Reviewerly/exHarmony huggingface dataset:
Novel Neural Network for Breast Cancer Diagnosis

The second biggest cause of mortality for women worldwide is Breast Cancer (BC). BC diagnosis by hand using histological breast pictures is expensive, time-consuming, and non-generalizable. Using a CNN to directly learn features from entire slide images is an alternative way for feature extraction. A significant number of labelled images, which can occasionally be challenging to get, are necessary for training the CNN. Reusing a pre-trained CNN model for feature attainment with huge image datasets from other disciplines is the solution. The BreakHis dataset contains images of BC histology, and in this article, we provide a “Novel CNN” architecture using Transfer Learning for identifying those images. This model's binary classification-benign and malignant-allows it to quickly and accurately diagnose breast cancer. In the suggested framework, DenseNet-201 pre-trained model is used to attain features from the histopathological pictures. Then, to generate a reliable hybrid model, the attained features are applied into the Global Average Pooling Layer, followed by Dropout, Batch-Normalization, and Dense Layers. The proposed model had a 99.75% accuracy rate. These encouraging findings will open the door to utilize this model as an automated tool to help clinicians diagnose breast cancer and may improve the survival rate for the disease.
A Specific Emitter Identification Approach Based on Multi-Head Attention Mechanism

Specific emitter identification (SEI) is the process of identifying a unique emitter based on the internal features carried by the received signal, which has been widely used in spectrum management. In this paper, we consider the influence of modulation mode and power amplifier (PA) on radio frequency fingerprint (RFF) and propose an identification scheme based on squeeze-and-excitation network (SENet) and multi-head attention mechanism (MHA). The proposed scheme can use the abundant information of signal nonlinear characteristics for identifying emitters. Different from most existing SEI schemes based on feature transformation, our scheme can learn the feature from time domain signal, which can extract the overall characteristics hidden in the received signal. We evaluate the proposed algorithm through extensive simulation experiments, discuss the impact of frequency offset, the number of emitters, and channel conditions on the identification performance, and compare the proposed scheme with several advanced deep learning network architectures to prove its effectiveness.
DeHiREC: Detecting Hidden Voice Recorders via ADC Electromagnetic Radiation

Unauthorized covert voice recording brings a remarkable threat to privacy-sensitive scenarios, such as confidential meetings and private conversations. Due to the miniaturization and disguise characteristics, hidden voice recorders are difficult to be noticed in their surroundings. In this paper, we present DeHiREC, the first proof-of-concept system that can detect offline hidden voice recorders from their electromagnetic radiations (EMR). We first characterize the unique patterns of the emanated EMR signals and then locate the EMR source, i.e., the analog-to-digital converter (ADC) module embedded in the mixed signal system-on-chips (MSoCs). Since these unintentional EMR signals can be extremely noisy and weak, accurately detecting them can be challenging. To address this challenge, we first design an EMR Catalyzing method to stimulate the EMR signals actively and then employ an adaptive-folding algorithm to improve the signal-to-noise ratio (SNR) of the sensed EMRs. Once the sensed EMR variation corresponds to our active stimulation, we can determine that there exists a hidden voice recorder. We evaluate the performance of DeHiREC on 13 commercial voice recorders under various impacts, including interference from other devices. Experimental results reveal that DeHiREC is effective in detecting all 13 voice recorders and achieves an overall success rate of 92.17% and a recall rate of 86.14% at a distance of 0.2 m.
Why cyber insurance has an image problem

For many organisations, cyber insurance is a tricky subject. The cost can be a disincentive, the process of insuring can be complex and it seems there is an increasing list of circumstances under which insurers will not pay out. But given the threats that all organisations face, can you really risk not being insured?
Semantic domain comparison of research keywords by indicator-based fuzzy distances: A new prospect

Assessing the similarity of scientific outputs based on an indicator has not been addressed much so far. The topic, however, may find several potential applications which can help enrich procedures of ranking, research monitoring, and scientific policy-making. The present study offers a new method to quantify such similarities based on keyword co-occurrence matrices. In the proposed method, first, the keyword co-occurrence networks are transformed into their associated newly defined fuzzy sets, named as scientosemantic domains. Then, a fuzzy distance between the two domains is found based on an arbitrary indicator. In this paper, the three indicators of frequency, development and investment appeal are used. The proposed method is implemented for five types of concept comparison. For each type, concepts are represented by a canonical keyword with different field codes. Scientosemantic domains of concepts are sourced out of bibliometric data obtained from appropriate queries on SCOPUS. Number of keywords used to define scientosemantic domains ranges from about 30 to 800. Since indicator-based comparison of scientosemantic domains are not dealt with in the literature, the obtained distances between concepts are verified by qualitative and expert evaluations. For all cases, frequency- and development-based distances are less than those for investment appeal; while crisp distances for the latter extend beyond 0.6, the former does not exceed 0.3. The greatest distances are observed for investment appeal in technology-related keywords.
A Generalization of NTRUEncrypt

Abstract The main purpose of this chapter is to give a more general construction of NTRU based on ideal matrices and q -ary lattice theory. To understand our construction, first we discuss a more general form of the ordinary cyclic code, namely $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> -cyclic code, which firstly appeared in (Lopez-Permouth et al., 2009; Shi et al., 2020); thus, we give a more generalized NTRUEncrypt from replacing finite field by real number field $$\mathbb {R}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>R</mml:mi> </mml:math> .
Central Bank Digital Currency Cross-Border Payment Model Based on Blockchain Technology

Abstract Since the turn of the twenty-first century, the growth of the globalized economy and trade has accelerated, and the cross-border payment system, which is an essential component of the international financial infrastructure, has played a significant role in the global economy and trade. However, traditional cross-border payments present risks and challenges, such as expensive processing fees, limited payment efficiency, information asymmetry in the trade process, and reliance on a highly centralized cross-border payment system. This chapter is based on consortium blockchain technology and utilizes Polkadot’s Parachain, Relay chain, and cross-chain technologies as references; a scalable, high-efficiency, high-security, and privacy-protecting central bank digital currency cross-border payment model is designed. Analyzed the usage of hash digest technology and CoinJoin technology to avoid the tracing of transactions in order to protect privacy. The issuance of multi-country central bank digital currency or stablecoin anchored to a basket of fiat currencies is discussed as the currency in circulation in the model. Finally, the central bank digital currency cross-border payment development trend is summarized and forecasted.
On the High Dimensional RSA Algorithm—A Public Key Cryptosystem Based on Lattice and Algebraic Number Theory

Abstract The most known public key cryptosystem was introduced in 1978 by Rivest et al. (1978) and is now called the RSA public key cryptosystem in their honor. Later, a few authors gave a simple extension of RSA over algebraic numbers field (see Takagi and Naito (2015), Uematsu et al. (1985, 1986)), but they require that the ring of algebraic integers is Euclidean ring, and this requirement is much more stronger than the class number one condition. In this chapter, we introduce a high dimensional form of RSA by making use of the ring of algebraic integers of an algebraic number field and the lattice theory. We give an attainable algorithm (see Algorithm 1) which is significant both from the theoretical and practical point of view. Our main purpose in this chapter is to show that the high dimensional RSA is a lattice based on public key cryptosystem indeed, of which would be considered as a new number in the family of post-quantum cryptography (see Peikert (2014), Pradhanet al. (2019)). On the other hand, we give a matrix expression for any algebraic number fields (see Theorem 2), which is a new result even in the sense of classical algebraic number theory.
On the LWE Cryptosystem with More General Disturbance

Abstract The main purpose of this chapter is to give an extension on learning with errors problem (LWE)-based cryptosystem about the probability of decryption error with more general disturbance. In the first section, we introduce the LWE cryptosystem with its application and some previous research results. Then we give a more precise estimation probability of decryption error based on independent identical Gaussian disturbances and any general independent identical disturbances. This upper bound probability could be closed to 0 if we choose applicable parameters. It means that the probability of decryption error for the cryptosystem could be sufficiently small. So we verify our core result that the LWE-based cryptosystem could have high security.
Canonical mean filter for almost zero-shot multi-task classification

The support set plays a key role in providing conditional prior for fast adapting the feature extractors in few-shot tasks. The representative few-shot method CNAPs used a simple conditional feature encoder to extract the prior, which was a conditional feature of the task-specific support set. However, the strict form required by the support set makes its construction difficult in practical applications. Motivated by ANIL, we first investigate the role of the adaption in CNAPs by designing Almost Zero-Shot (AZS) tasks in which a fixed support set is used across all tasks instead of the task-specific support set. The AZS experimental results indicate that the adaptation contributes little to the feature extractor. Nevertheless, simply removing the adaptation from CNAPs will cause severe performance drop on some sub-datasets of the Meta-Dataset. To alleviate this problem, we further proposed a Canonical Mean Filter (CMF) module that maps any support set to a canonical form. The CMF yields a stable conditional feature, and hence allows for CNAPs to perform well without the conditional feature encoder and the parameter adaptation network at the test stage. CNAPs embedded with CMF significantly outperform the vanilla CNAPs for the AZS tasks, and even achieve comparable performance to one-shot tasks but with 40.48% parameter reduction and without task-specific support sets at the test stage.
Prediction and identification of nonlinear dynamical systems using machine learning approaches

Nonlinear dynamical systems are widely implemented in many areas. The Prediction and identification of these dynamical systems purely based on observational data is of great significance for practical applications. In the work, we develop a machine learning based approach called Runge–Kutta guided next-generation reservoir computing (RKNG-RC). The proposed scheme can process data information generated by the most complicated nonlinear dynamical systems such as chaotic Lorenz63 system even with noise, and experimental systems such as chaotic Chua’s electronic circuit, showing an outstanding ability for prediction tasks. More importantly, the RKNG-RC is found to have distinctive interpretability that from the trained weights the ordinary differential equation governing the observable data can be deduced, which is beyond the processing capacities of traditional approaches. The work provides an efficient platform for processing information generated by various dynamical systems.
Overcoming Deceptive Rewards with Quality-Diversity

Quality-Diversity offers powerful ideas to create diverse, high-performing populations. Here, we investigate the capabilities these ideas hold to solve exploration-hard single-objective problems, in addition to creating diverse high-performing populations.
Theoretical Limits on the Success of Lexicase Selection Under Contradictory Objectives

Lexicase selection is a state of the art parent selection technique for problems that can be broken down into multiple selection criteria. Prior work has found cases where lexicase selection fails to find a Pareto-optimal solution due to the presence of multiple objectives that contradict each other. In other cases, however, lexicase selection has performed well despite the presence of such objectives. Here, we develop theory identifying circumstances under which lexicase selection will or will not fail to find a Pareto-optimal solution. Ultimately, we find that lexicase selection can perform well under many circumstances involving contradictory objectives, but that there are limits to the parameter spaces where high performance is possible. Additionally, we show empirical evidence that epsilon-lexicase selection is much more strongly impacted by contradictory objectives. Our results inform parameter value decisions under lexicase selection and decisions about which problems to use lexicase selection for.
A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems

The drive for smarter, greener, and more livable cities has led to research towards more effective solar energy forecasting techniques and their integration into traditional power systems. However, the availability of real-time data, data storage, and monitoring has become challenging. This research investigates a method based on Bi-directional LSTM (BDLSTM) neural network. BDLSTM takes into account the data’s past and future context. The future hidden layer takes input in ascending order while the past hidden layer evaluates the input in decreasing order, making BDLSTM relevant in analyzing the input data’s past context and evaluating future predictions. The eleven-year (2010–2020) weather dataset used for this paper was acquired from NASA. Two pre-processing approaches, Automatic Time Series Decomposition (ATSD) and Pearson correlation, were used to remove the noisy values from the residual components and for feature selection, respectively. To ensure storage and reuse of data, the architecture includes a cloud-based server for data management and reuse for future predictions. Popular in multi-energy systems, the cloud-based server also serves as a platform for monitoring predicted solar energy data. The metrics values and results obtained have demonstrated that the BDLSTM performs efficiently on the available data. Data from two separate climatic horizons proved the study’s quality and reliability.
A Novel Quadtree-Based Genetic Programming Search for Searchable Encryption Optimization

The encoding method of a searchable encryption can significantly impact the performance of a location-based alert system. While there were attempts to design searchable encryption manually, Gray Encoding is considered the most preferable method. However, if the alert zones are scattered unevenly, Gray Encoding fails to achieve token aggregation. In this research, a novel Quadtree-based Genetic Programming (Quadtree-GP) is proposed to iteratively identify superior searchable encryption candidates for the location-based alert system. Quadtree-GP can be effectively applied on customized requirements and different grid maps. Extensive experimental results show that Quadtree-GP is able to find searchable encryption candidates that outperform GP search, random search, and the baseline Gray Encoding in terms of user response time, token remaining percentage, and execution time.
Transfer Learning for Embodied Neuroevolution

Transfer Learning (TL) has been widely used in machine learning where the neuronal layers in a learned source Artificial Neural Network (ANN) are transferred to a target ANN so as to speed up the latter's learning. TL most often requires that the source and target domains are similar. However, its use in dissimilar domains as also in ANNs that use neuroevolution strategies has hardly been investigated. In this paper, we present a mechanism, suited for neuroevolution, that can identify specific neurons that need to be transferred. These Hot neurons from the source ANN, when transferred to the target ANN, helps in hastening the learning at the target. Simulations conducted using robots, clearly indicate that the mechanism is well suited for both similar and dissimilar tasks or environments.
Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite

This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated. The empirical analysis is conducted on the SBOX-COST benchmarking test suite, where bound constraints are enforced on the problem domain. This analysis provides some insights that might be useful in designing adaptive strategies for handling such constraints.
Exploring the Frequency of Boundary Control Methods Activation in Metaheuristic Algorithms

Recently, Boundary Control Methods (BCMs) have become increasingly relevant in the field of metaheuristic algorithms. In this study, we investigate the relationship between the activation frequency of different BCMs and the problem's dimensionality. Additionally, we analyze each problem dimension independently. Our research primarily concentrates on the top three algorithms from the IEEE CEC 2020 competition: AGSK, IMODE, and j2020, utilizing the competition benchmark set to conduct experiments. Our findings provide valuable insights into the metaheuristic domain, underlining the significance of comprehending BCM activation patterns to improve algorithm design and benchmarking practices.
Encoding the At-Most-One Constraint for QUBO and Quantum Annealing: Experiments with the N-Queens problem

We present experiments in solving constrained optimization and constraint satisfaction problems by means of Quantum Annealing in terms of QUBO (Quadratic Unconstrained Binary Optimization). We investigate different QUBO formulations for the encoding of integers into Booleans and for the encoding of the At-Most-One constraint, which is used in combinatorial problems requiring capacity constraints. We compare four different QUBO models and report experiments done on the D-Wave annealing computer and the "quantum-inspired" Fixstars Amplify Annealing Engine. For this, we consider a simple example taken from the domain of Constraint Satisfaction Problems (CSP), the N-queens problem, as its formulation involves multiple instances of the At-Most-One constraints.
Evaluation of Algorithms from the Nevergrad Toolbox on the Strictly Box-Constrained SBOX-COST Benchmarking Suite

In this paper we report the benchmarking results of four algorithms on the Strict Box-Constraint Optimization Studies (SBOX-COST) benchmarking suite which, as the name states, employs strict box constraints. These results are compared to the benchmarking results on the Black-box Optimization Benchmarking (BBOB) suite which serves as basis for the SBOX-COST suite, but is less restrivtive with respect to box constraints. SBOX-COST enforces its box constraints by returning an invalid value (∞) whenever a point outside of the bounds is evaluated. We use the following four algorithms from the Nevergrad Toolbox: Estimation of Multivariate Normal Algorithm (EMNA), Differential Evolution (DE), Constrained Optimization BY Linear Approximation (Cobyla) and Particle Swarm Optimization (PSO). All algorithms are employed without parameter tuning. Generally, all algorithms perform quite similiar on both suites but we notice a slight advantage for the algorithms that are allowed to evaluate the functions outside of the bounds. The effect of the advantage varies very much with function ID and in some cases, an algorithm's performance on a problem from the SBOX-COST suite is even better than on the corresponding problem from the BBOB suite.
Solving Scheduling Problems with Quantum Computing: a Study on Flexible Open Shop

Despite quantum computing is revealing an increasingly promising technology that has the potential to introduce a significant speed-up in many areas of computation, the number of problems that it can represent and solve is currently rather limited. Therefore, one of the current challenges faced by the quantum computing community is to broaden the class of problems that can be tackled. Among these problems, scheduling problems are a class of particularly interesting and hard combinatorial problems; in this paper, we present a novel solution for representing and solving the Flexible Open Shop Scheduling Problem (FOSSP) to optimality by minimizing the makespan. We firstly present a compact formulation of this problem as a Quadratic unconstrained binary optimization (QUBO), which can be used to solve this problem with a quantum annealer. Then, we proceed to the Quantum Approximate Optimization Algorithm (QAOA) problem formulation, thus producing both the cost and mix Hamiltonians related to the problem. From the Hamiltonians, we provide the complete description of the quantum circuit that can be used to tackle the FOSSP within the QAOA framework. This second approach can be used to solve the optimization problem with a general-purpose quantum gate-based hardware.
Evolution of Editing Scripts From Examples

Experienced programmers use editing scripts to effectively modify selected lines and columns in a file or a set of files according to a desired editing transformation. For many non-programmers, it is, however, out of their skill sets to develop such scripts. We propose a software tool that significantly simplifies this task. The user is asked to create a snippet of the file before and after the desired edits (input and output example). The proposed tool uses these examples to evolve an editing script which is then executed on all lines of the input files to perform the expected transformation. We developed a simple programming language for file edits and a genetic programming-based system capable of evolving scripts in this language. For typical source files in which some data has to be deleted, added, or modified, the proposed system can evolve a valid script performing desired transformations in order of seconds or minutes.