Reviewerly/exHarmony huggingface dataset:
PageRank Algorithm Based on Dynamic Damping Factor

The search engine ranking algorithm plays a significant role in enhancing the user experience by effectively ranking search pages. Among various algorithms, the PageRank algorithm has emerged as the dominant choice owing to its simplicity and efficiency. An essential parameter in the PageRank algorithm is the damping factor, which represents the time that random web visitors spend following the hyperlink structure and significantly influences the algorithm’s performance. However, existing methods often set the damping factor empirically, overlooking the relevance of web visitors’ topics. This oversight becomes problematic as users are increasingly exposed to noisy chains and overwhelmed by excessive hyperlinks, leading to a decline in the PageRank algorithm’s performance and a diminished user experience. To tackle this challenge, we propose an adaptive dynamic damping factor based on the web browsing context, enhancing the effectiveness of the optimized PageRank algorithm. Through experimental analysis, we results demonstrate that the dynamic damping coefficient-based PageRank algorithm effectively mitigates the impact of noisy web pages on query results and significantly improves the algorithm’s convergence speed.
JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning

In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users’ career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. However, solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles in real-world applications. To this end, we propose a novel solution, named JAMES, that constructs three unique embeddings (i.e., graph, contextuat, and syntactic) of a target job title to effectively capture its various traits. We further propose a multi-aspect co-attention mechanism to attentively combine these embeddings, and employ neural logical reasoning representations to collaboratively estimate similarities between messy job titles and normalized job titles in a reasoning space. To evaluate JAMES, we conduct comprehensive experiments against ten competing models on a large-scale real-world dataset with over 350,000 job titles. Our experimental results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively. To further facilitate the acquisition of normalized job titles for job-domain applications, our JAMES API is available at: https://tinyurl.con JAMES-job-title-mapping.
Direct tomography of high-dimensional entangled states

Weak measurement enables the direct measurement of the wavefunction by associating the weak values with the probability amplitudes, which is known as Direct Tomography (DT). Compared to standard quantum state tomography, DT requires far fewer measurement bases and avoids reconstruction algorithm, showing distinct advantages in the characterization of high-dimensional quantum states. Here, we propose a general parallel extension of the DT protocol for entangled systems involving multiple particles with the probability amplitudes corresponding to the joint weak values. By coupling each particle of the quantum system with the independent meter state, we obtain joint weak values of nonlocal observables through collective measurements on the post-selected meter states. Our rigorous derivation allows for large coupling strengths, which significantly reduces statistical errors. We experimentally demonstrate the feasibility of our protocol by performing DT of path-entangled photonic states in three dimensions, achieving fidelities between the results of DT and those of standard quantum state tomography of over 95%. Our results provide a powerful tool for tomography of entangled states and investigation of their nonlocal properties.
Research on image de-fogging algorithm for rocket recovery

In the rocket recovery environment, the imaging conditions are complicated, and the vision equipment is prone to problems such as ground target information loss, image contrast and saturation reduction, color deviation and so on, which directly affect the solution of the rocket pose. Therefore, it is necessary to study the de-fog algorithm for the special environment of rocket recovery, so as to contribute to the solution of rocket pose and altitude measurement. Through the investigation of previous research on de-fogging algorithms, it can be found that the dark channel de-fogging algorithm is not only simple in principle but also has better de-fogging effect in changeable environment. In addition, the brightness of fog in rocket recovery is often affected by the flame emitted from the nozzle, and a single dark channel de-fogging algorithm cannot adjust the overall brightness according to the environmental characteristics, resulting in blurred feature points in the image after de-fogging. Therefore, considering that RETINEX theory can be used to adjust the brightness of images, in combination with the dark channel prior model, this paper mainly uses RETINEX theory to improve the dark channel de-fogging theory to process the transmission t(x), so that the final image retains the feature information to the maximum extent, and improves the adaptability of the dark channel de-fogging algorithm to different fog environments. Finally, the removal effect of different de-fogging algorithms on different concentrations of fog in rocket recovery environment is quantitatively analyzed. The experimental results show that the de-fogging algorithm in this paper can solve the impact of the fog generated by the rocket nozzle on the vision equipment.
Digital image-correlation for 2D displacement measurement based on unsupervised neural network

Digital Image Correlation (DIC) is a non-contact measurement technique for deformation with a long-studied challenge to find a balance between calculation efficiency and seed point quantity. Deep learning offers a new solution to improve DIC efficiency, and supervised learning DIC methods require high-quality training data, leading to challenges in ground-truth generation that can be time-consuming. We propose a DIC method for 2D displacement measurement based on unsupervised Convolutional Neural Network (CNN) to address the problem. A speckle image warp model is used to transform the target speckle image to the predicted reference speckle image according to the predicted 2D displacement map. The predicted and original reference speckle images are compared to achieve unsupervised training. Our proposed method eliminates the need for extensive training data annotation. We conducted several experiments to demonstrate its validity and robustness. The MAE and RMSE by unsupervised learning are only 0.0681 pixels and 0.0886 pixels, respectively, demonstrating the potential of our method to achieve accuracy that is comparable to supervised methods.
New Class of Conjugate Gradient Methods for Removing Impulse Noise Images

The conjugate coefficient optimal is the very establishment of a variety of conjugate gradient methods. This paper proposes a new class coefficient of conjugate gradient (CG) methods for impulse noise removal, which is based on the quadratic model. Our proposed method ensures descent independent of the accuracy of the line search and it is globally convergent under some conditions, Numerical experiments are also presented for the impulse noise removal in images.
Deep carbonate reservoir characterization using multi seismic attributes:A comparison of unsupervised machine learning approaches

Seismic reservoir characterization is of great interest for sweet spot identification, reservoir quality assessment, and geological model building. The sparsity of the labeled samples often limit the application of supervised machine learning for seismic reservoir characterization. Unsupervised learning methods, on the other hand, explore the internal structure of data and extract low-dimensional features of geologic interest from seismic data without the need for labels. We compare various unsupervised learning approaches, including the linear method PCA, manifold learning methods T-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), and the Convolutional Autoencoder (CAE), on both the 3D synthetic and field seismic data of a deep carbonate reservoir, SW China. On the synthetic data, the low-dimensional features extracted by UMAP and CAE provide a better indication of porosity and gas saturation than traditional seismic attributes. In particular, UMAP better preserves the global structure of geological features, and show the potential of decoupling the gas saturation and porosity effects from seismic responses. We demonstrate that by joint use of multi type of seismic attributes instead of using single type of seismic attributes can better delineate the reservoir structures using unsupervised ML. On the field seismic data, UMAP can effectively characterize sedimentary facies distribution, which is consistent with the geological understanding. Nevertheless, the porosity and saturation can not be reliably identified from field seismic data using unsupervised ML, which is likely to be caused by the complex pore structures in carbonates complicating the mapping relationship between seismic responses and reservoir parameters.
Monolithic Ion Trap Integrated with a Micro Optical Cavity for Quantum Networks

Monolithic fabrication of ion traps integrated with a micro optical cavity is the building block for large scale ion trap quantum network. The authors designed such traps and successfully trapped single ions. Cavity mirrors with the desired shape and quality are fabricated on optical fibre tips robustly.
Crypto-sentiment Detection in Malay Text Using Language Models with an Attention Mechanism

Background: Due to the increased interest in cryptocurrencies, opinions on cryptocurrency-related topics are shared on news and social media. The enormous amount of sentiment data that is frequently released makes data processing and analytics on such important issues more challenging. In addition, the present sentiment models in the cryptocurrency domain are primarily focused on English with minimal work on Malay language, further complicating problems. Objective: The performance of the sentiment regression model to forecast sentiment scores for Malay news and tweets is examined in this study. Methods: Malay news headlines and tweets on Bitcoin and Ethereum are used as the input. A hybrid Generalized Autoregressive Pretraining for Language Understanding (XLNet) language model in combination with Bidirectional-Gated Recurrent Unit (Bi-GRU) deep learning model is applied in the proposed sentiment regression implementation. The effectiveness of the proposed sentiment regression model is also investigated using the multi-head self-attention mechanism. Then, a comparison analysis using Bidirectional Encoder Representations from Transformers (BERT) is carried out. Results: The experimental results demonstrate that the number of attention heads is vital in improving the XLNet-GRU sentiment model performance. There are slight improvements of 0.03 in the adjusted R2 values with an average MAE of 0.163 (Malay news) and 0.174 (Malay tweets). In addition, an average RMSE of 0.267 and 0.255 were obtained respectively for Malay news and tweets, which show that the proposed XLNet-GRU sentiment model outperforms the BERT sentiment model with lesser prediction errors. Conclusion: The proposed model contributes to predicting sentiment on cryptocurrency. Moreover, this study also introduced two carefully curated Malay corpora, CryptoSentiNews-Malay and CryptoSentiTweets-Malay, which are extracted from news and tweets, respectively. Further works to enhance Malay news and tweets corpora on cryptocurrency-related issues will be expended with implementing the proposed XLNet Bi-GRU deep learning model for greater financial insight. Keywords: Cryptocurrency, Deep learning model, Malay text, Sentiment analysis, Sentiment regression model
Building expertise on FAIR through evolving Bring Your Own Data (BYOD) workshops: describing the data, software, and management- focused approaches and their evolution

Abstract Since 2014, “Bring Your Own Data” workshops (BYODs) have been organised to inform people about the process and benefits of making resources Findable, Accessible, Interoperable, and Reusable (FAIR, and the FAIRification process). The BYOD workshops’ content and format differ depending on their goal, context, and the background and needs of participants. Data-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research questions. Management-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification projects. Software-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support FAIRification. Overall, these BYODs intend to foster collaboration between different types of stakeholders involved in data management, curation, and reuse (e.g. domain experts, trainers, developers, data owners, data analysts, FAIR experts). The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and experience. In this paper, we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data, and rare disease registries and biobanks, which have shaped the structure of the workshops. We discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops, including successes and encountered challenges. Finally, we examine how the participants’ feedback has motivated the research on FAIR, including the development of workflows and software.
Topology analysis of the Ripple transaction network

Abstract The Ripple network is one typical blockchain‐based decentralized credit network, which supports money transfer without physical money movement by only transferring the credits between participants. It is critical to obtain a deep understanding on the characteristics of the payment networks while optimizing the network design and transaction routing. This paper presents a comprehensive analysis to the Ripple transaction network, including two subnets formed by the two key functionalities, that is, Ripple Direct Payment Network (RDPN) and Ripple Credit Payment Network (RCPN). The analysis is performed with different network metrics, including clustering coefficient, centrality, and so on. Furthermore, this paper provides an in‐depth analysis on the node degrees and edge weights, which reflect the number of transacted accounts of an account and the number of transactions between two accounts. The results show that the network is highly imbalanced and concentrated with a few nodes and edges holding most of the resources. Moreover, RDPN and RCPN show different characteristics in terms of transmitted and received transactions, the senders are more concentrated in RDPN, whereas in RCPN, the receivers are more concentrated.
How Zero Trust as a Service (ZTaaS) Reduces the Cost of a Breach

Nowadays, the cost of data breaches is immense for organizations and society. Organizations can benefit from understanding the core components influencing a data breach’s likelihood and impact. Zero Trust (ZT) strategies have proven to be more effective in addressing cyber risk resilience than the more traditional ‘implement-a-framework’ approach because the ZT focus on segmenting the environment by ringfencing sensitive data sets, limiting lateral movements and reducing the potential blast radius in case of a breach. However, Zero Trust has traditionally focused on technical measures and associated verifications. In isolation, technological measures won’t offer the optimal data-breach-cost-reduction. Company owners and boards that neglect their responsibilities are liable to hefty financial fines and individual liability. This paper describes how Zero Trust and Zero Trust as a Service (ZTaaS) decrease the economic impact of a data breach. It concludes with a conceptual approach that practitioners can immediately leverage to reduce the potential cost of a data breach and lower insurance premiums. Insurers can benefit from the simultaneous reduction of likelihood and impact.
Active preference-based Gaussian process regression for reward learning and optimization

Designing reward functions is a difficult task in AI and robotics. The complex task of directly specifying all the desirable behaviors a robot needs to optimize often proves challenging for humans. A popular solution is to learn reward functions using expert demonstrations. This approach, however, is fraught with many challenges. Some methods require heavily structured models, for example, reward functions that are linear in some predefined set of features, while others adopt less structured reward functions that may necessitate tremendous amounts of data. Moreover, it is difficult for humans to provide demonstrations on robots with high degrees of freedom, or even quantifying reward values for given trajectories. To address these challenges, we present a preference-based learning approach, where human feedback is in the form of comparisons between trajectories. We do not assume highly constrained structures on the reward function. Instead, we employ a Gaussian process to model the reward function and propose a mathematical formulation to actively fit the model using only human preferences. Our approach enables us to tackle both inflexibility and data-inefficiency problems within a preference-based learning framework. We further analyze our algorithm in comparison to several baselines on reward optimization, where the goal is to find the optimal robot trajectory in a data-efficient way instead of learning the reward function for every possible trajectory. Our results in three different simulation experiments and a user study show our approach can efficiently learn expressive reward functions for robotic tasks, and outperform the baselines in both reward learning and reward optimization.
The importance of emotional intelligence for students in departmental educational organizations

This article discusses the importance of emotional intelligence for students in departmental educational organizations. The essence of the concept being studied is revealed, and some research in the field of studying the phenomenon is reflected. The article presents the results of an empirical study aimed at studying the level of formation of emotional intelligence among students at the Vologda Institute of Law and Economics of the Federal Penitentiary Service of Russia and educational organizations of the Ministry of Internal Affairs of the Russian Federation, interpreted the results obtained and made appropriate conclusions. The acquired information makes it possible to conclude that many students at departmental institutes are characterized by an average and also a high indicator of the degree of formation of emotional intelligence, as well as its parts. We find it correct to introduce into the general educational course of departmental universities certain pedagogical conditions intended for the formation and development of the construct under study.
An Energy Management Strategy Based on DDPG With Improved Exploration for Battery/Supercapacitor Hybrid Electric Vehicle

Given that transportation contributes to 23% of global energy-related greenhouse gas emissions, the electrification of the transport sector is an inevitable trend. This paper presents a hybrid energy management method that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm. To enhance its exploration capability and balance exploration and exploitation, the Locality Sensitive Hashing (LSH) algorithm and Prioritized Experience Replay (PER) algorithm are introduced. DDPG, being a deterministic strategy, typically explores the environment by adding noise to the action space, which lacks effective exploration capability. Therefore, the LSH algorithm is employed for approximate counting of state variables in high-dimensional continuous state spaces. This encourages agents to explore less frequently occurring state variables, thereby improving the algorithm’s exploration ability. Additionally, the integration of the PER algorithm enables more efficient utilization of limited sample data during experience replay, leading to improved learning efficiency and stability. In addition, periodic updates are used to compensate for the additional computational consumption caused by counting. Simulation results on driving cycles from the typical road conditions demonstrate that the proposed algorithm has stronger exploration ability and better management performance comparing with EMSs based on DDPG, DQN and Q-Learning. The LSH-PER-DDPG based EMS achieves a significant 8.5% reduction in lithium battery power consumption, a 6.25% increase in supercapacitor recovery, and a substantial 15.7% reduction in instantaneous fluctuation of lithium battery output current.
Virtual Training: Empowering Information Technology Professionals with Enhanced Skills

In an era defined by technological advancements, virtual training stands out as a transformative educational method, diverging from traditional classroom settings. Its innovative use of technology provides engaging, cost-effective learning opportunities for remote students, professionals seeking to enhance their skills, and organizations aiming to modernize training initiatives. By seamlessly integrating multimedia, self-paced modules, and collaborative tools, virtual training equips learners with the knowledge and competencies required to excel in their respective fields. Overcoming geographical boundaries, it facilitates global access and supports continuous learning in an ever-changing world. The adaptability, scalability, and datadriven insights of virtual training make it an indispensable tool for individuals and organizations, nurturing skill development, knowledge retention, and proficiency enhancement. The adoption of virtual training has surged in recent years due to its costeffectiveness and flexibility, especially in the fast-paced realm of the information technology (IT) sector. IT professionals must continuously update their skills to keep up with rapidly evolving technologies, making virtual training a practical solution to address workforce education needs. This research aims to assess the effectiveness of virtual training among IT sector employees, focusing on IT professionals who have participated in virtual training programs. The study gathers data from a sample of 200 employees working in IT companies in Kolkata and utilizes the Pearson correlation methodology to evaluate various variables across different data series. The study's findings reveal that IT sector employees generally hold positive attitudes and perceptions about virtual training. Furthermore, the research concludes that the efficacy of virtual training positively impacts areas such as communication improvement, accelerated knowledge dissemination, promotion of collaborative teamwork, career progression, and professional development.