Reviewerly/exHarmony huggingface dataset:
Hofstede’in Kültürel Boyutları Çerçevesinde Online Tüketim ve Kültür İlişkisi Üzerine Bir Analizi

Today, the internet is used extensively for information, news and entertainment. However, the behavior of purchasing goods or services on the internet has increased in recent years, especially with the effect of the global pandemic. This increase has led to changes in daily life practices. The main purpose of this study, which is based on these developments, is the effect of cultural ties on the reflections of consumers' online purchasing behaviors on this behavior, within the scope of Hofstede's Cultural Dimensions Theory. For this purpose, the study was discussed within the scope of cultural dimensions and online behavior dimensions. For the purpose of the research, a conceptual framework was created in the context of the studies in the literature and hypotheses of the research were formed. The data obtained through the online survey applied to consumers over the age of 18 who engage in online consumption behavior in Çanakkale, which constitutes the universe of the study, were analyzed by using SPSS for Windows 25.0 program. According to the results of the analysis, it was found that Hofstede's cultural dimensions had a significant effect on online behavior during the process of online purchasing behavior.
Compiling Requirements from Models for Early Phase Scope Estimation in Agile Software Development Projects

Inadequate early scope estimation is a common problem in software projects, leading to failures in meeting project requirements. Agile projects usually do not concentrate on a comprehensive requirements analysis and specification before the start of the project, making scope assessment difficult. This paper presents the methodology for facilitating a more accurate early estimation of project scope, based on requirements information gathered in various forms (requirements models and textual descriptions) during the requirements workshop. The requirements from different sources are compiled into one list and reconciled, since they are prepared by a number of participants in the requirements workshop using different notations (UML diagrams, SysML models, Story map) and may have differences in the vocabulary. Reconciliation encompasses the unification of vocabulary, as well as the identification and the removal of overlaps in requirements. The final list of requirements is used to estimate the scope of the project in story points. The estimate can be presented to the client and used as a basis for the project contract. A case study on the application of the proposed methodology is presented, using the animal shelter information system as a development project. It demonstrates that the methodology is viable and can facilitate the gathering of a more extensive set of requirements, thus ensuring a more detailed scope estimation.
Distributed Data Real-Time Transaction Calculation Based on Collaborative Optimization and Multi-Objective Genetic Algorithm

Based on the above situation, this article elaborated on the methods that should be used to calculate the multi-information genetic algorithm (GA) in the current situation. The article mainly compared the non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with elite strategy. By measuring the solving speed and quality of the two algorithms, it was found that the NSGA-II had a greater advantage. Based on the NSGA-II, optimization processing was carried out. The NSGA-II was compared before and after optimization. After analyzing 48 data samples, it was found that the results of the NSGA-II before and after optimization showed that the algorithm tended to be more stable after optimization, thus indicating that the improved data was more accurate. The results indicated that the NSGA-II was necessary for its improvement, and its results were also reasonable.
Unregistered words in everyday language and a proposal for their optimal lexicographic microstructure

This article looks into lexicographic adaptation to media change. Instant messengers in Korea function as the most popular communication medium. According to the latest survey by Gallup Korea, instant messengers are used by 92% of the population overall. It means that the instant messenger corpus provides an ideal resource for accessing the language of the masses from a corpus linguistic point of view. In this contribution, we analyze an instant messenger corpus of 1.4 million words, and look into the prevalent unregistered words in the corpus to propose a microstructural model for them. Section 2 introduces the normalized parallel corpus of Messenger used in this study, and discusses the extraction methodology for unregistered words. We discuss the operational definition of unregistered words for dictionary inclusion and their extraction process. Section 3 examines the prevalence of unregistered words in the defined Messenger corpus and categorizes them based on the characteristics of messenger language. These characteristics encompass deviations from the pre-existing writing system, deviations from linguistic norms, deviations from socio-ethical criteria, incidental omissions, and non-verbal expressions. Section 4 proposes an optimal lexicographical structure incorporating unregistered words and their characteristics identified in the previous sections. Additionally, we discuss the extension and modification of microstructures in existing dictionaries, which could be made to effectively represent this new medium’s language.
Design of efficient data acquisition and calculation of ground disaster system based on FPGA

For the current millimeter wave radar imaging data volume than the mud level meter, rain gauge and other single-point monitoring means of data volume tens of times or hundreds of times geometrically increasing and other challenges, in order to meet the data acquisition computing needs, designed based on FPGA ground disaster system data acquisition computing design of colleges and universities. First of all, we analyzed the requirements of data acquisition of ground disaster system, designed the overall architecture of the system based on FPGA, developed XC7K325T as the main control chip, ADC analog-to-digital conversion and other related modules in one, and then conducted experimental verification of the data acquisition and operation system of ground disaster system, which proved that the system has good acquisition and processing performance, and effectively overcame the problems of large amount of data acquisition of ground disaster system and inaccurate acquisition and operation processing. The system can effectively overcome the problems of large volume of data collection and inaccuracy of data collection and processing, and is conducive to the subsequent data processing of the ground disaster system.
Mitigation of quantum crosstalk in cross-resonance-based qubit architectures

The cross-resonance gate (cr gate) architecture that exploits fixed-frequency transmon qubits and fixed couplings is a leading candidate for quantum computing. Nonetheless, without the tunability of qubit parameters such as qubit frequencies and couplings, gate operations can be limited by the presence of quantum crosstalk arising from the always-on couplings. When increasing system sizes, this can become even more serious, considering frequency collisions caused by fabrication uncertainties. Here, we introduce a cr-gate-based transmon architecture with passive mitigation of both quantum crosstalk and frequency collisions. Assuming typical parameters, we show that $ZZ$ crosstalk can be suppressed while maintaining $XY$ couplings to support fast, high-fidelity cr gates. The architecture also allows one to go beyond the existing literature by extending the operating regions in which fast, high-fidelity cr gates are possible, thus alleviating the frequency-collision issue. To examine the practicality, we analyze the cr-gate performance in multiqubit lattices and provide an intuitive model for identifying and mitigating the dominant source of error. For the state-of-the-art precision in setting frequencies, we further investigate its impact on the gates. We find that $ZZ$ crosstalk and frequency collisions can be largely mitigated for neighboring qubits, while interactions beyond near-neighbor qubits can introduce new frequency collisions. As the strength is typically at the submegahertz level, adding weak off-resonant drives to selectively shift qubits can mitigate the collisions. This work could be useful for suppressing quantum crosstalk and improving gate fidelities in large-scale quantum processors based on fixed-frequency qubits and fixed couplings.
Formal Verification of Long Short-Term Memory based Audio Classifiers: A Star based Approach

Formally verifying audio classification systems is essential to ensure accurate signal classification across real-world applications like surveillance, automotive voice commands, and multimedia content management, preventing potential errors with serious consequences.Drawing from recent research, this study advances the utilization of star-set-based formal verification, extended through reachability analysis, tailored explicitly for Long Short-Term Memory architectures and their Convolutional variations within the audio classification domain.By conceptualizing the classification process as a sequence of set operations, the star set-based reachability approach streamlines the exploration of potential operational states attainable by the system.The paper serves as an encompassing case study, validating and verifying sequence audio classification analytics within real-world contexts.It accentuates the necessity for robustness verification to ensure precise and dependable predictions, particularly in light of the impact of noise on the accuracy of output classifications.
Drone Detection and Identification Using Artificial Intelligence

Autonomous drone detection systems offer a probable solution to overcoming the issue of potential drone misuse, such as drug smuggling, violating people’s privacy, etc. Detecting drones can be difficult, due to similar objects in the sky, such as airplanes and birds. The objective of the project is to evaluate state-of-the-art models and training strategies for drone detection.
Integrasi Server On-Premise dengan Server Cloud Menggunakan Cloud VPN dan Mikrotik Ipsec Untuk Peningkatan Keamanan Koneksi

In the ever-evolving digital era, integration of onpremise server infrastructure with cloud servers has become an important focus for many companies. The problem faced is regarding connections that use public IP addresses directly, which are often vulnerable to various security risks and cyber threats. Therefore, this research aims to identify and overcome the main problems that arise in this integration. With a better understanding of these challenges, developers and organizations can improve the security of their hybrid server infrastructure. The research methodology uses Mikrotik IPsec and VPN Tunnel IKEv2 on Google Cloud services. This approach is utilized to increase connection security between onpremise servers and cloud servers. First, the author configures Mikrotik IPsec on the onpremise server and sets up the IKEv2 VPN Tunnel on Google Cloud services. Next, the author carried out practical tests by sending data between the two infrastructures and monitoring the security and stability of the connection. With these steps, we can evaluate the effectiveness of security solutions implemented in a hybrid server environment. The research results show that this approach is successful in securing the connection between the two infrastructures, reducing potential risks such as sniffing, port scanning attacks, brute force, DDOS, and other cyber threats. Moreover, during practical trials, the connection remained stable and performed optimally, demonstrating the effectiveness of the proposed solution in improving the security of hybrid server infrastructure. These results can provide concrete evidence that using Mikrotik IPsec and VPN Tunnel IKEv2 on Google Cloud services can be an effective step in securing communications between onpremise servers and cloud servers. Thus, this research provides valuable guidance for developers and organizations looking to implement similar solutions.&#x0D;
Ghost Roast by Shawneé Gibbs (review)

Reviewed by: Ghost Roast by Shawneé Gibbs April Spisak Gibbs, Shawneé Ghost Roast; written by Shawneé Gibbs and Shawnelle Gibbs; illus. by Emily Cannon. Versify/HarperCollins, 2024 [224p] Trade ed. ISBN 9780358141815 $26.99 E-book ed. ISBN 9780358137245 $11.99 Paper ed. ISBN 9780358141808 $18.99 Reviewed from digital galleys R Gr. 7-9 Chelsea used to love helping her dad with his work, but now that she is older the last thing she wants is to be known as the daughter of a ghost hunter. When she gets in trouble with some new friends, however, her divorced parents both agree that a suitable punishment will be working with her dad all summer. It’s terrible at first—her dad’s office is a mess, and his advertising makes Chelsea want to hide, but everything changes after Chelsea realizes that she doesn’t need any of her dad’s fancy equipment to see ghosts. And in a city like New Orleans, there are plenty of restless spirits to see. Chelsea would certainly like to be the Black girl who blasts away the ghosts of former slave owners on a “manor” that used to be a plantation, but she quickly discovers that her dad’s approach to ghost removal is cruel and that there has to be a better way to help the spirits, even the ones who may deserve being zapped into oblivion. Zippy, manga-inspired emotional cues make this graphic novel [End Page 134] visually appealing right away, and the vivid coloring effectively contrasts with the washed-out hues to differentiate between Chelsea’s corporeal world and the ghosts and their pasts. While the text crowds the panels on occasion, the story is worth telling, and given that this is a stand-alone graphic novel, there is a lot to fit into the short page count. Chelsea is a terrific protagonist, memorable in her balance of empathy and drama, but the ghosts are the stars here, mysterious, vulnerable, and each carrying their stories that no one (until Chelsea) can hear. Copyright © 2023 The Board of Trustees of the University of Illinois
Water Resources Management at Raden Intan Islamic State University, Indonesia

UIN Raden Intan Lampung is an Islamic university located at the southernmost tip of Sumatra. The area receives a rainfall ranging between 77-317 mm3 in 2022, and this rainfall must be harnessed and conserved as water reserves for daily needs. The university has a total area of 45 hectares, and it is home to a population of 34,482 individuals. The soil texture in the UIN Raden Intan Lampung campus area is predominantly clay, and the dense soil particles make it challenging to absorb water. Consequently, measures are required to regulate soil moisture levels to facilitate optimal water absorption. One strategy to preserve the water supply on campus is the construction of artificial ponds that capture rainwater. Ten such ponds have been built, covering 21.54% of the total green open space owned. Furthermore, around 8,000 biopore infiltration holes have been installed to maintain groundwater availability and improve soil fertility. The campus also conducts campaigns to promote water conservation and uses water-saving equipment technology. Treated wastewater from ablution activities in mosques is employed for secondary needs such as fish farming activities and irrigation. Ultimately, the preservation of water resources on campus must be sustainable, given the large population of people who depend on water for their daily needs, and ensuring water availability for the future is critical.
The Right to Break the Law? Perfect Enforcement of the Law Using Technology Impedes the Development of Legal Systems

Abstract Technological developments increasingly enable monitoring and steering the behavior of individuals. Enforcement of the law by means of technology can be much more effective and pervasive than enforcement by humans, such as law enforcement officers. However, it can also bypass legislators and courts and minimize any room for civil disobedience. This significantly reduces the options to challenge legal rules. This, in turn, can impede the development of legal systems. In this paper, an analogy is made with evolutionary biology to illustrate that the possibility to deviate from legal rules and existing norms is sometimes necessary for the further development of legal systems. Some room to break the law, for instance, through civil disobedience or imperfect enforcement of the law, will ensure sufficient variation. This allows for properly evolving legal systems that can continue to provide fair solutions, even when society and concepts of fairness further develop.
POTENTIAL USE OF ARTIFICIAL INTELLIGENCE FOR DIGITAL PROMOTIONAL VIDEO: NEUROMARKETING APPROACH

Ad production and product marketing media selection have changed due to the industrial revolution 4.0. Smartphones, computers, and gadgets have shifted consumer product information searches from print to digital. Digital promotional video (DPV) informs products best. Marketers can use hearing and sight to communicate product details. DPV production is expensive and time-consuming, from filming, editing, rendering, and broadcasting. AI can cut DPV production costs and speed up the creative process. AI software processes scripts into computers to generate DPVs. DPV will survey, interview, and focus group multiple respondents before publishing it. Neuromarketing can access consumers' subconscious minds and emotions. This research review investigates neuromarketing-based AI for DPV
Dehazing and vision enhancement: challenges and future scope

Poor visibility of outdoor images has been drastically increased. Applications using computer vision, including surveillance systems, intelligent transportation systems, are not able to function properly due to limited visibility. Numerous image dehazing methods have been introduced as a solution to this problem, and they are crucial in enhancing the functionality of several computer vision systems. The dehazing approaches are intriguing to researchers as a consequence. In order to demonstrate that dehazing techniques could be successfully used in actual practice, this study conducts an extensive examination of the state-of-the-art dehazing approaches. In contrast, it motivates scholars to apply some of these methods for removing haze from hazy images. In this chapter, we discuss several robust mathematical models along with some neural network-based approaches and their implementations in various aspects. Finally, we address several concerns about difficulties and potential future applications of dehazing approaches.Due to poor visibility conditions, the visibility of outdoor images is drastically decreased. Applications using computer vision, including surveillance systems, intelligent transportation systems, etc., are not able to function properly due to limited visibility. Numerous image dehazing methods have been introduced as a solution to this issue, and they are crucial in enhancing the functionality of several computer vision systems. The dehazing approaches are intriguing to researchers as a consequence. In order to demonstrate that dehazing techniques could be successfully used in actual practice, this study conducts an extensive examination of the state-of-the-art dehazing approaches. In contrast, it motivates scholars to apply some of these methods for removing haze from hazy images. We keep an eye on the robust mathematical models along with some neural network-based approaches and their implementations in various aspects. Finally, we address several concerns about difficulties and potential future applications of dehazing approaches.
The biology of legal nothingness (the invisible hand of biology)

This chapter considers the ways in which law has regulated biological processes that have an impact upon the creativity of the individual in making cultural works. Thus, this chapter does not consider the broader impact of regulation upon the biological body per se, but instead how regulation has impacted the manner in which people create cultural works using technologies as a process of the biological body, or how the creation of cultural biological works has been impacted. The chapter argues that in relation to the former, regulation has had limits in terms of being able to reach into the innermost thoughts of the individual. Instead, the technologies of regulation are those that impact the human body rather than human thought directly, and do so in terms of products rather than processes. This has involved altering or affecting the individual’s ability to be able to interact with others, for instance through the utilisation of proprietary boundaries or through other means of restriction, for example imprisonment, or removal of access to a computer or printing press. Essentially, these are all imprecise methods by which to alter the creative outputs of individuals, quite divorced from directly interacting with the processes of biology and thought. However, regulation is not so imprecise in the creation of biological cultural works. This is something that we see in fields such as synthetic biology and bioprinting. Synthetic biology is limited by all sorts of regulations, for example in relation to the making of genetically modified organisms (GMOs). In the EU, for example, there is a precautionary approach to all regulation into GMOs. Nonetheless, the regulation remains concerned more with final products than a conception of process. This is even true of regulation dealing with processes patents, which consider the process as a thing rather than a fluid process in action. These are the differences and issues that this chapter considers, in order to suggest how different approaches to regulation can influence the direction of creative biological processes.
Exact learning dynamics of deep linear networks with prior knowledge <sup>*</sup>

Learning in deep neural networks is known to depend critically on the knowledge embedded in the initial network weights. However, few theoretical results have precisely linked prior knowledge to learning dynamics. Here we derive exact solutions to the dynamics of learning with rich prior knowledge in deep linear networks by generalising Fukumizu's matrix Riccati solution (Fukumizu 1998 Gen1 1E-03). We obtain explicit expressions for the evolving network function, hidden representational similarity, and neural tangent kernel over training for a broad class of initialisations and tasks. The expressions reveal a class of task-independent initialisations that radically alter learning dynamics from slow non-linear dynamics to fast exponential trajectories while converging to a global optimum with identical representational similarity, dissociating learning trajectories from the structure of initial internal representations. We characterise how network weights dynamically align with task structure, rigorously justifying why previous solutions successfully described learning from small initial weights without incorporating their fine-scale structure. Finally, we discuss the implications of these findings for continual learning, reversal learning and learning of structured knowledge. Taken together, our results provide a mathematical toolkit for understanding the impact of prior knowledge on deep learning.