Reviewerly/exHarmony huggingface dataset:
What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&amp;A communities

Because of the growing involvement of communities from various disciplines, data science is constantly evolving and gaining popularity. The growing interest in data science-based services and applications presents numerous challenges for their development. Therefore, data scientists frequently turn to various forums, particularly domain-specific Q&A websites, to solve difficulties. These websites evolve into data science knowledge repositories over time. Analysis of such repositories can provide valuable insights into the applications, topics, trends, and challenges of data science.In this article, we investigated what data scientists are asking by analyzing all posts to date on DSSE, a data science-focused Q&A website. To discover main topics embedded in data science discussions, we used latent Dirichlet allocation (LDA), a probabilistic approach for topic modeling.As a result of this analysis, 18 main topics were identified that demonstrate the current interests and issues in data science. We then examined the topics' popularity and difficulty. In addition, we identified the most commonly used tasks, techniques, and tools in data science. As a result, "Model Training", "Machine Learning", and "Neural Networks" emerged as the most prominent topics. Also, "Data Manipulation", "Coding Errors", and "Tools" were identified as the most viewed (most popular) topics. On the other hand, the most difficult topics were identified as "Time Series", "Computer Vision", and "Recommendation Systems". Our findings have significant implications for many data science stakeholders who are striving to advance data-driven architectures, concepts, tools, and techniques.
Predicting the Blockchain Technology Acceptance in Supply Chains with Inter-Firm Perspective: An Integrated DEMATEL and PLS-SEM Approach

Purpose Blockchain technology is one of the candidate technologies for resolving supply chain issues that may occur as a result of the complex and continuously changing market structures of the present day. In addressing the problems encountered in such a system, blockchain technology has emerged as a target technology due to its notable advantages, such as smart contracts and product traceability. Since a supply chain consists of suppliers, manufacturers, retailers, and industrial customers, blockchain technology can be considered inter-firm technology. However, the literature mainly focuses on technical, individual, and environmental aspects of technology acceptance depending on technology acceptance theories. Therefore, more information is needed about how inter-firm relations and their aspects affect organizational blockchain technology acceptance. Consequently, this study aims to identify the inter-firm characteristics and their influence on the acceptance of blockchain technologies in supply chains using network theory.
DOLG-NeXt: Convolutional neural network with deep orthogonal fusion of local and global features for biomedical image segmentation

Biomedical image segmentation (BMIS) is an essential yet challenging task for the visual analysis of biomedical images. Modern deep learning-based architectures, such as UNet, UNet-based variants, Transformers-based networks, and their combinations, have achieved reasonable success in BMIS. However, they still face certain shortcomings in extracting fine-grained features. They are also limited by scenarios where the modeling of local and global feature representations needs to be optimized correctly for spatial dependency in the decoding process, which can result in duplicate data utilization throughout the architecture. Besides, Transformer-based models lack inductive bias in addition to the complexity of the models. As a result, it can perform unsatisfactorily in a lesser biomedical image setting. This paper proposes a novel encode-decoder architecture named DOLG-NeXt, incorporating three major enhancements over the UNet-based variants. Firstly, we integrate squeeze and excitation network (SE-Net)-driven ConvNeXt stages as encoder backbone for effective feature extraction. Secondly, we employ a deep orthogonal fusion of local and global (DOLG) features module in the decoder to retrieve fine-grained contextual feature representations. Finally, we construct a SE-Net-like lightweight attention network alongside the DOLG module to provide refined target-relevant channel-based feature maps for decoding. To objectively validate the proposed DOLG-NeXt method, we perform extensive quantitative and qualitative analysis on four benchmark datasets from different biomedical image modalities: colonoscopy, electron microscopy, fluorescence, and retinal fundus imaging. DOLG-NeXt achieves a dice coefficient score of 95.10% in CVC-ClinicDB, 95.80% in ISBI 2012, 94.77% in 2018 Data Science Bowl, and 84.88% in the DRIVE dataset, respectively. The experimental analysis shows that DOLG-NeXt outperforms several state-of-the-art models for BMIS tasks.
A data-driven fuzzy system for the automatic determination of fuzzy set type based on fuzziness

Existing research mainly uses prior knowledge to set all fuzzy sets in a fuzzy system to the same type. In data-driven fuzzy modeling, automatically determining the type of fuzzy set is still a challenging problem. In this paper, a novel data-driven design method is proposed to automatically determine the types of fuzzy sets (type-1 fuzzy sets (T1-FSs) or interval type-2 fuzzy sets (IT2-FSs)) based on fuzziness. In the hybrid-type fuzzy system (HTFS), the fuzzy set type is determined by fuzziness to improve the performance of the fuzzy system, and the interpretability of the fuzzy system is determined by the integrity, distinguishability, and redundancy of the fuzzy sets. First, fuzzy clustering based on fuzzy entropy is used to initialize the rule base and the types of fuzzy sets. Second, social learning particle swarm optimization (SLPSO) is used to optimize the parameters of the HTFS, and the types of the fuzzy sets are determined by fuzziness, which means that the types of the fuzzy sets will change dynamically in the optimization process. Because the fuzzy set types change, a parameter change strategy is also proposed for adaptive fuzzy set types. Finally, the HTFS is compared with the T1 fuzzy system on 10 real-world datasets, and it is shown that the HTFS has better performance than the T1 fuzzy system. Comparing the HTFS with other advanced methods, it is shown that the HTFS has better prediction performance while taking into account the interpretability of the fuzzy set distribution.
Energy profit evaluation of a photovoltaic system from a selected building in Jordan

The purpose of this study is to find the most profitable way to construct a photovoltaic (PV) system on a residential building in Amman by taking into account the local climate, the average monthly energy use, and the rates charged by the local electric utility. The building has a monthly energy usage of 550 kWh on average, with the biggest demand for energy occurring in the months of January and December and the lowest demand occurring in the months of April, May, and June. The photovoltaic (PV) system is mounted at a variety of angles, including 10°, 20°, 32°, and 54°. This study took into account the fact that the price of electrical energy is dependent on the demand for electricity in order to determine the periods of the day when there is the greatest need for energy. Another crucial aspect to consider is the sun's strength, which shifts during the course of the year. When solar intensity is at its peak and power rates are at their highest, a PV system is considered as a realistic choice to satisfy energy needs. This is because PV systems can generate energy even in cloudy conditions. A comprehensive examination of the solar energy that is taken in by a PV cell and the electric energy that is produced as a result of this is also included in the study.
On Single-Model Transferable Targeted Attacks: A Closer Look at Decision-Level Optimization

Known as a hard nut, the single-model transferable targeted attacks via decision-level optimization objectives have attracted much attention among scholars for a long time. On this topic, recent works devoted themselves to designing new optimization objectives. In contrast, we take a closer look at the intrinsic problems in three commonly adopted optimization objectives, and propose two simple yet effective methods in this paper to mitigate these intrinsic problems. Specifically, inspired by the basic idea of adversarial learning, we, for the first time, propose a unified Adversarial Optimization Scheme (AOS) to release both the problems of gradient vanishing in cross-entropy loss and gradient amplification in Po+Trip loss, and indicate that our AOS, a simple transformation on the output logits before passing them to the objective functions, can yield considerable improvements on the targeted transferability. Besides, we make a further clarification on the preliminary conjecture in Vanilla Logit Loss (VLL) and point out the problem of unbalanced optimization in VLL, in which the source logit may risk getting increased without the explicit suppression on it, leading to the low transferability. Then, the Balanced Logit Loss (BLL) is further proposed, where we take both the source logit and the target logit into account. Comprehensive validations witness the compatibility and the effectiveness of the proposed methods across most attack frameworks, and their effectiveness can also span two tough cases (i.e., the low-ranked transfer scenario and the transfer to defense methods) and three datasets (i.e., the ImageNet, CIFAR-10, and CIFAR-100). Our source code is available at https://github.com/xuxiangsun/DLLTTAA.
Delving into EFL teachers’ digital literacy and professional identity in the pandemic era: Technological Pedagogical Content Knowledge (TPACK) framework

The recent decades have witnessed an accelerated pace of educational development due to the advancement of digital technology. The recent inclusive spread of the COVID-19 pandemic has influenced this development, resulting in the emergence of an educational revolution that extensively uses online courses. These changes entail figuring out how teachers' digital literacy has expanded along with this phenomenon. In addition, given the new technological advances in recent years, an upheaval has taken place in teachers' understanding of their dynamic roles, which is known as professional identity. Professional identity influences teaching practices with special consideration devoted to English as a Foreign Language (EFL). Technological Pedagogical Content Knowledge (TPACK) is considered an effective framework whereby the incorporation of technology into different theoretical situations such as EFL classes is understood. This initiative was introduced as an academic structure to improve the knowledge base, which helps the teachers to efficiently teach using technology. This yields important insight for teachers, particularly, English teachers, who can use them to improve three aspects of education, namely, technology, pedagogy, and content knowledge. Along the same lines, this paper aims to consider the relevant literature on the contribution of teacher identity and literacy to teaching practices, using the TPACK framework. Accordingly, some implications are presented to educational stakeholders such as teachers, learners, and material developers.
Hyperspectral image denoising via spectral noise distribution bootstrap

Hyperspectral image (HSI) denoising is an ill-posed problem, leading to integrating proper prior knowledge about hyperspectral noise is critical to developing an efficient denoising method. Most existing methods share a common assumption that all bands have equal noise intensity. However, such assumption runs counter to the practical HSIs, leading to unpleasant denoising results. To tackle this, we intend to investigate the intrinsic properties of real HSI noise in the spectral dimension and construct a novel denoising framework bootstrapping by spectral noise distribution N^, termed N^-Net. On the one hand, we develop dense and sparse recurrent calculations, exploiting intrinsic properties of HSI noise (i.e., diversity, dense dependency, and global sparsity) to estimate spectral noise distribution. On the other hand, having the estimated spectral noise distribution, we develop a bootstrap mechanism with a repetitive emphasis on its guidance for subsequent spatial noise separation and clean HSI recovery, ensuring a more delicate denoising effect. In particular, we verify that the proposed denoising framework can achieve promising denoising performances due to the merit of spectral noise distribution bootstrapping, which also promotes new insights for future related research. The code is avaliable at https://github.com/EtPan/N-Net.
Ensemble feature selection using Bonferroni, OWA and Induced OWA aggregation operators

Feature selection becomes inevitable owing to a rapid increase in digital technology which permits the generation of high dimensional data in a large quantity within a short time. Feature selection techniques not only improves classification accuracy but also decreases time complexity as well as computation cost and storage. Ensemble feature selection has lately emerged as a potential approach to data mining. Identifying numerous optimal features is one of the key advantages of ensemble feature selection. The objective of this study is to introduce an ensemble technique based on the rank aggregation procedure. Four operators are used for the purpose of aggregation, namely the induced ordered weighted averaging (IOWA) operator, the normalized Bonferroni weighted mean operator, the Bonferroni-ordered weighted averaging operator and the Bonferroni-induced ordered weighted averaging operator. These operators enable the evaluation of continuous aggregations, multiple assessments between each input and distance measurements in the same formulation. An objective weighting approach denoted as the entropy weight method, is utilized to measure the degree of disorder. A total of 10 benchmark data sets are employed to evaluate the superiority of the proposed methodologies. The effectiveness of the proposed method is evaluated and compared using the accuracy, F-measure, precision and recall performance metrics, and better results than those from other existing techniques are obtained.
KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. While primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.
On-chip spin-photon entanglement based on photon-scattering of a quantum dot

The realization of on-chip quantum gates between photons and solid-state spins is a key building block for quantum-information processors, enabling, e.g., distributed quantum computing, where remote quantum registers are interconnected by flying photons. Self-assembled quantum dots integrated in nanostructures are one of the most promising systems for such an endeavor thanks to their near-unity photon-emitter coupling and fast spontaneous emission rate. Here we demonstrate an on-chip entangling gate between an incoming photon and a stationary quantum-dot spin qubit. The gate is based on sequential scattering of a time-bin encoded photon with a waveguide-embedded quantum dot and operates on sub-microsecond timescale; two orders of magnitude faster than other platforms. Heralding on detection of a reflected photon renders the gate fidelity fully immune to spectral wandering of the emitter. These results represent a major step in realizing a quantum node capable of both photonic entanglement generation and on-chip quantum logic, as demanded in quantum networks and quantum repeaters.
Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

In a fully mechanized mining face, there is interference between the hydraulic support face guard and the shearer drum. The two collisions seriously affect coal mine production and personnel safety. The identification of a shearer drum can be affected by fog generated when the shearer drum cuts forward. It is hydraulic support face guard recovery, not the timely block shearer drum, that will also affect the recognition of the shearer drum. Aiming at the above problems, a shearer drum identification method based on improved YOLOv5s with dark channel-guided filtering defogging is proposed. Aiming at the problem of fog interference affecting recognition, the defogging method for dark channel guided filtering is proposed. The optimal value of the scene transmittance function is calculated using guided filtering to achieve a reasonable defogging effect. The Coordinate Attention (CA) mechanism is adopted to improve the backbone network of the YOLOv5s algorithm. The shearer drum features extracted by the C3 module are reallocated by the attention mechanism to the weights of each space and channel. The information propagation of a shearer drum’s features is enhanced by such improvements. Thus, the detection of shearer drum targets in complex backgrounds is improved. S Intersection over Union (SIoU) is used as a loss function to improve the speed and accuracy of the shearer drum. To verify the effectiveness of the improved algorithm, multiple and improved target detection algorithms are compared. The algorithm is deployed at Huangling II mine. The experimental results present that the improved algorithm is superior to most target detection algorithms. In the absence of object obstruction, the improved algorithm achieved 89.3% recognition accuracy and a detection speed of 48.8 frame/s for the shearer drum in the Huangling II mine. The improved YOLOv5s algorithm provides a basis for identifying interference states between the hydraulic support face guard and shearer drum.
Technology acceptance and COVID-19: a perspective for emerging opportunities from crisis

Ingredients for leadership development include strategic intent, sensibility, knowledge, analytic acuity, and the confidence to make tough calls. Emergencies highlight the importance of technical progress and the opportunity to enhance design and productivity. Significant technological and organisational barriers hinder acceptance of these technologies; hence it is necessary to employ strategy. Research should be at the system’s heart through planned and unplanned transitions. The vision of building and maintaining business resilience and organising swift changes includes using perceptual methods. With this view, leaders can make deliberate decisions. It also tends to disrupt traditional approaches to dexterity. At the same time, strategic flexibility necessitates an evident connection between the business and the institution’s mission. A leader’s positive strategic intent is core that better enables investment in crisis response competencies. It includes timely use of knowledge management, organisational learning frameworks, business strategy, and system agility. Many nations in the developing world have simultaneously experienced the rise of digitisation and the spread of the covid-19 epidemic. Thus, academics should focus on the difficulties and prospects associated with people, groups, and management.
Blockchain and Machine Learning: A Critical Review on Security

Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard data, it is not completely reliable. Various elements, including the particular use case, the type of data, and legal constraints can determine whether it is suitable for keeping private and sensitive data. While there may be benefits, it is important to take into account possible hazards and abide by privacy and security laws. The blockchain itself is secure, but additional applications and layers are not. In terms of security, ML can aid in the development of blockchain applications. Therefore, a critical investigation is required to better understand the function of ML and blockchain in enhancing security. This study examines the current situation, evaluates the articles it contains, and presents an overview of the security issues. Despite their existing limitations, the papers included from 2012 to 2022 highlighted the importance of ML’s impact on blockchain security. ML and blockchain can enhance security, but challenges remain; advances such as federated learning and zero-knowledge proofs are important, and future research should focus on privacy and integration with other technologies.