Reviewerly/exHarmony huggingface dataset:
The Subfield and Extended Codes of a Subclass of Optimal Three-Weight Cyclic Codes

Abstract A class of optimal three-weight $$[q^k-1,k+1,q^{k-1}(q-1)-1]$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>[</mml:mo> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>k</mml:mi> </mml:msup> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:msup> <mml:mi>q</mml:mi> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>q</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> </mml:math> cyclic codes over $${\mathrm{I\!F}}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> <mml:mspace /> <mml:mi>F</mml:mi> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> , with $$k\ge 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , achieving the Griesmer bound, was presented by Heng and Yue (IEEE Trans Inf Theory 62(8):4501–4513, 2016. https://doi.org/10.1109/TIT.2016.2550029). In this paper we study some of the subfield codes of this class of optimal cyclic codes when $$k=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> . The weight distributions of the subfield codes are settled. It turns out that some of these codes are optimal and others have the best known parameters. The duals of the subfield codes are also investigated and found to be almost optimal with respect to the sphere-packing bound. In addition, the covering structure for the studied subfield codes is determined. Some of these codes are found to have the important property that any nonzero codeword is minimal, which is a desirable property that is useful in the design of a secret sharing scheme based on a linear code. Moreover, a specific example of a secret sharing scheme based on one of these subfield codes is given. Finally, a class of optimal two-weight linear codes over $${\mathrm{I\!F}}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>I</mml:mi> <mml:mspace /> <mml:mi>F</mml:mi> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> , achieving the Griesmer bound, whose duals are almost optimal with respect to the sphere-packing bound is presented. Through a different approach, this class of optimal two-weight linear codes was reported very recently by Heng (IEEE Trans Inf Theory 69(2):978–994, 2023. https://doi.org/10.1109/TIT.2022.3203380). Furthermore, it is shown that these optimal codes can be used to construct strongly regular graphs.
How to frame innovation in mathematics

Abstract We discuss conceptual change and progress within mathematics, in particular how tools, structural concepts and representations are transferred between fields that appear to be unconnected or remote from each other. The theoretical background is provided by the frame concept, which is used in linguistics, cognitive science and artificial intelligence to model how explicitly given information is combined with expectations deriving from background knowledge. In mathematical proofs, we distinguish two kinds of frames, namely structural frames and ontological frames. The interaction between both kinds of frames can drive mathematical interpretation. We first discuss two examples where structural frames (formulaic notation) drive ontological development (the discovery or exploration of mathematical objects). The development of Boole’s Boolean algebra may at first appear as a metaphorical treatment of the (then) new area of logic. In the analysis, we discuss how different (aspects of) certain algebraic frames change in the transfer, how arising difficulties are solved and overall argue that Boole uses the numerical algebra frame as a research template for the discovery of a system for calculations in logic. Following Ifrah, we analyse the discovery of zero as an extension to the number ontology as driven by the development of notation. Both structural and ontological frames are extended and simplified as notation progresses. Finally, we discuss two examples from infinite combinatorics, viz. topological graph theory, and one foundational issue. In both examples, the two simultaneous frames about one object are maintained independently. They motivate different research questions, but may also fruitfully interact: shifting between multiple synchronously maintained perspectives acts as a motor of innovation. The analysis shows how a frame-based approach allows to model how different perspectives drive mathematical innovation because they highlight different aspects, questions and heuristics.
Learning smooth dendrite morphological neurons by stochastic gradient descent for pattern classification

This article presents a learning algorithm for dendrite morphological neurons (DMN) based on stochastic gradient descent (SGD). In particular, we focus on a DMN topology that comprises spherical dendrites, smooth maximum activation function nodes, and a softmax output layer, whose original learning algorithm is performed in two independent stages: (1) dendrites' centroids are learned by k-means, and (2) softmax layer weights are adjusted by gradient descent. A drawback of this learning method is that both stages are unplugged; once dendrites' centroids are defined, they keep static during weights learning, so no feedback is performed to correct the dendrites' positions to improve classification performance. To overcome this issue, we derive the delta rules for adjusting the dendrites' centroids and the output layer weights by minimizing the cross-entropy loss function under an SGD scheme. This gradient descent-based learning is feasible because the smooth maximum activation function that interfaces the dendrite units with the output layer is differentiable. The proposed DMN is compared against eight morphological neuron models with distinct topologies and learning methods and four well-established classifiers: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF), and k-nearest neighbors (k-NN). Besides, the classification performance is evaluated on 81 datasets. The experimental results show that the proposed method tends to outperform the DMN methods and is competitive or even better than SVM, MLP, RF, and k-NN. Thus, it is an alternative approach that can effectively be used for pattern classification. Moreover, SGD for DMN learning standardizes this neural model, like current artificial neural networks.
Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.
Broadband bandpass Purcell filter for circuit quantum electrodynamics

In circuit quantum electrodynamics, qubits are typically measured using dispersively coupled readout resonators. Coupling between each readout resonator and its electrical environment, however, reduces the qubit lifetime via the Purcell effect. Inserting a Purcell filter counters this effect while maintaining high readout fidelity but reduces measurement bandwidth and, thus, limits multiplexing readout capacity. In this Letter, we develop and implement a multi-stage bandpass Purcell filter that yields better qubit protection while simultaneously increasing measurement bandwidth and multiplexed capacity. We report on the experimental performance of our transmission-line-based implementation of this approach, a flexible design that can easily be integrated with current scaled-up, long coherence time superconducting quantum processors.
Image Haziness Contrast Metric Describing Optical Scattering Depth

Contrast is not uniquely defined in the literature. There is a need for a contrast measure that scales linearly and monotonically with the optical scattering depth of a translucent scattering layer that covers an object. Here, we address this issue by proposing an image contrast metric, which we call the Haziness contrast metric. In its essence, the Haziness contrast compares normalized histograms of multiple blocks of the image, a pair at a time. Subsequently, we test several prominent contrast metrics in the literature, as well as the new one, by using milk as a scattering medium in front of an object to simulate a decline in image contrast. Compared to other contrast metrics, the Haziness contrast metric is monotonic and close to linear for increasing density of the scattering material, compared with other metrics in the literature. The Haziness contrast has a wider dynamic range, and it correctly predicts the order of scattering depth for all the channels in the RGB image. Utilization of the metric to evaluate the performance assessment of dehazing algorithms is also suggested.
The Espouse of Women in the Online Romance Fraud World: Role of Sociocultural Experiences and Digital Technologies

ABSTRACTOnline romance fraud is a form of cybercrime that has pervasive consequence on both individuals and organizations. As against previous studies that predominantly focus on male scammers, this study examines the influence of sociocultural experiences and technological modalities on women’s involvement in online romance fraud in Tamale, Ghana. The study employs a blend of feminist criminology and Cressey’s Fraud Triangle Theory to gain a more comprehensive understanding of the engagement of women in the scamming phenomenon in this region. Employing the experiences of fifteen (15) female scammers, the results show that sociocultural backgrounds of female scammers, combined with the internet’s inherent opportunities, play a pivotal role in determining the engagement. The sociocultural background of women, coupled with the technological nature of the act, plays a significant role in driving their engagement in these illicit activities: motivation to offend and opportunity through social relationships. This study emphasizes how technological pathways enhance the opportunities available to women, highlighting the ways in which the digital landscape of such frauds potentially allows women to evade legal and societal repercussions. In conclusion, this study highlights the need for a comprehensive approach that considers both the technological aspects and the underlying societal constructs that facilitate them. Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 United Nations (Citation2022). World population prospects 2022. Retrieved from: https://population.un.org/wpp/.Additional informationFundingThis work was supported by the Narodowa Agencja Wymiany Akademickiej.Notes on contributorsYushawu AbubakariAbubakari Yushawu is a PhD researcher at the Institute of Sociology of the University of Wroclaw, Poland. The author is also currently affiliated to the Department of Social and Cultural Anthropology at the KU Leuven and a research fellow at the African Center for Cyberlaw and Cybercrime Prevention (ACCP). His research interest includes sociological theories, social inequalities, deviant and crime studies, criminological theories, and qualitative research methods.
Distributed scalability tuning for evolutionary sharding optimization with Random-equivalent security in permissionless Blockchain

In spite of multiple advantages of the adoption of blockchain (BC), it still faces some integration challenges in modern applications, such as the Internet of Things. These challenges include low throughput rates in permissionless settings. To solve this challenge, several state-of-the-art works proposed sharding (i.e., partitioning) the BC infrastructure. Sharding the network into smaller shards improves the total system throughput, regardless of the node-shard assignment criteria. Most previous work applied a Random Sharding (RS) approach, i.e., randomly allocating nodes into predefined shards, to satisfy the required unpredictability property of node-shard allocation. In this paper, we propose a Blockchain Optimized and Secure Sharding (BOSS) protocol that aims to optimize the node-shard allocation resulting in increased throughput using a variant of the evolutionary Genetic Algorithm. The RS-equivalent levels of security and unpredictability are guaranteed by deploying a distributed random tuning mechanism for the intra-shard weight. We designed BOSS as an extension of the well-defined RS-based RapidChain protocol. We show that the proposed methods can be adapted to other sharding protocols that originally used RS techniques. We implemented and tested our protocol with more than 362,880 cases that covered seven configurable system and optimization parameters. Our evaluation revealed ≈17% average enhancement in scalability, along with a negligible <0.5% mean absolute difference in security levels. To the best of our knowledge, this is the first work that optimizes inter- and intra-shard scalability, with publicly verifiable solutions in permissionless BCs, while maintaining RS-equivalent security and unpredictability.
Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing storage facilities (TSFs), because they are not static due to sedimentation, compaction and changes associated with hydrospheric and lithospheric processes (e.g., erosion, saltation and mobility of chemical constituents). In this paper, we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin (South Africa). Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TS F, we trained a machine learning model to predict in-situ gold grades. Subsequently, we deployed the model to the Lindum TSF, which is 3 km away, over a period of a few years. We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF. We were able to infer extraction sequencing (to the resolution of the data), acid mine drainage formation and seasonal migration. These findings suggest that dynamic mineral resource models and live geochemical monitoring (e.g., of elemental mobility and structural changes) are possible without additional physical sampling.
High-order knowledge-based Discriminant features for kinship verification

This research work aims to propose an effective and robust face kinship verification system by leveraging several axes, including advanced learning techniques, deep learning, and CNN networks. The contributions of this work include the use of a preprocessing method known as Multiscale Retinex with Chromaticity Preservation (MSRCP) and the Gradientfaces technique (GRF) to improve image quality and contrast enhancement. Additionally, a novel discriminative handcrafted descriptor called Histograms of dual-tree complex wavelet transform (Hist-DTCWT) is proposed. Moreover, a logistic regression for score-level fusion is used to enhance the matching process. Furthermore, a high-order knowledge-based feature using a tensor subspace model was proposed to combine multiple discriminative features. Tests were conducted out using three datasets, where the proposed method has outperformed the state the art. Typically, verification accuracies of 96.62%, 95.54% and 92.94% have been reached under Cornell KinFace, UB KinFace and TS KinFace datasets, respectively.
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.
A Structured Literature Review Approach to Define Serverless Computing and Function as a Service

Nearly a decade ago, the emergence of an event-driven computing model attracted a lot of attention in industry and academia. This new computing concept has been described with a plethora of terms: Serverless Computing, Function as a Service (FaaS), Serverless functions, cloud functions, server-aware vs. server-less, to name only a few of the most frequently used terms. This enumeration showcases a problem of the domain: the lack of clear terminology, a conceptualization of Serverless and FaaS and a differentiation of the two. To overcome this limitation we present a Structured Literature Review (SLR) and usage trends which help to understand the current usage of the terms. Based on characteristics extracted from several definitions and validated in the course of the SLR we demarcate Serverless and FaaS. Our SLR shows that 90% of the considered papers predomi-nantly use the term Serverless whereas most of the authors intend to write about FaaS in the sense of our definition. Another insight is that the viewpoint on Serverless and FaaS evolved over time and that since 2021 most authors see Serverless as an umbrella term for FaaS and Backend as a Service (BaaS). Some of them also include further cloud service models, i.e. parts of PaaS and SaaS, providing a server-less experience for an application developer. This is an interpretation we share.
Semantic Parameter Matching in Web APIs with Transformer-based Question Answering

OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs. Although these formats are in general both human and machine-readable, only the part of the format describing the syntax of a Web API is machine-understandable. Descriptions, which explain the meaning and purpose of Web API elements, are embedded as natural language text snippets into documents and target human readers but not machines. To enable machines to read and process these state-of-practice Web API documentation, we propose a Transformer model that solves the generic task of identifying a Web API element within a syntax structure that matches a natural language query. For our first prototype, we focus on the Web API integration task of matching output with input parameters and fined-tuned a pre-trained CodeBERT model to the downstream task of question answering with samples from 2,321 OpenAPI documentation. We formulate the original question answering problem as a multiple choice task: given a semantic natural language description of an output parameter (question) and the syntax of the input schema (paragraph), the model chooses the input parameter (answer) in the schema that best matches the description. The paper describes the data preparation, tokenization, and fine-tuning process as well as discusses possible applications of our model as part of a recommender system. Furthermore, we evaluate the generalizability and the robustness of our fine-tuned model, with the result that it achieves an accuracy of 81.46% correctly chosen parameters.
Use and Applications of Blockchain Technology in Human Resource Management Functions

Organizations from different sectors are using blockchain technology, but still it is only 0.5% of the world population that is using blockchain technology in 2019, but there is a steady increase in its demand, and it is anticipated that this demand will increase to 80% of the population using it. HR is becoming an inevitable strategic function of the organizations, and blockchain is helping the organizations to overhaul the HR functioning. HR managers are using blockchain in various processes such as recruitment and selection, validation, mapping skills, processing payroll, security of data, and prevention of frauds. This chapter attempts to identify the characteristics and uses of blockchain technology and scope of its application in human resource management. The study would be conducted by reviewing the extant literature from various secondary sources of data collection. The study will assist in providing an insight about implementation of blockchain in HRM and also facilitate in organizational decision making about the implementation of technology.
Towards a Quantum-Resistant Weak Verifiable Delay Function

In this paper, we present a new quantum-resistant weak Verifiable Delay Function based on a purely algebraic construction. Its delay depends on computing a large-degree isogeny between elliptic curves, whereas its verification relies on the computation of isogenies between products of two elliptic curves. One of its major advantages is its expected fast verification time. However, it is important to note that the practical implementation of our theoretical framework poses significant challenges. We examine the strengths and weaknesses of our construction, analyze its security and provide a proof-of-concept implementation. (Author list in alphabetical order; see https://www.ams.org//profession/leaders/CultureStatement04.pdf .)