circuit depth reduction of gate-model quantum computers. The proposed solution evaluates the reduced time complexity equivalent of a reference quantum circuit. We prove the complexity of the quantum algorithm and the achievable reduction in circuit depth. The method provides a tractable solution to reduce the time complexity and physical layer costs of quantum computers.
Image encryption application in a system for compounding self-excited and hidden attractors

Nowadays, chaos-based encryption systems implemented by hardware attracted highly attention. While chaotic systems, encryption algorithms and hardware platform are the core of the chaos-based image encryption system. In this paper, an image encryption system is developed. The proposed image encryption scheme employs a multi-scroll chaotic system and the Arnold map as entropy sources. The images are preprocessed by XOR operation between chaotic sequences and image pixels; Arnold map is used to change the position of the processed image pixels for scrambling the image. The chaotic sequences are generated by the multi-scroll chaotic system which is constructed by introducing Piecewise (PW) functions into Lorenz-like system. The modified Lorenz-like system produces a novel composite chaotic attractor consisting of a pair of different magnitude self-excited attractors and a pair of different magnitude hidden attractors. Through adjusting the parameters of PW functions, the system can also produce novel grid multi-scroll attractors. The hardware implementation of the image encryption scheme has been realized by using FPGA XC7A35TCPG236 and VGA monitor. The proposed encryption system consumes low power (248 mW) and has high throughput (4 bit/us) with an encryption time of 237 ms for encrypting a 256 × 256 plain image. Then, some basically tests are proved our image encryption system effectively.
Aid, Charity and Donation Tracking System Using Blockchain

Blockchain is a promising technology and is becoming predominant for solving many problems in the field related to security under the control of both public and private sectors. Blockchain is gaining popularity within the domain of charity. Due to lack of transparency in the transactions involved in Donations the donor(s) are not able to know whether their donations are being utilized properly, which has made people lose trust in Charities. The paper proposes a Blockchain based Decentralized Donation tracking system built on Ethereum Blockchain which will provide full transparency, accountability and direct reach to the intended recipients.
Production capacity prediction of hydropower industries for energy optimization: Evidence based on novel extreme learning machine integrating Monte Carlo

As the basic industry of national economic development, the electric power industry is closely associated with the overall economic development and social progress. Hydropower, as a power generation form, has fewer data samples than thermal power generation, which leads to a difficult problem to establish an accurate production and energy prediction model. Therefore, a novel production capacity prediction model using extreme learning machine based on Monte Carlo algorithm is presented for energy optimization and saving. Through using the Monte Carlo algorithm, the small sample data can be expanded. Then, the expansion of the small sample data is utilized as the training set and the testing set for the extreme learning machine to predict the production capacity and optimize the energy configuration. Finally, the proposed method is used to predict the production of a hydropower plant for improving the energy efficiency. Compared with the traditional extreme learning machine, the correctness and the applicability of the proposed method are proved. Moreover, the energy optimal configuration of the hydropower industry production can improve the energy efficiency and save the energy of hydropower industrial processes.
Efficient Mining Cluster Selection for Blockchain-Based Cellular V2X Communications

Cellular vehicle-to-everything (V2X) communication is expected to herald the age of autonomous vehicles in the coming years. With the integration of blockchain in such networks, information of all granularity levels, from complete blocks to individual transactions, would be accessible to vehicles at any time. Specifically, the blockchain technology is expected to improve the security, immutability, and decentralization of cellular V2X communication through smart contract and distributed ledgers. Although blockchain-based cellular V2X networks hold promise, many challenges need to be addressed to enable the future interoperability and accessibility of such large-scale platforms. One such challenge is the offloading of mining tasks in cellular V2X networks. While transportation authorities may try to balance the network mining load, the vehicles may select the nearest mining clusters to offload a task. This may cause congestion and disproportionate use of vehicular network resources. To address this issue, we propose a game-theoretic approach for balancing the load at mining clusters while maintaining fairness among offloading vehicles. Keeping in mind the low-latency requirements of vehicles, we consider a finite channel blocklength transmission which is more practical compared to the use of infinite blocklength codes. The simulation results obtained with our proposed offloading framework show improved performance over the conventional nearest mining cluster selection technique.
Novelty detection in continuously changing environments

Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
Artificial Neural Network Based Ensemble Approach for Multicultural Facial Expressions Analysis

Facial expressions convey exhaustive information about human emotions and the most interactive way of social collaborations, despite differences in ethnicity, culture, and geography.Due to cultural differences, the variations in facial structure, facial appearance, and facial expression representation are the main challenges to the facial expression recognition system.These variations necessitate the need for multicultural facial expression analysis.This study presents several computational algorithms to handle these variations to get high expression recognition accuracy.We propose an artificial neural network-based ensemble classifier for multicultural facial expression analysis.The facial images from the Japanese female facial expression database, Taiwanese facial expression image database, and RadBoud faces database are combined to form a multi-culture facial expression dataset.The participants in the multicultural dataset originate from four ethnic regions including Japanese, Taiwanese, Caucasians, and Moroccans.Local binary pattern, uniform local binary pattern, and principal component analysis are applied for facial feature representation.Experimental results prove that facial expressions are innate and universal across all cultures with minor variations.
Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper.
Stimulus classification using chimera-like states in a spiking neural network

A complex network of bistable Hodgkin-Huxley (HH) neurons with excitatory coupling can exhibit a partially spiking chimera behavior. We propose to use this chimera-like state for classification of the entering stimulus amplitude in the neural network with coexisting resting and spiking states. Due to different additive noise applied to each neuron in the network, the neurons are nonidentical. Therefore, depending on the amplitude of the external current, a part of the neurons stays in the resting state, while another part oscillates. Keeping fixed the coupling strength between neurons inside the network, we train the neural network on external pulses with two different amplitudes to adjust the coupling strength between the network neurons and two output neurons. We consider two variants of the classifier, in the presence and in the absence of inhibitory coupling between output neurons, and study how the output neurons respond to the external pulses of different amplitudes. The accuracy of the proposed classifier reaches 100% when the output neurons are inhibitory coupled, so that only one of these neurons is activated.
Novel embedding secrecy within images utilizing an improved interpolation-based reversible data hiding scheme

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution.
Fret: Functional Reinforced Transformer With BERT for Code Summarization

Code summarization has long been viewed as a challenge in software engineering because of the difficulties of understanding source code and generating natural language. Some mainstream methods combine abstract syntax trees with language models to capture the structural information of the source code and generate relatively satisfactory comments. However, these methods are still deficient in code understanding and limited by the long dependency problem. In this paper, we propose a novel model called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fret</b> , which stands for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> unctional <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</b> inforced <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ransformer with BERT. The model provides a new way to generate code comments by learning code functionalities and deepening code understanding while alleviating the problem of long dependency. For this purpose, a novel reinforcer is proposed for learning the functional contents of code so that more accurate summaries to describe the code functionalities can be generated. In addition, a more efficient algorithm is newly designed to capture the source code structure. The experimental results show that the effectiveness of our model is remarkable. Fret significantly outperforms all the state-of-the-art methods we examine. It pushes the BLEU-4 score to 24.32 for Java code summarization (14.23% absolute improvement) and the ROUGE-L score to 40.12 for Python. An ablation test is also conducted to further explore the impact of each component of our method.
Cortical surface registration using unsupervised learning

Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
The importance of influential users in (re)producing Swedish far-right discourse on Twitter

Using a combination of descriptive statistics, sentiment analysis and close readings of a collection of 74,336 Swedish tweets, this article explores the platform usage patterns of users who are influential in a Swedish far-right discourse on Twitter and how these users help to (re)produce far-right discourse. Specifically, it focuses on their use of platform functions and on language use. The analysis shows that influential users have a narrow focus in terms of the content they post and how they profile themselves. They are highly active, have more followers and produce more original content than other users. Surprisingly, while previous research has found that emotionally charged tweets are retweeted more and that highly popular and influential Twitter users tend to express more emotion while tweeting, influential users in this dataset often posted far-right content concealed as neutral, factual statements. This use of seemingly neutral language creates an inclusive far-right context, lets influential users evade responsibility for their content as well as facilitates more overtly hateful interpretations.
Predicting phase and sensing phase coherence in chaotic systems with machine learning

Recent interest in exploiting machine learning for model-free prediction of chaotic systems focused on the time evolution of the dynamical variables of the system as a whole, which include both amplitude and phase. In particular, in the framework based on reservoir computing, the prediction horizon as determined by the largest Lyapunov exponent is often short, typically about five or six Lyapunov times that contain approximately equal number of oscillation cycles of the system. There are situations in the real world where the phase information is important, such as the ups and downs of species populations in ecology, the polarity of a voltage variable in an electronic circuit, and the concentration of certain chemical above or below the average. Using classic chaotic oscillators and a chaotic food-web system from ecology as examples, we demonstrate that reservoir computing can be exploited for long-term prediction of the phase of chaotic oscillators. The typical prediction horizon can be orders of magnitude longer than that with predicting the entire variable, for which we provide a physical understanding. We also demonstrate that a properly designed reservoir computing machine can reliably sense phase synchronization between a pair of coupled chaotic oscillators with implications to the design of the parallel reservoir scheme for predicting large chaotic systems.
Few-shot activity recognition with cross-modal memory network

Deep learning based action recognition methods require large amount of labelled training data. However, labelling large-scale video data is time consuming and tedious. In this paper, we consider a more challenging few-shot action recognition problem where the training samples are few and rare. To solve this problem, memory network has been designed to use an external memory to remember the experience learned in training and then apply it to few-shot prediction during testing. However, existing memory-based methods just update the visual information with fixed label embeddings in the memory, which cannot adapt well to novel activities during testing. To alleviate the issue, we propose a novel end-to-end cross-modal memory network for few-shot activity recognition. Specifically, the proposed memory architecture stores the dynamic visual and textual semantics for some high-level attributes related to human activities. And the learned memory can provide effective multi-modal information for new activity recognition in the testing stage. Extensive experimental results on two video datasets, including HMDB51 and UCF101, indicate that our method could achieve significant improvements over other previous methods.
Serverless Blockchain-Enabled Architecture for IoT Societal Applications

IoT-enabled applications, such as cloud manufacturing, guided water quality or air quality analysis, energy-conscious societal applications, and smart agricultural economics, are designed using a blend of high-end computing technologies, such as cloud, edge, and fog. Smart cities and governmental authorities keep a keen eye out for implementing IoT applications in an automated/decentralized approach with enhanced security measures so that tens of thousands of users, including entrepreneurs, are benefited. Existing IoT architectures are prone to energy inefficiency or resource underutilization problems due to the avoidance of apt technologies, such as serverless computing. This article proposes to set forth a serverless blockchain-enabled IoT architecture for societal applications. It explores the existing IoT architectures and pinpoints the advantages of applying serverless blockchains on IoT architectures. In addition, the proposed IoT architecture is illustrated with a specific use case of IoT societal applications namely air quality monitoring for smart cities (AQMS). This article discloses how air quality sensor data from defective industries were securely transacted to blockchain networks surpassing from the three levels of computing namely edge, fog, and cloud while utilizing serverless and server-oriented functions. In addition, this article exposes a list of the most potent serverless functions that assist AQMS IoT societal applications in detail. The IoT architecture, discussed in this article, will enable innovations and research works for IoT developers and researchers.
Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.