Reviewerly/exHarmony huggingface dataset:
ChatGPT is not a pocket calculator -- Problems of AI-chatbots for teaching Geography

The recent success of large language models and AI chatbots such as ChatGPT in various knowledge domains has a severe impact on teaching and learning Geography and GIScience. The underlying revolution is often compared to the introduction of pocket calculators, suggesting analogous adaptations that prioritize higher-level skills over other learning content. However, using ChatGPT can be fraudulent because it threatens the validity of assessments. The success of such a strategy therefore rests on the assumption that lower-level learning goals are substitutable by AI, and supervision and assessments can be refocused on higher-level goals. Based on a preliminary survey on ChatGPT's quality in answering questions in Geography and GIScience, we demonstrate that this assumption might be fairly naive, and effective control in assessments and supervision is required.
Arabic Chatbot Evaluation Based on Extractive Question-Answering Transfer Learning and Language Transformers

Chatbots are computer programs that use artificial intelligence to imitate human conversations. Recent advancements in deep learning have shown interest in utilizing language transformers, which do not rely on predefined rules and responses like traditional chatbots. This study provides a comprehensive review of previous research on chatbots that employ deep learning and transfer learning models. Specifically, it examines the current trends in using language transformers with transfer learning techniques to evaluate the ability of Arabic chatbots to understand conversation context and demonstrate natural behavior. The proposed methods explore the use of AraBERT, CAMeLBERT, AraElectra-SQuAD, and AraElectra (Generator/Discriminator) transformers, with different variants of these transformers and semantic embedding models. Two datasets were used for evaluation: one with 398 questions and corresponding documents, and another with 1395 questions and 365,568 documents sourced from Arabic Wikipedia. Extensive experimental works were conducted, evaluating both manually crafted questions and the entire set of questions, using confidence and similarity metrics. The experimental results showed that the AraElectra-SQuAD model achieved an average confidence score of 0.6422 and an average similarity score of 0.9773 on the first dataset, and an average confidence score of 0.6658 and similarity score of 0.9660 on the second dataset. The study concludes that the AraElectra-SQuAD model consistently outperformed other models, displaying remarkable performance, high confidence, and similarity scores, as well as robustness, highlighting its potential for practical applications in natural language processing tasks for Arabic chatbots. The study suggests that the AraElectra-SQuAD model can be further enhanced and applied in various tasks such as chatbots, virtual assistants, and information retrieval systems for Arabic-speaking users. By combining the power of transformer architecture with fine-tuning on SQuAD-like large data, this trend demonstrates its ability to provide accurate and contextually relevant answers to questions in Arabic.
Access to drinking water and sanitation in developing countries: Does financial development matter?

ABSTRACTABSTRACTThe aim of this study is to examine the effect of financial development on access to safe water and sanitation. Using panel data from a sample of 106 developing countries over the period 2000–2019, empirical results based on two-step system generalised method of moments suggest that financial development improves access to drinking water and sanitation for the total population and for both urban and rural populations. In addition, financial development reduces the gap between urban and rural populations in terms of access to these two basic services. Further analysis also suggests that the financial market and financial institutions, as well as their sub-indices (financial depth, financial access, and financial efficiency), also improve access to water and sanitation. These results underscore the need for continued efforts to design and implement policies that promote financial development. In addition, given the greater impact of financial institutions, we suggest that reforms to improve the financial system should be more oriented towards the development of financial institutions.KEYWORDS: Financial developmentdrinking watersanitationsustainable developmentJEL CLASSIFICATION: G2Q25Q34Q01 AcknowledgmentsWe also thank the organizers of the CSAE Conference held on March 19–22, 2023. The feedback we received during this conference allowed us to further improve the quality of this paper. Finally, we thank Isaac Ketu and Tii Nchofoung for their help in proofreading the different versions of the manuscript.Disclosure statementAuthors declare that they do not have any competing financial, professional, or personal interests from other parties. We thank the reviewer of this article for all of the comments to improve this research. No potential conflict of interest was reported by the authors.
BMSQABSE: Design of a Bioinspired Model to Improve Security &amp; QoS Performance for Blockchain-Powered Attribute-based Searchable Encryption Applications

Attribute-based searchable encryption (ABSE) is a sub-field of security models that allow intensive searching capabilities for cloud-based shared storage applications. ABSE Models require higher computational power, which limits their application to high-performance computing devices. Moreover, ABSE uses linear secret sharing scheme (LSSS), which requires larger storage when compared with traditional encryption models. To reduce computational complexity, and optimize storage cost, various researchers have proposed use of Machine Learning Models (MLMs), that assist in identification &amp; removal of storage &amp; computational redundancies. But most of these models use static reconfiguration, thus cannot be applied to large-scale deployments. To overcome this limitation, a novel combination of Grey Wolf Optimization (GWO) with Particle Swarm Optimization (PSO) model to improve Security &amp; QoS performance for Blockchain-powered Attribute-based Searchable Encryption deployments is proposed in this text. The proposed model augments ABSE parameters to reduce its complexity and improve QoS performance under different real-time user request scenarios. It intelligently selects cyclic source groups with prime order &amp; generator values to create bilinear maps that are used for ABSE operations. The PSO Model assists in generation of initial cyclic population, and verifies its security levels, QoS levels, and deployment costs under multiple real-time cloud scenarios. Based on this initial analysis, the GWO Model continuously tunes ABSE parameters in order to achieve better QoS &amp; security performance levels via stochastic operations. The proposed BMSQABSE model was tested under different cloud configurations, and its performance was evaluated for healthcare deployments. Based on this evaluation, it was observed that the proposed model achieved 8.3% lower delay, with 4.9% lower energy consumption, 14.5% lower storage requirements when compared with standard ABSE models. It was able to mitigate Distributed Denial of Service (DDoS), Masquerading, Finney, and Sybil attacks, which assists in deploying the proposed model for QoS-aware highly secure deployments.
An Enhanced Scammer Detection Model for Online Social Network Frauds Using Machine Learning

The prevalence of online social networking increase in the risk of social network scams or fraud. Scammers often create fake profiles to trick unsuspecting users into fraudulent activities. Therefore, it is important to be able to identify these scammer profiles and prevent fraud such as dating scams, compromised accounts, and fake profiles. This study proposes an enhanced scammer detection model that utilizes user profile attributes and images to identify scammer profiles in online social networks. The approach involves preprocessing user profile data, extracting features, and machine learning algorithms for classification. The system was tested on a dataset created specifically for this study and was found to have an accuracy rate of 94.50% with low false-positive rates. The proposed approach aims to detect scammer profiles early on to prevent online social network fraud and ensure a safer environment for society and women’s safety.
Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing

This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study.
Stakeholder Analysis of Sustainable Wastewater Management: A Case Study of Bogor, Indonesia

Wastewater management in Bogor, Indonesia, faces significant challenges to achieving a sustainable state. The growth of self-help settlements which are sporadic, unplanned, unorganized, and very dense, has an impact on the government&amp;#039;s difficulties in providing sustainable wastewater management services. The research aims to examine fast-growing areas of self-help settlements, identify critical elements as a basis for strategy, and map the roles and influence of stakeholders in managing fast-growing areas in self-help settlements based on domestic wastewater management. This research uses a case study approach. Data were collected through observation, interviews, and focus group discussions. Data in this study were analyzed using Mactor software. The result showed knowledge and understanding of the correct management of domestic wastewater are the basis for achieving the actors&amp;#039; goals. In an implementation, social capital is needed to increase community participation by providing incentives that can affect the management of domestic wastewater, and it is necessary to increase the capacity of actors to assist the community in managing domestic wastewater. Ease of implementation at the community level must be based on community-based planning with government assistance so that the community itself carries out the decision on the type of domestic wastewater management by considering the advantages and disadvantages of each wastewater management system.
ElectroNet: An Enhanced Model for Small-Scale Object Detection in Electrical Schematic Diagrams

Abstract This study presents the design and development of a highly accurate and efficient approach for interpreting both hand-drawn and printed electric-circuit schematics. It addresses critical challenges in the field such as the lack of annotated data forobject-detection in schematics. To mitigate this, we have created an extensive dataset, comprising of 23 classes of electric-circuit elements, textual elements with units, prefixes, decimal points, and an array of various handwriting and printing styles with a total of 198495 annotations in the dataset. This rich and diverse dataset ensures the robustness of our system against different styles of circuit drawings. We combined our dataset with the MNIST dataset to enhance the model’s resilience to diverse writing styles. Another obstacle faced in this domain is the accurate recognition and assignment of textual labels in schematics. To overcome this, we employed object-detection methodologies, leveraging use of hybrid model. It has proven effective in analyzing line diagrams and incorporating various language OCRs. Despite initial success with minor parameter adjustments, a shortfall in the accuracy of object-detection was identified when significant size variations occurred between objects. To address this, we trained a new model, leading to significant improvements in the recognition of circuit elements and textual symbols across all sizes, with high precision. This enhanced system showed an overall correct simulation rate of 92%, and an overall correct recognition rate of 94% for textual symbols. This work establishes the efficacy of employing object-detection techniques for interpreting electric schematics while also underlining the challenges and potential avenues for future research in this domain. Our approach has shown notable improvements in recognizing electric-circuit drawings, particularly for smaller components, and opens avenues for further advancements in the field of circuit recognition and analysis.
Research on Data Mining Technology of Internet Privacy Protection based on local differential privacy

With the rapid development of social economy and science and technology, data sharing and analysis technology is one of the contents widely used in the Internet field. Collecting and distributing terminal data and mining analysis is the normal form of big data analysis. Under the condition of mutual distrust between the collector and the terminal data owner, privacy protection in data collection and analysis will directly affect the application results of big data analysis. Nowadays, localized differential privacy has been applied in data acquisition and data mining analysis. Although there are still many problems in relevant theories and technologies, with the continuous in-depth research of scientific researchers, relevant issues have been proposed according to practical cases, which not only effectively control the privacy budget loss, but also improve the accuracy of data application. Therefore, on the basis of understanding the concept of localized differential privacy and related models, and according to the current application status of Internet privacy protection data mining technology, this paper proposes a clustering method with LDP GMMC as the core. The final experimental results show that the improved data acquisition method can better meet the privacy protection requirements in the non-spherically distributed data scenario.
Learning Structure Aware Deep Spectral Embedding

Spectral Embedding (SE) has often been used to map data points from non-linear manifolds to linear subspaces for the purpose of classification and clustering. Despite significant advantages, the subspace structure of data in the original space is not preserved in the embedding space. To address this issue subspace clustering has been proposed by replacing the SE graph affinity with a self-expression matrix. It works well if the data lies in a union of linear subspaces however, the performance may degrade in real-world applications where data often spans non-linear manifolds. To address this problem we propose a novel structure-aware deep spectral embedding by combining a spectral embedding loss and a structure preservation loss. To this end, a deep neural network architecture is proposed that simultaneously encodes both types of information and aims to generate structure-aware spectral embedding. The subspace structure of the input data is encoded by using attention-based self-expression learning. The proposed algorithm is evaluated on six publicly available real-world datasets. The results demonstrate the excellent clustering performance of the proposed algorithm compared to the existing state-of-the-art methods. The proposed algorithm has also exhibited better generalization to unseen data points and it is scalable to larger datasets without requiring significant computational resources.
Quantum NETwork: from theory to practice

The quantum internet is envisioned as the ultimate stage of the quantum revolution, which surpasses its classical counterpart in various aspects, such as the efficiency of data transmission, the security of network services, and the capability of information processing. Given its disruptive impact on the national security and the digital economy, a global race to build scalable quantum networks has already begun. With the joint effort of national governments, industrial participants, and research institutes, the development of quantum networks has advanced rapidly in recent years, bringing the first primitive quantum networks within reach. In this work, we aim to provide an up-to-date review of the field of quantum networks from both theoretical and experimental perspectives, contributing to a better understanding of the building blocks required for the establishment of a global quantum internet. We also introduce a newly developed quantum network toolkit to facilitate the exploration and evaluation of innovative ideas. Particularly, it provides dual quantum computing engines, supporting simulations in both the quantum circuit and measurement-based models. It also includes a compilation scheme for mapping quantum network protocols onto quantum circuits, enabling their emulations on real-world quantum hardware devices. We showcase the power of this toolkit with several featured demonstrations, including a simulation of the Micius quantum satellite experiment, a testing of a four-layer quantum network architecture with resource management, and a quantum emulation of the CHSH game. We hope this work can give a better understanding of the state-of-the-art development of quantum networks and provide the necessary tools to make further contributions along the way.
"Proper" Shift Rules for Derivatives of Perturbed-Parametric Quantum Evolutions

Banchi &amp; Crooks (Quantum, 2021) have given methods to estimate derivatives of expectation values depending on a parameter that enters via what we call a "perturbed" quantum evolution <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>x</mml:mi><mml:mo stretchy="false">&amp;#x21A6;</mml:mo><mml:msup><mml:mi>e</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>i</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>x</mml:mi><mml:mi>A</mml:mi><mml:mo>+</mml:mo><mml:mi>B</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi class="MJX-variant">&amp;#x210F;</mml:mi></mml:mrow></mml:msup></mml:math>. Their methods require modifications, beyond merely changing parameters, to the unitaries that appear. Moreover, in the case when the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>B</mml:mi></mml:math>-term is unavoidable, no exact method (unbiased estimator) for the derivative seems to be known: Banchi &amp; Crooks's method gives an approximation.In this paper, for estimating the derivatives of parameterized expectation values of this type, we present a method that only requires shifting parameters, no other modifications of the quantum evolutions (a "proper" shift rule). Our method is exact (i.e., it gives analytic derivatives, unbiased estimators), and it has the same worst-case variance as Banchi-Crooks's.Moreover, we discuss the theory surrounding proper shift rules, based on Fourier analysis of perturbed-parametric quantum evolutions, resulting in a characterization of the proper shift rules in terms of their Fourier transforms, which in turn leads us to non-existence results of proper shift rules with exponential concentration of the shifts. We derive truncated methods that exhibit approximation errors, and compare to Banchi-Crooks's based on preliminary numerical simulations.
A Conceptual Framework for Studying Factors that Influence Digital Technology Adoption Among Manufacturing Sector: Leadership Effectiveness as a Mediating Variable

Purpose: The objective of this study was to explore the current literature on the adoption of digital technology and emphasized the importance of leadership effectiveness in the E&amp;E Manufacturing sector context. Theoretical framework: To examine how organizations will respond to introducing new technologies in manufacturing firms, this study established two mainstream management theories, the Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI), to develop a conceptual framework. Design/methodology/approach: this study extensively reviewed the literature to identify the important factors influencing digital technologies adoption in the E&amp;E manufacturing sector. Findings: This study sought to develop a conceptual framework within the context of E&amp;E manufacturing companies and to identify the factors that influence the adoption of digital technology. This research indicates that leadership style and psychological traits appear to play a significant role in an organization's openness to new technologies. Research, Practical &amp; Social implications: The study's findings may contribute to the manufacturing management practices in performing a more significant strategic role toward Industry Revolution 4.0 requirements. Originality/value: Considering the digital transformation, the leadership field needs more sensitive research in institutional and cultural contexts, focusing on how leadership effectiveness may mediate digital technologies adoption among organizations.
Implementasi Metode Fuzzy Time Series Untuk Meramalkan Jumlah Ekspor Produk Kopi Dari Indonesia

The current trade era opens up opportunities for trading Indonesian coffee in the international market. The number of future exports can be predicted by knowing past export prices. The method that can be used to predict the number of Indonesian coffee exports to various countries is the Fuzzy Time Series method. The Fuzzy Time Series method, the forecasting technique, utilizes fuzzy sets as the basis for the prediction process. In this study, the forecasting stages include defining Xmax and Xmin, determining how many and how long class intervals are, defuzzification and defining fuzzy sets, fuzzifying data on the number of coffee exports, determining fuzzy logic relationships (FLR), establishing fuzzy logical groups (FLRG) relationships, defuzzification, and perform forecasting calculations. Using the Fuzzy Time Series Method, the results of forecasting Indonesia's coffee exports for the following year are 376371.3 tons of coffee products to be exported so that exporters can see this opportunity Keywords: Coffee, export, forecasting, fuzzy time series