Reviewerly/exHarmony huggingface dataset:
Optical multiqubit gate operations on an excitation-blockaded atomic quantum register

We consider a multiqubit system of atoms or ions with two computational ground states and an interacting excited state in the so-called blockade regime, such that only one qubit can be excited at any one time. Examples of such systems are rare-earth-ion-doped crystals and neutral atoms trapped in tweezer arrays. We present a simple laser excitation protocol that yields a complex phase factor on any desired multiqubit product state, and which can be used to implement multiqubit gates such as the $n$-bit Toffoli gates. The operation is performed using only two pulses, where each pulse simultaneously address all qubits involved in the operation. By relying on the well-established complex hyperbolic secant pulse shape, our scheme is also robust and permits complete transfers to and from the excited states despite variations in Rabi frequencies and transition frequencies. A detailed analysis of the multiqubit gate performance is provided.
Cross-project clone consistent-defect prediction via transfer-learning method

Code clones are comparable code snippets that are introduced into software by developers in order to increase software development productivity. A change to code clone may result in a consistent-defect if the developers forget to verify the consistency of the code after the change. To reduce such change-related maintenance costs, researchers have proposed a number of methods for predicting clone consistency in advance. Unfortunately, the effectiveness of these cross-project models is unsatisfactory, and performing such predictions with insufficient data remains a challenge. Meanwhile, cross-project defect prediction via transfer learning method is prevalent in the software engineering community. Consequently, we first construct an empirical study to explore whether transfer-learning techniques could well be utilized for clone cross-project consistent-defect prediction in the initial stages of software development. In this paper, we employ transfer-learning techniques to predict clone consistency at both the time of clone creating and clone changing in order to avoid clone consistent-defects and maintenance. We conduct an experiment on open-source projects to evaluate the effectiveness of various transfer-learning methods. Our investigation demonstrates that transfer-learning techniques have a beneficial impact on predicting cross-project clone consistent-defect, and that the size of the dataset also has a positive effect on prediction. In order to promote software safety and security, we recommend that developers leverage transfer-learning to enhance the capability for clone cross-project consistent-defect prediction early in the software development phase.
Motion Planning of UAV for Port Inspection Based on Extended RRT* Algorithm

A suitable trajectory in a port inspection mission is important for unmanned aerial vehicles (UAVs). Motion planning can help UAVs quickly generate an optimal trajectory that meets the constraints. The motion planning of UAVs is achieved in this paper as follows: firstly, a collision detection (CD) function is applied that evaluates whether the bias_RRT* (rapidly exploring random tree) algorithm needs to be called. Secondly, an isosceles triangle optimization function optimizes the path. Next, a trajectory is generated based on the minimum snap trajectory method. Lastly, the bias_RRT* algorithm and the improved bias_RRT* algorithm are used in the two experimental scenes for path planning comparison, and trajectory planning is carried out. The results show that, in the improved method, the path length and calculation time are shortened, and the trajectory cost and trajectory deviation are also significantly reduced. Overall, it appears that a camera-equipped UAV under the proposed approach can accomplish monitoring tasks more effectively and safety in port environment.
Special Issue: Data-Driven Discovery in Geosciences: Opportunities and Challenges

With the rapid expansion in big data and artificial intelligence (AI), Earth sciences are undergoing unprecedented advances in data processing and interpretation techniques, as well as in facilitating data-driven discoveries of complex Earth systems. This special collection explores scientific research related to data-driven discoveries in geosciences and provides a timely presentation of progress in developments and/or applications of AI and big data approaches to multiple aspects of geosciences. These include geohazards monitoring, mineral resource exploration, and environmental assessments. We hope this collection will inspire researchers and will transform the work undertaken in the field of data-driven Earth science. While many challenges remain, including the formidable tasks of transforming the deluge of geoscience data into useable information and furthering knowledge via cutting-edge AI techniques, we envision that data-driven discovery will revolutionize conventional methods of observation, analysis, modeling, and prediction in geosciences, and will further advance scientific understanding of our complex Earth system.
Exploration Targeting in the Shadan Porphyry Gold–Copper Deposit, Lut Block, Iran: Analysis of Spatial Distribution of Sheeted Veins and Lithogeochemical Data

The Lut Block is a potential porphyry-style mineralized region in Iran including the well-explored Shadan porphyry gold-copper deposit, which has an extensive zone of gold- and copper-bearing stockwork-like sheeted veins. The structural setting of this deposit is a key to understanding the genesis of the mineralization. Our field studies show that the mineralization occurs as steeply dipping ore bodies along NW–SE transpressional faults. The zones with a high frequency of veins and volume of veins have a NW–SE trend, which is closely related to transpressional faults. We propose that such a compressional regime inhibits focused ore-forming fluid flow to higher levels in the crust. After a local change in stress field, fluid depressurization and channeling along transpressional faults generated the sheeted veins. This indicates an important role of these transpressional faults in focusing and controlling mineralization within this porphyry deposit, which has important implications for the exploration of porphyry deposits on a regional scale. The spatial distribution of sheeted veins is used to examine gold anomalies from lithogeochemical data extracted by fractal models. The results show that a combination of high vein density and high vein volume areas with gold anomalies could result in identifying areas with greater potential at the deposit to regional scale.
Drivers of, and Barriers to, the Adoption of Mixed Reality in the Construction Industry of Developing Countries

Mixed Reality (MR) that combines elements of both augmented reality (AR) and virtual reality (VR) has great potential for use in the construction industry. However, its usage in construction projects in developing countries has not been widely researched. This study aims to examine the major drivers of, and barriers to, the adoption of MR technologies (MRTs) in the construction sector of developing countries. A mixed methodology that included both qualitative and quantitative data analysis was used. The literature review revealed 37 barriers to, and 41 drivers of, MR adoption. A questionnaire was then distributed to 220 randomly selected respondents from the pertinent construction industry, representing all major stakeholders. The relative importance index (RII) was used to rank the barriers and drivers in terms of significance. The results showed that the primary barriers to MR adoption are the high cost of initial investment, public perception of the technology being immature, limited demand, and difficulty accessing relevant experts’ knowledge. The key drivers of MR adoption include improved project knowledge, reduced overall project costs, low-cost and realistic training scenarios, reduced damage and development costs, and enhanced user experience. These findings provide insights into the major barriers and drivers of MR in the construction sector of developing countries and will help pertinent companies to focus their research and development (R&amp;D) efforts on overcoming these barriers and promote their adoption to move towards the much sought-after construction automation and digitalization.
Sentiment Analysis of Comment Texts on Online Courses Based on Hierarchical Attention Mechanism

With information technology pushing the development of intelligent teaching environments, the online teaching platform emerges timely around the globe, and how to accurately evaluate the effect of the “any-time and anywhere” teacher–student interaction and learning has become one of the hotspots of today’s education research. Bullet chatting in online courses is one of the most important ways of interaction between teachers and students. The feedback from the students can help teachers improve their teaching methods, adjust teaching content, and schedule in time so as to improve the quality of their teaching. How to automatically identify the sentiment polarity in the comment text through deep machine learning has also become a key issue to be automatically processed in online course teaching. The traditional single-layer attention mechanism only enhances certain sentimentally intense words, so we proposed a sentiment analysis method based on a hierarchical attention mechanism that we called HAN. Firstly, we use CNN and LSTM to extract local and global information, gate mechanisms are used for extracting sentiment words, and the hierarchical attention mechanism is then used to weigh the different sentiment features, with the original information added to the attention mechanism concentration to prevent the loss of information. Experiments are conducted on China Universities MOOC and Tencent Classroom comment data sets; both accuracy and F1 are improved compared to the baseline, and the validity of the model is verified.
Graph Autoencoder with Preserving Node Attribute Similarity

The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.
Empirical Study of Overfitting in Deep Learning for Predicting Breast Cancer Metastasis

Overfitting may affect the accuracy of predicting future data because of weakened generalization. In this research, we used an electronic health records (EHR) dataset concerning breast cancer metastasis to study the overfitting of deep feedforward neural networks (FNNs) prediction models. We studied how each hyperparameter and some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied were activate function, weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch size, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs.
A Study on ML-Based Software Defect Detection for Security Traceability in Smart Healthcare Applications

Software Defect Prediction (SDP) is an integral aspect of the Software Development Life-Cycle (SDLC). As the prevalence of software systems increases and becomes more integrated into our daily lives, so the complexity of these systems increases the risks of widespread defects. With reliance on these systems increasing, the ability to accurately identify a defective model using Machine Learning (ML) has been overlooked and less addressed. Thus, this article contributes an investigation of various ML techniques for SDP. An investigation, comparative analysis and recommendation of appropriate Feature Extraction (FE) techniques, Principal Component Analysis (PCA), Partial Least Squares Regression (PLS), Feature Selection (FS) techniques, Fisher score, Recursive Feature Elimination (RFE), and Elastic Net are presented. Validation of the following techniques, both separately and in combination with ML algorithms, is performed: Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree (DT), and ensemble learning methods Bootstrap Aggregation (Bagging), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Random Forest(RF), and Generalized Stacking (Stacking). Extensive experimental setup was built and the results of the experiments revealed that FE and FS can both positively and negatively affect performance over the base model or Baseline. PLS, both separately and in combination with FS techniques, provides impressive, and the most consistent, improvements, while PCA, in combination with Elastic-Net, shows acceptable improvement.
Telepresence robots in libraries: applications and challenges

Purpose The main purpose of this paper is to explore telepresence robots are being used in libraries to facilitate library services and also to explain the future trend in the application of robots in libraries. Design/methodology/approach Through a review of the literature, this paper analyzes various library websites and consults literature relating to the use of telepresence robots in libraries; the current application of robots in libraries has been highlighted along with case studies of libraries currently adopting telepresence robots. Findings The uses of telepresence technology in libraries help to enhance library services, reach new users and provide a more inclusive and accessible library experience. Telepresence robots enhance the quality and accessibility of library services, expand library outreach and provide new opportunities for virtual engagement and programming. The application of telepresence robots in libraries can offer many benefits, but there are also several challenges that libraries must address to ensure successful implementation. Originality/value This paper highlights how the application of telepresence robots in libraries improves service productivity in libraries and creates a more engaging environment for the user group. The benefits and challenges of using robots in the library and the future trend in the application of telepresence robots in libraries are also discussed.
SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints

Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due to the high cost of performing segmentation labeling, many synthetic datasets have been proposed. However, most of them miss the multi-sensor nature of the data, and do not capture the significant changes introduced by the variation of daytime and weather conditions. To fill these gaps, we introduce SELMA, a novel synthetic dataset for semantic segmentation that contains more than 30K unique waypoints acquired from 24 different sensors including RGB, depth, semantic cameras and LiDARs, in 27 different weather and daytime conditions, for a total of more than 20M samples. SELMA is based on CARLA, an open-source simulator for generating synthetic data in autonomous driving scenarios, that we modified to increase the variability and the diversity in the scenes and class sets, and to align it with other benchmark datasets. As shown by the experimental evaluation, SELMA allows the efficient training of standard and multi-modal deep learning architectures, and achieves remarkable results on real-world data. SELMA is free and publicly available, thus supporting open science and research.
Co-designing management recommendations with stakeholders: A case study about northern pike (Esox lucius) in the southern Baltic Sea

Involving all interest groups, often called stakeholders, is increasingly recognized as a key part of sustainable natural resource management, promoting acceptability and compliance with management decisions. However, there is the difficulty of how to build trust, integrate diverse knowledge bases and account for varying objectives of different stakeholders. We present insights from a fisheries stakeholder process that used structured decision making and participatory modelling in a workshop series over the course of three years to develop management advice for a northern pike (Esox lucius) meta-population inhibiting brackish lagoons of the southern Baltic Sea in Germany. The stock is co-exploited by commercial and recreational fisheries who are in conflict over space and fish and vary in values. We formed and professionally facilitated a stakeholder group composed of 15 representatives of all main interest groups. The workshops generated a shared vision and seven mutually agreed management objectives that combined aspects of biological, ecological, economic, and social sustainability. To meet objectives, the group identified more than 40 management options, revolving around stock monitoring, control and surveillance, knowledge transfer, harvest regulations, habitat management, predator control and pike stocking. Participatory model building provided forecasts on the impacts of selected management options, allowing to transparently examine trade-offs based on quantitative criteria matched to objectives. The process overall led to stakeholder consensus about multiple management tools that the group recommended to government officials for future management reform. Challenges remain as to the communication of irreducible ecological and social uncertainties associated with quantitative predictions. In this context, cognitive maps about cause-and-effect relationships affecting pike were found useful to integrate knowledge and remind about uncertainties. Our case study shows that stakeholder-inclusive approaches using structured decision making and participatory modelling with professional facilitation are time- and planning-intensive, but can promote common system understanding and development of mutually agreed management advice also in heterogeneous stakeholder groups that are often in severe conflict with each other.
Exploring the Effects of Blockchain Scalability Limitations on Performance and User Behavior in Blockchain-Based Shared Manufacturing Systems: An Experimental Approach

This study investigates the effects of blockchain technology scalability limitations on the performance of Blockchain-Based Shared Manufacturing (BBSM), an innovative smart-manufacturing paradigm aimed at enhancing the utilization of global manufacturing resources via peer-to-peer (P2P) collaboration of self-organized manufacturing assets. Despite the prevalence of research highlighting blockchain technology’s scalability limitations as the main barrier for adoption, few studies have explored their effects on the operation of blockchain-based systems. The primary goal of the presented research work is to explore the implications of blockchain technology scalability limitations on the BBSM system’s performance and user behavior. To obtain realistic behavior, an experiment is conducted using an online game played by human participants. Analysis of the players’ strategy is used for implementation of a multi-agent simulation model, which is then employed to assess the influence of varying blockchain network configurations on the BBSM concept’s performance. Preliminary experimental findings reveal that a congested blockchain network leads to increased transaction costs and reduced service prices, consequently devaluing the manufacturing role in the BBSM system and causing underutilization of existing maximum production capacities. Moreover, allocating funds to financial activities rather than manufacturing activities yields superior outcomes for system users. Simulation results indicate that the BBSM system’s response to alterations in blockchain network throughput is contingent upon the production function. The findings of this study reveal that the scalability limitations of blockchain technology impair the performance of the BBSM system and affect user behavior in the system, underscoring the necessity for future research to concentrate on incorporating scalable solutions within blockchain-based manufacturing systems.
Chinese ASR and NER Improvement Based on Whisper Fine-Tuning

Based on 680k hours of weakly supervised multilingual and multi-task speech transcription/translation data, Whisper [1] has developed a robust system for both Automated Speech Recognition (ASR) and Speech Translation (ST). Whisper provides a simple model architecture based on Mel spectrum + two-layer convolution + Seq2seq Transformer architecture, which is easy to fine-tune on conditional generation tasks. This paper analyzes how to fine-tune Chinese ASR [2] and NER tasks based on Whisper, including (1) how to design different prompts for different generative tasks; (2) how to train ASR and NER tasks at the same time; (3) whether the performance can be further improved by using weak supervision for data enhancement. Experiments based on AISHELL [3] and AISHELL-NER [4] data, and multi-task fine-tuning based on Whisper can effectively improve the performance of ASR and NER.