Reviewerly/exHarmony huggingface dataset:
Experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle

Grover's search algorithm is a well-known quantum algorithm that has been extensively studied and improved to increase its success rate and enhance its flexibility. However, most improved search algorithms require an adjustment of the oracle, which may not be feasible in practical problem-solving scenarios. In this work, we report an experimental demonstration of a deterministic quantum search for multiple marked states without adjusting the oracle. A linear optical setup is designed to search for two marked states, one in a 16-state database with an initial equal-superposition state and the other in an 8-state database with different initial nonequal-superposition states. The evolution of the probability of finding each state in the database is also measured and displayed. Our experimental results agree well with the theoretical predictions, thereby proving the feasibility of the search protocol and the implementation scheme. This work is a pioneering experimental demonstration of deterministic quantum search for multiple marked states without adjusting the oracle.
Evolution of Blockchain Technology

This chapter presents a detailed introduction to the decentralized data processing and storage paradigm called blockchain technology, delving into its fundamental concepts, foundational principles, and advantages. A literature review is conducted, detailing existing research areas, and enabling technologies driving further research and development into the technology. The two-way fusion of machine learning – another data-driven technology – is considered, wherein one technology addresses limitations and drawbacks within the other. The existing applications of blockchain technology within various domains are discussed, along with some identified research challenges and future trends. A case study is also provided to demonstrate the integration of blockchain technology into the internet of vehicles domain.
AI and core electoral processes: Mapping the horizons

Abstract It is well documented that there has been significant enthusiasm across the globe in respect of using AI for all forms of social activity. However, the electoral process – the time, place, and manner of elections within democratic nations – is one of few sectors in which there has been limited penetration of AI. Electoral management bodies in many countries have recently started exploring and deliberating over the use of AI in the electoral process. In this paper, we consider five avenues within the core electoral process which have potential for AI usage, and map the challenges involved in using AI within them. These five avenues are: voter list maintenance, determining polling booth locations, polling booth protection processes, voter authentication, and video monitoring of elections. Within each avenue, we lay down the context, illustrate current or potential usage of AI, and discuss extant or potential ramifications of AI usage, as well as potential directions for mitigating risks when considering AI usage. We believe that the scant current usage of AI within electoral processes provides a very rare opportunity to deliberate on the risks and mitigation possibilities prior to actual and widespread AI deployment. This paper is an attempt to map the horizons of risks and opportunities in using AI within electoral processes and to help shape the debate around the topic.
Literature Review on the Development of Visualization Studies (2012–2022)

In the past decade, the visualization and transformation of data and information have attracted lots of research interest, while visualization has gradually extended to all industries. Based on the retrieval of core literature in a Web of Science search from 2012 to 2022, this study finds that these developments mainly discussed the change of visualization and its related concepts, current research hotspots, and influential journal papers. Consequently, it aims to explore research gaps and provide directional guidance for future research.
The Role of Internet of Behaviors (IoB) in Digital Marketing

The internet of behaviors (IoB) refers to the technology that combines the internet of things (IoT), big data analytics, artificial intelligence (AI), along with behavioral science, to collect and analyze data from interconnected physical objects, with the goal of gaining insights into human behavior, needs, interests, and preferences. By analyzing purchasing patterns, consumption behaviors, and demographic interests, IoB uncovers hidden patterns in consumer behaviors. In essence, IoB endeavors to interpret the information gathered from users' online behaviors through the lens of behavioral psychology. This chapter explores the IoB technology and its impact on the digital marketing industry. This chapter discusses several sub-topics, including how businesses are currently implementing IoB in digital marketing and in other fields, the ways IoB can improve the customer experience, ethical and social concerns related to IoB, and the future of IoB in digital marketing. The chapter offers valuable insights for both digital marketing practitioners and researchers.
A novel deinterleaving method for radar pulse trains using pulse descriptor word dot matrix images and cascade‐recurrent loop network

Abstract Traditional signal deinterleaving methods depend on preset parameters, use complicated steps and have limitations in handling complex electromagnetic environments where signals overlap in the space, time and frequency domains. This paper presents a novel approach using a deep segmentation network for radar signal deinterleaving. The pulse descriptor word data is transformed into a dot matrix image, which is then processed by a Cascade‐Recurrent Loop Network (CRLN) for segmentation. The CRLN consists of a Type Segmentation Network, an Amount Decision Network and a Recurrent Individual Segmentation Network. By recursively segmenting each target and determining the number of sources, the CRLN overcomes the challenges posed by the discontinuity and high overlap of individuals in the dot matrix image. Experimental results demonstrate the effectiveness of the proposed method, surpassing traditional deinterleaving methods, achieving high accuracy, low omission rate and effectively mitigates the increasing batch problem in complex scenarios. The experiments conducted on a randomly generated dataset yielded impressive results: over 90% accuracy when dealing with pulses from 15 radars of different types, over 96% accuracy when deinterleaving pulses from 4 radar individuals of a same type and over 90% accuracy when handling 5 radars of different types with 10% pulse loss.
Unrest could rise in Middle East non-oil economies

Headline MIDDLE EAST/NORTH AFRICA: Social unrest could increase
Automated picking of seismic first arrivals using a single- to multidomain self-trained network

Over the past few decades, seismic data volume has increased owing to the evolution of survey acquisition designs that comprise numerous receivers and shotpoints. Consequently, the demand for efficient algorithms to detect seismic first arrivals has been increasing. Machine-learning methods, particularly convolutional neural networks, have shown great potential in achieving this. However, most existing methods lack generalization abilities or are too sensitive to noise. We develop a new picking strategy based on a single- to multidomain self-trained network to achieve automated picking of first arrivals. Our self-trained network learns from single to multiple domains while rejecting the incorrectly predicted picks and assigning pseudo-first-arrival times to the unlabeled data set. The model inherits the advantages of single- and multidomain picking strategies and has high generalizability and noise robustness. In experiments on an open hard-rock seismic multisurvey data set, our picking method outperforms the benchmark for most evaluation metrics. Remarkably, our approach achieves an accuracy improvement of approximately 10% against the data from one mine site (Lalor), with a sampling rate that differed from the training data. In addition, our method indicates robust results considering the limited amount of labeled data. The experimental results demonstrate the high generalizability and robustness of our method for unseen data sets and reveals the effectiveness of single- to multidomain self-trained networks to improve the accuracy and stability of automated first-arrival picking. Our method contributes to reducing human labor and the total seismic processing time.
QuanDA: GPU Accelerated Quantitative Deep Neural Network Analysis

Over the past years, numerous studies demonstrated the vulnerability of deep neural networks (DNNs) to make correct classifications in the presence of small noise. This motivated the formal analysis of DNNs to ensure that they delineate acceptable behavior. However, in the case that the DNN’s behavior is unacceptable for the desired application, these qualitative approaches are ill equipped to determine the precise degree to which the DNN behaves unacceptably. We propose a novel quantitative DNN analysis framework, QuanDA, which not only checks whether the DNN delineates certain behavior but also provides the estimated probability of the DNN to delineate this particular behavior. Unlike the (few) available quantitative DNN analysis frameworks, QuanDA does not use any implicit assumptions on the probability distribution of the hidden nodes, which enables the framework to propagate close to real probability distributions of the hidden node values to each proceeding DNN layer. Furthermore, our framework leverages CUDA to parallelize the analysis, enabling high-speed GPU implementation for fast analysis. The applicability of the framework is demonstrated using the ACAS Xu benchmark, to provide reachability probability estimates for all network nodes. This paper also provides potential applications of QuanDA for the analysis of DNN safety properties.
Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO), application to controller tuning and optimization

ABSTRACTAn Improved Cascade Chaotic Invasive Weed Optimization Algorithm (ICCIWO) is proposed, employing cascade chaotic maps in the structure of a metaheuristic optimisation algorithm. The main aim of this modification is to provide a new algorithm for tuning the parameters of controllers. Conventionally, invasive weed optimisation algorithms (IWO) have been utilised in the literature to solve optimisation problems. The IWO has merits, namely fast convergence and high extraction capability. However, the IWO suffers from a lack of population diversity and poor exploration. Two novel improvements are proposed here to eliminate these disadvantages, based on utilising chaotic behaviours in the population generation and in the standard deviation update rule. The final proposed mechanism is applied to tune the parameters of a PID controller and solve several benchmark optimisation problems. Simulation analysis compares the performance of the proposed method with a recent similar approach. The simulation results validate the high performance of the proposed novel cascade chaotic scheme.KEYWORDS: Cascade Chaotic Mappingsinvasive weed optimizationmeta heuristic optimization algorithmsPID controller Disclosure statementNo potential conflict of interest was reported by the authr(s).Additional informationFundingThis research received no specific grant from any funding agency in public, commercial, or not-for-profit sectors.
Constacyclic and quasi-twisted codes over $ \mathbb{Z}_{q}[u]/\langle u^{2}-1\rangle $ and new $ \mathbb{Z}_4 $-linear codes

In this paper, we investigate the algebraic structures and properties of constacyclic and quasi-twisted (QT) codes over the ring R = Zq + uZq with u 2 = 1.We show that the image of a constacyclic code over R under a natural Gray map is a QT code of index 2 over Zq.Given the decomposition of a QT code, we find the decomposition of its dual code.We present 116 new linear codes over Z 4 from the Gray images of QT codes over this ring with q = 4. Finally, a characterization of linear complementary pair (LCP) constacyclic codes over R is provided.
Forcing revisited

Abstract The purpose of this paper is to propose and explore a general framework within which a wide variety of model construction techniques from contemporary set theory can be subsumed. Taking our inspiration from presheaf constructions in category theory and Boolean ultrapowers, we will show that generic extensions, ultrapowers, extenders and generic ultrapowers can be construed as examples of a single model construction technique. In particular, we will show that Łoś's theorem can be construed as a specific case of Cohen's truth lemma, and we isolate the weakest conditions a filter must satisfy in order for the truth lemma to work.
Multi-agent Game Domain: Monopoly

In the previous chapters we have looked at the visual domain and single-agent environments for action domains. In this chapter, we will apply the novelty framework to a multi-agent game environment. As an example of multi-agent game environment, we introduce a simulated version of the Monopoly board game. Monopoly is a multi-agent board game that involves four players taking turns by rolling a pair of unbiased dice and making decisions. The conventional Monopoly board consists of 40 square locations which include 22 real estate locations, 4 railroads, and 2 utility locations that players can buy, sell, or trade. Furthermore, there are squares that correspond to “Go,” a jail location, card locations, and the free parking location. Figure 7.1 shows all assets, their corresponding purchase prices, and color. We setup the Monopoly simulator to have one learning-based agent ( $$\alpha _{\mathcal{T}}$$ ) and three fixed-policy agents. These constitute the four players in the game. The objective of the learning-based agent ( $$\alpha _{\mathcal{T}}$$ ) is to learn winning strategies for Monopoly. Formally, the task $$\mathcal T$$ of the agent, $$\alpha _{\mathcal{T}}$$ , is defined as: given the observation space $$x_t \in \mathcal O$$ at time t, select an action $$a_t \in \mathcal A$$ to maximize the overall reward resulting in a higher win rate.
Novelty in 2D CartPole Domain

In this chapter, we apply the constructs defined in Chapter 1 to the relatively simple domain of 2D CartPole. We consider different versions of CartPole agents to highlight the different effects of novelties on an agent. The results generally show that the novelty theoretic framework allows the estimation of different novelties’ impact on the performance of agents, which can be used for testing open-world learning hypotheses. This chapter presents a few surprising results that help further inform the framework.
Software implementation of the system for learning to write Chinese characters

Objective . The purpose of the work is devoted to the development and description of the mathematical model of the Chinese character recognition system, taking into account all the features of writing the Chinese language. The Chinese language learning app with character recognition module can help you replace a native speaker or home teacher for self-study. However, the developed software applications are based only on the creation of a neural network and cannot provide recognition, taking into account all the features of the language, which is so important when studying, therefore this topic is still relevant. Method . The neural network training model is based on the use of artificial neural networks using the backpropagation algorithm. Result . The article presents a software implementation of a system for teaching Chinese characters, taking into account the peculiarities of writing, the direction of each feature and its exact definition, taking into account the correct sequence and location in the character, as well as controlling the length of the features. Conclusion . Each of the writing features is an integral part of learning a language, since it can not only completely change the meaning of the written hieroglyph, but also help the learner to memorize the hieroglyph in a structured way, giving him a clear structure and algorithm of actions for writing the hieroglyph. When errors are detected when writing, the system will indicate to the user where exactly and in what area the error was made, what feature of the language he did not take into account, and attention should be paid to it.
Testing the operability of the remote monitoring method implemented in HelpMe-Tracker on people and checking the application’s response to deviations in health indicators

Objective . The purpose of the work is to check the operability of the proposed method for remote monitoring of patients. Method . To carry out the test, an experiment was set up in which various categories of people of different ages, including those with diseases. During the experiment, the application should record deviations in various indicators obtained from the sensors of the bracelet, and inform about it. Result . Thus, the experiment showed that the method of remote monitoring of patients proposed by us is fully suitable for monitoring the current condition. Working by this method, the application recorded all anomalies in the indicators obtained from the sensors of the bracelet, and successfully informed about them. Conclusion . The application of our proposed method is sufficient to determine deviations in health indicators of patients, track the dynamics of their changes and see the overall picture of human health.
Blockchain in the EU: Transforming imaginaries and the social making of fnancial regulation

Based on local and online ethnographic research, this article explores how blockchain projects and financial supervisors navigate transforming technological dispositions and interact in constructing regulatory frameworks. While there is a straightforward replacement of revolutionary blockchain narratives with regulatory compliance ones, there is also degree of codependency between regulators and market actors in regulatory negotiation.
Towards Deconstructivist Music: Reconstruction paradoxes, neural networks, concatenative synthesis and automated orchestration in the creative process

Since the 1980s, deconstruction has become a popular approach for designing architecture. In music, however, the term has not been absorbed as well by the related literature, with a few exceptions. In this article, ways to find ideological groundings for deconstructivism in music are introduced through the concepts of enchaînement and reconstruction paradoxes. Similar to the Banach–Tarski paradox in mathematics, reconstruction paradoxes occur when reconstructing the parts of a whole no longer yields the same properties as the whole. In music, a reconstruction paradox occurs when a piece constructed from tonal segments no longer yields a perceived tonality. Deconstruction in architecture heavily relies on computer-aided design (CAD) to realise complex ideas. Similarly in music, computer-aided composition (CAC) techniques such as neural networks, concatenative synthesis and automated orchestration are used. In this article, we discuss such tools in the context of this advocated new aesthetics: deconstructivist music .
Survey of Emerging Blockchain Technologies for Improving the Data Integrity and Auditability of Manufacturing Bills of Materials in Enterprise Resource Planning

ABSTRACT Blockchain and cloud computing continue to emerge and evolve as important technologies for data management. This has strategic implications for data stored in enterprise resource planning (ERP) systems. We use manufacturing bills of materials (MBOMs) as an example of critical data in ERP. MBOMs define the complex product structures of manufacturing enterprises. We survey five alternatives for storing, controlling, and auditing MBOMs in ERP: Oracle Blockchain Table; Oracle Blockchain Platform Enterprise Edition; Microsoft Azure SQL Database Ledger; SAP HANA Blockchain Ledgers; and IBM Blockchain Platform. We also consider cloud computing without blockchain technologies. We analyze the advantages and disadvantages of each alternative by its effect on the management of MBOMs. Although we use MBOMs in our illustrations, many of the concepts discussed also apply to other critical data stored in ERPs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G33; M21.
<i>Frail hope and new frontiers</i>

This guest editorial takes as its starting point the 2021 guest editorial in ANTHROPOLOGY TODAY on the anthropology of blockchain written by Kosmarski and Gordiychuk, in which they discussed the possibilities of blockchain in terms of a ‘frail hope of novel, weird, grassroots, decentralized forms of social life’. They also argued that blockchain brought us to ‘new frontiers’ in politics, economics, capital, votes and subjective feelings. Two years later, in this 2023 guest editorial, the authors take stock of where blockchain technology stands concerning these ‘frail hopes’ and ‘new frontiers’. They distinguish between three articulations of blockchain imaginaries: blockchain‐as‐discourse, blockchain‐as‐sociotechnical assemblage and blockchain‐as‐spectacle. Then, they explore what blockchain means for capital, and whether we are headed towards mass adoption of blockchain technology, concluding that, for now, they see primarily institutional adoption. The authors also discern parallel institutional structures, with traditional finance on one side and blockchain‐enabled crypto finance on the other, and they examine the regulated future of crypto assets.
Some pig! Exchanging NFTs for the perspective of regeneration

There are a thousand more silly ideas for every utopian blockchain project to heal the planet, foster financial justice, transform cultural production or democratize practically anything and everything. Non‐fungible tokens (NFTs) are one of them. The funny thing is that the more you pick at them, the more they harmonize with certain longstanding anthropological arguments. Non‐fungibility lurks in the anthropological imagination of non‐capitalist economies. This article uses the common law of fungibility to explore the elevation of the very idea of non‐fungibility presumed in many NFTs. This idea might have more in common with saints’ incorruptible flesh or the Crown Jewels than the standard fictions of capitalist finance. They cut social ties by insisting on their singularity and immutability. In doing so, they crowd out other blockchain conversations about regeneration and ways to reckon the debts we owe any number of possible futures.