Reviewerly/exHarmony huggingface dataset:
A Systematic Framework for Addressing Critical Challenges in Adopting DevOps Culture in Software Development: A PLS-SEM Perspective

DevOps is the bundle of applications and culture activity that focuses to separate the challenges between evaluation and performance associates to boost participation and communication. Several companies clutch DevOps fundamentals due to tremendous perspectives, like small tempo for production, rise accuracy, and durability. In contempt of the global assumption of DevOps and its framework, there is an absence of interpretation studies and essential ideas, implementation, and issues related to executing DevOps procedures. The major objective of this research study is to investigate and study issues affiliated with the DevOps culture (DVC) and applications. The challenges that are challenging in adopting DevOps culture in software development (SD) are inspected by conducting the systematic literature review (SLR). Furthermore, it explains the working of DevOps in industries, gives full clarification of DevOps, and probes the cultural issues faced by the industries while executing DevOps. This study discloses ten barriers that should be declamation in accepting DevOps culture. In accordance with the discoveries, these ten barriers contemplate during the execution of DevOps culture i.e. lack of collaboration and coordination, Lack of skill and knowledge, complicated infrastructure, Lack of management, Lack of DevOps approach, and trust confidence problems.
A robust and accurate centerline extraction method of multiple laser stripe for complex 3D measurement

Multiple laser stripe measurement (MLSM) is a vital technique in optical three-dimensional (3D) measurement. The accurate extraction of the laser stripe centerlines plays a decisive role in achieving high measurement accuracy. However, complex 3D measurement scenarios pose challenges due to various interferences, such as optical interference caused by dense stripes and no-ideal distribution caused by non-uniform reflectivity. To address these challenges, we propose an adaptive step size gradient centerline extraction method based on an 2D linear filter. This method incorporates local adaptive variable parameters for fine-tuning while utilizing global parameters. We validate its performance by conducting comprehensive comparative experiments with two commonly used methods. The results demonstrate that our proposed method enables more precise centerline extraction, exhibits enhanced stability, and yields lower measurement errors in complex 3D measurement conditions and highlight the effectiveness and superior performance of our method.
Monte Carlo and Temporal Difference Methods in Reinforcement Learning [AI-eXplained]

Reinforcement learning (RL) is a subset of machine learning that allows intelligent agents to acquire the ability of executing desired actions through interactions with an environment. Its remarkable progress has achieved significant results in diverse domains, such as Go and StarCraft, and practical challenges like protein-folding. This short paper presents overviews of two common RL approaches: the Monte Carlo and temporal difference methods. To obtain a more comprehensive understanding of these concepts and gain practical experience, readers can access the full article on IEEE Xplore, which includes interactive materials and examples.
Stable first-arrival picking through mathematical morphology and edge detection

SUMMARY First-arrival picking is a fundamental and challenging task in seismic data processing. Existing algorithms such as the cross-correlation algorithm (CCT) struggle to achieve satisfactory noise immunity while maintaining picking efficiency. In this paper, we propose the first-arrival picking through mathematical morphology and edge detection (FPME) algorithm with three steps. The converting step transforms the original data into a binary image though a filtering technique, where the binary image represents the global energy distribution of high-energy and low-energy samples. The rendering step delimits the signal zone in the binary image through morphological operations, where the signal zone eliminates the noise in the upper part. The picking step detects first arrivals in the signal zone through the edge detection methods, where an evaluation function is designed to select the optimal detection results as first arrivals. Experiments were performed on four field data sets. The results demonstrated that FPME is more stable and reliable than five classic and popular algorithms.
Sand cat arithmetic optimization algorithm for global optimization engineering design problems

Abstract Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance.
Integrated information management of supply chain in logistics parks based on the <scp>IoT</scp>

Abstract Logistics park is a strategic choice for the growth of logistics industry, and it needs to build a public platform for supply chain management (SCM) suitable for logistics parks. To solve the problems of poor adaptability, high investment risk, and incomplete functionality of the supply chain integrated information management (IM) platform in logistics parks, it is necessary to use Internet of Things (IoT) technology to improve the supply chain integrated IM platform, improve the timely interaction of logistics information and reduce logistics costs. This article analyzed the factors that affect the integrated management of logistics park supply chain, analyzed the architecture of logistics park supply chain, integrated IM under the IoT, and implemented the platform architecture using relevant technologies. In the experiment, the resource allocation, resource scheduling, and resource monitoring effects of the supply chain integrated IM platform were analyzed. The results showed that the supply chain IM platform of logistics parks under the IoT has improved infrastructure by 0.14 compared to traditional platform, resource allocation by 0.16, logistics service level by 0.14, and industrial layout by 0.14. The IoT can effectively enhance the resource allocation and industrial layout rationality of logistics parks and has important significance in improving logistics information security and information exchange.
Contextualizing entrepreneurship for Africa's post-Covid-19 recovery and growth

Entrepreneurship plays an important role in employment, income generation, food security, empowerment, job creation, and productivity growth in Africa. However, many businesses in Africa are heavily dependent on foreign direct investment and channels both for inputs and markets and lack economic resilience and diversification. Consequently, the complete lockdown and frontier closure due to the COVID-19 pandemic resulted in a deteriorating impact on its formal and informal economic activities. The purpose of the paper is therefore to examine the challenges and responses of the African economy to COVID-19 and if the contextualized entrepreneurship approach is important in the post-COVID recovery and growth in Africa. The paper used secondary data collected through a systematic and contextual review of the latest publications and reports. The findings show that Africa is challenged with a lack of managerial skills, infrastructure, capital markets, access to finance, and incentives for entrepreneurship. Moreover, a realistic, contextualized, and sustainable trajectory recovery and development plan is critical to tackle the scale and depth of the COVID-19 challenges in Africa. In this regard, this paper provides important recommendations for African-oriented entrepreneurship development pathways and policies. This will have important implications for the post-COVID-19 recovery and the growth of entrepreneurship in Africa.
Leveraging BERTopic for the Analysis of Scientific Papers on Seaweed

Seaweed has many benefits, serving as a source of highly nutritious food, raw material for the cosmetic industry, pharmaceuticals, and animal feed. Seaweed also plays a crucial role in maintaining environmental ecosystems. Therefore, it is important to understand research trends concerning seaweed. BERTopic is a commonly used method in topic modeling for analyzing scientific publication data. This study aims to find the best model using BERTopic for analyzing scientific publications related to seaweed. This research tests and evaluates the performance of methods such as stopword removal, embedding, dimensionality reduction, and clustering based on topic coherence values to obtain the optimal model. The test results indicate that removing stopwords using CountVectorizer, employing SCIBERT as the transformer, utilizing UMAP as the dimensionality reduction algorithm, and applying k-means as the clustering algorithm yield the best coherence values for analyzing scientific publications related to seaweed.
Automating Information Retrieval from Faculty Guidelines: Designing a PDF-Driven Chatbot powered by OpenAI ChatGPT

Educational materials, including guides, tutorials, and master plans, are universally presented in e-book formats. This facilitates a comprehensive understanding for academics, encompassing both technical intricacies and broader conceptual frameworks. E-books offer several benefits, such as searchability and the incorporation of links to additional information sources. However, many individuals express concerns that e-books are not particularly comfortable for extended reading periods. In the other hand, a Generative AI approach is employed for the development of an intelligent chatbot. Our primary contribution lies in an automated information retrieval method, involving the design of a PDF-Driven Chatbot using Large Language Models (LLMs) in the context of faculty guidelines question answering. This research utilizes the LangChain Framework, OpenAI’s Chat-GPT (GPT3.5 Turbo), and Pinecone for generating responses. The outcomes demonstrate that the chatbot is capable of generating coherent responses closely aligned with the context of the PDF document.
All-photonic Gottesman-Kitaev-Preskill–qubit repeater using analog-information-assisted multiplexed entanglement ranking

Long-distance quantum communication will require the use of quantum repeaters to overcome the exponential attenuation of signal with distance. One class of such repeaters utilizes quantum error correction to overcome losses in the communication channel. Here we propose a strategy of using the bosonic Gottesman-Kitaev-Preskill (GKP) code in a two-way repeater architecture with multiplexing. The crucial feature of the GKP code that we make use of is the fact that GKP qubits easily admit deterministic two-qubit gates, hence allowing for multiplexing without the need for generating large cluster states as required in previous all-photonic architectures based on discrete-variable codes. Moreover, alleviating the need for such clique clusters entails that we are no longer limited to extraction of at most one end-to-end entangled pair from a single protocol run. In fact, thanks to the availability of the analog information generated during the measurements of the GKP qubits, we can design better entanglement swapping procedures in which we connect links based on their estimated quality. This enables us to use all the multiplexed links so that large number of links from a single protocol run can contribute to the generation of the end-to-end entanglement. We find that our architecture allows for high-rate end-to-end entanglement generation and is resilient to imperfections arising from finite squeezing in the GKP state preparation and homodyne detection inefficiency. In particular we show that long-distance quantum communication over more than 1000 km is possible even with less than 13 dB of GKP squeezing. We also quantify the number of GKP qubits needed for the implementation of our scheme and find that for good hardware parameters our scheme requires around ${10}^{3}\ensuremath{-}{10}^{4}$ GKP qubits per repeater per protocol run.
Exploring the Interplay of the Physical Environment and Organizational Climate in Innovation

The intricate relationship between physical and social environments within organizations plays a pivotal role in shaping innovation endeavors. This paper introduces a three-dimensional framework aimed at comprehending the intricate mechanisms through which the physical and social environments synergistically drive innovation. Building on this, a systematic four-dimensional framework (communality, individuality, comfort, and health) is proposed to structure a comprehensive literature review, mapping out the intricate linkages between innovation and the physical environment. Through this extensive review, we delve into the intricate connections between the physical innovation environment and the broader innovation climate, unearthing valuable insights. Additionally, we highlight two promising directions for future research within the realm of physical environment–innovation climate interactions. Furthermore, we underscore the paramount importance of embracing an interdisciplinary approach, seamlessly blending perspectives from both the physical and social spheres to gain a holistic and nuanced understanding of the innovation landscape. This integrated viewpoint is pivotal to unraveling the multifaceted dynamics that underlie successful innovation initiatives.
Getting ready for teaching with digital technologies: Scenario-based self-assessment in teacher education and professional development

Abstract Teachers’ technology-related skills are often measured with self-assessments. However, self-assessments are often criticised for being inaccurate and biased. Scenario-based self-assessment is a promising approach to make self-assessment more accurate and less biased. In this study with N = 552 inservice and student teachers, we validated a scenario-based self-assessment instrument IN.K19 + for teachers. The instrument enables scenario-based self-assessment of instrumental and critical digital skills and technology-related teaching skills for teachers. In a confirmatory factor analysis, we show that the instrument has sufficient factorial validity. To test the predictive validity of the instrument, we examined the instruments’ relationship to the frequency of technology use during teaching and teacher-initiated student learning activities involving digital technologies. Results from structural equation modelling show that instrumental digital skills and technology-related teaching skills are positively related to the frequency of digital technology use during teaching, while critical digital skills are not. In terms of the initiation of student learning activities, instrumental and critical digital skills show relationships with initiating student learning activities that include lower cognitive engagement. Technology-related teaching skills are related to initiating learning activities that indicate higher cognitive engagement. The results show that instrumental and critical digital skills play an important role with respect to the basic use of digital technologies in the classroom, while technology-related teaching skills turn out to be crucial for more complex scenarios of digital technology use. This pattern of findings supports the predictive validity of the IN.K19 + instrument.
Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.
Calibration methods to fit parameters within complex biological models

Mathematical and computational models of biological systems are increasingly complex, typically comprised of hybrid multi-scale methods such as ordinary differential equations, partial differential equations, agent-based and rule-based models, etc. These mechanistic models concurrently simulate detail at resolutions of whole host, multi-organ, organ, tissue, cellular, molecular, and genomic dynamics. Lacking analytical and numerical methods, solving complex biological models requires iterative parameter sampling-based approaches to establish appropriate ranges of model parameters that capture corresponding experimental datasets. However, these models typically comprise large numbers of parameters and therefore large degrees of freedom. Thus, fitting these models to multiple experimental datasets over time and space presents significant challenges. In this work we undertake the task of reviewing, testing, and advancing calibration practices across models and dataset types to compare methodologies for model calibration. Evaluating the process of calibrating models includes weighing strengths and applicability of each approach as well as standardizing calibration methods. Our work compares the performance of our model agnostic Calibration Protocol (CaliPro) with approximate Bayesian computing (ABC) to highlight strengths, weaknesses, synergies, and differences among these methods. We also present next-generation updates to CaliPro. We explore several model implementations and suggest a decision tree for selecting calibration approaches to match dataset types and modeling constraints.
MNN4Rec: A relation-aware approach based on multi-view news network for news recommendation

Personalised news recommendation comprises two crucial components: news understanding and user modelling. Previous studies have attempted to model news understanding and user interests using various internal news information and external knowledge graphs (KG). However, they have overlooked the collaborative function of the external KG and the internal information among diverse news and user behaviours, resulting in serious news cold-start problems and poor interpretability of user interests. To address these issues, this article proposes a novel approach called Relation-Aware Approach based on Multi-view News Network for News Recommendation (MNN4Rec). Specifically, MNN4Rec first constructs a Multi-view News Network (MNN), which includes candidate news and user-clicked news, and represents their exclusive multi-view information as heterogeneous nodes. Furthermore, we develop explicit and implicit news relationships and design a special sampling algorithm to search for news co-neighbours. We then use a novel dual-channel graph attention mechanism to obtain the fine-grained news understanding representation. Moreover, we construct explainable user interests by modelling the interaction of user-clicked news through the multi-headed self-attention mechanism in both semantic and relation levels. Finally, we match candidate news understanding with user interests to generate a prediction score for recommendation. Experimental results on Microsoft’s news data set MIND demonstrate that MNN4Rec outperforms existing news-recommendation methods while also mitigating the cold-start problem and enhancing the interpretability of user interests. Our code is available at https://github.com/JiangHaoPG11/MNN4Rec_code .
Efficient Model-Free Subsampling Method for Massive Data

Subsampling plays a crucial role in tackling problems associated with the storage and statistical learning of massive datasets. However, most existing subsampling methods are model-based, which means their performances can drop significantly when the underlying model is misspecified. Such an issue calls for model-free subsampling methods that are robust under diverse model specifications. Recently, several model-free subsampling methods have been developed. However, the computing time of these methods grows explosively with the sample size, making them impractical for handling massive data. In this article, an efficient model-free subsampling method is proposed, which segments the original data into some regular data blocks and obtains subsamples from each data block by the data-driven subsampling method. Compared with existing model-free subsampling methods, the proposed method has a significant speed advantage and performs more robustly for datasets with complex underlying distributions. As demonstrated in simulation experiments, the proposed method is an order of magnitude faster than other commonly used model-free subsampling methods when the sample size of the original dataset reaches the order of 107. Moreover, simulation experiments and case studies show that the proposed method is more robust than other model-free subsampling methods under diverse model specifications and subsample sizes.
Challenges of Network Forensic Investigation in Fog and Edge Computing

While network forensics has matured over the decades and even made progress in the last 10 years when deployed in virtual networks, network forensics in fog and edge computing is still not progressed to that level despite the now widespread use of these paradigms. By using an approach similar to software testing, i.e., a mixture of systematic and experience, we analyze obstacles specific to forensics in fog and edge computing such as spatial dispersion and possibly incomplete recordings, and derive how far these obstacles can be overcome by adapting processes and techniques from other branches of network forensics, and how new solutions could look otherwise. In addition, we present a discussion of open problems of network forensics in fog and edge environments and discusses the challenges for an investigator.
Coherence-Based Operational Nonclassicality Criteria

The nonclassicality of quantum states is a fundamental resource for quantum technologies and quantum information tasks, in general. In particular, a pivotal aspect of quantum states lies in their coherence properties, encoded in the nondiagonal terms of their density matrix in the Fock-state bosonic basis. We present operational criteria to detect the nonclassicality of individual quantum coherences that use only data obtainable in experimentally realistic scenarios. We analyze and compare the robustness of the nonclassical coherence aspects when the states pass through lossy and noisy channels. The criteria can be immediately applied to experiments with light, atoms, solid-state system, and mechanical oscillators, thus providing a toolbox allowing practical experiments to more easily detect the nonclassicality of generated states.