Reviewerly/exHarmony huggingface dataset:
Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism

In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields.
Underwater Image Deblurring Framework Using A Generative Adversarial Network

Blur effects commonly exist in the underwater images due to the camera shake, object motion and insufficient light in the practical underwater environment. They greatly affect the accuracy and efficiency of underwater investigations (an AUV-based vision exploration). To address this issue, we propose a solution using deep learning-based framework to translate blurry underwater images into clear ones. The network consists of two sub-networks, they incorporate to solve the notorious image translation problem by learning the blur kernel priors and the color priors. A specific large-scale underwater deblurring dataset is selected for training and evaluating our deep framework. Extensive experiments show our framework exhibits satisfying results.
An optimization method for motion blur image restoration and ringing suppression via texture mapping

Since the image sensor will produce blur problems in the process of collecting data of moving objects, the image needs to be restored. Ringing is one of the most common artifacts in deblurred images. This paper proposes a non-blind image deconvolution method based on texture mapping segmentation, named texture-Richardson-Lucy (TRL) algorithm, which suppresses ringing while deblurring the image. TRL is based on a novel ringing removal deconvolution algorithm, which adds a ringing detection term as regularization in the iterative process of the Richardson-Lucy algorithm. Taking into account the structural difference between the texture and the flat area, the image is segmented into several blocks and restored through adaptive iterative texture maps based on the pixel intensity and texture features of the image. In order to obtain a reasonable texture map, a Gaussian mixture model is used to fit the pixel intensity distribution, and use the expectation maximization algorithm and local binary mode to estimate. Experimental results and quantitative evaluations show that TRL can effectively reduce ringing artifacts while retaining details and achieving robustness to suppress ringing of different blur kernels. The processing time of a single 1 million pixel image in an 8-core CPU environment is about 3.5 s. And the PSNR and SSIM parameters are above 30 dB and 0.92, respectively. In conclusion, TRL is superior to the current popular algorithms.
Peculiarities of Dynamics of Hypergenic Mineral Transformation of Nickel Weathering Crusts of Ultramafic Rocks of the Kempirsay Group of Deposits in Western Kazakhstan

Nickel weathering ores are used to produce metallic nickel, stainless steels, and nickel sulfate, the main component of batteries. The global production of nickel from weathering ores is increasing and has surpassed production from sulfide magmatic deposits. The efficiency of the mining and processing of nickel ores from weathering rocks is determined by their mineralogical composition. The weathering crust profile of the Kempirsay ultramafite massif is divided into three zones—leached (kerolitized) serpentinites, nontronites, and final hydrolysis minerals (later referred to as “ochers”). The kerolitized zone consists of a mixture of Ni-bearing talc and saponites (later referred to as “kerolite”). During the geological mapping of the Donskoye, Buranovskoye, and Shelektinskoye deposits, the products of ultramafite hypergenic transformation into disintegrated and leached serpentinites, kerolites, nontronites, and ochers were selected and studied. For this purpose, 44 rock samples were studied via X-ray diffractometric and thermal analyses, supplemented with data from chemical, microscopic, and granulometric determinations. Based on the obtained numerical parameters of the crystalline structure of the weathering products, the thermochemical values were obtained. The hypergenic transformation of the initial minerals and their subsequent transformation were traced. The trace element distribution along the profile of the serpentinite weathering ores is related to the initial material composition of the ultramafites. The accumulation of nickel in industrial concentrations is associated with the nontronite–kerolite zone. X-ray diffractometric analysis can be used as a fast and reliable method for controlling the nickel content of ores and monitoring their mineralogical composition.
A systemic functional linguistics approach to implicit entity recognition in tweets

The identification of knowledge graph entity mentions in textual content has already attracted much attention. The major assumption of existing work is that entities are explicitly mentioned in text and would only need to be disambiguated and linked. However, this assumption does not necessarily hold for social content where a significant portion of information is implied. The focus of our work in this paper is to identify whether textual social content include implicit mentions of knowledge graph entities or not, hence forming a two-class classification problem. To this end, we adopt the systemic functional linguistic framework that allows for capturing meaning expressed through language. Based on this theoretical framework we systematically introduce two classes of features, namely syntagmatic and paradigmatic features, for implicit entity recognition. In our experiments, we show the utility of these features for the task, report on ablation studies, measure the impact of each feature subset on each other and also provide a detailed error analysis of our technique.
Towards a virtual water currency for industrial products using blockchain technology

Abstract Tracking unseen water in products (Embedded Virtual Water) has generated great interest in the scientific community. This water transfers between geographies via suppliers, manufacturers, distributors, retailers and customers in multiple phases. However, the Virtual Water Trading System lacks proper accounting standards, established protocols and processes in the context of product manufacturing. Therefore, there is a need to establish a technology platform to handle the complex virtual water international trade. Such a platform should uphold transparency and create ‘water consciousness’ and awareness among companies and consumers. The concept of a virtual water currency and blockchain technology platform together can manage these processes. Blockchain helps in setting up secure, verifiable, scalable and traceable systems. Blockchain manages the audit and contract management processes with ease. Virtual water currency is critical to advocate sustainability. The objective of this paper is to establish the key linkages between virtual water and usage of blockchain. A systematic literature survey was conducted on 16 journal repositories (153 journal papers) of IWA Publishing to establish virtual water linkages and five journal databases (IEEE Xplore, Sciencedirect, ACM Digital Library, Springer Link and Wiley Online Library covering 5026 journal papers) for blockchain and water management linkages. This study proposes to introduce virtual water currency and set up an International Virtual Water Trading System using blockchain. The proposed platform seamlessly integrates the quality, cost and sustainability of industrial products and their sub-components.
Content-Aware Unsupervised Deep Homography Estimation and Its Extensions

Homography estimation is a basic image alignment method in many applications. It is usually done by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real-world applications. To overcome these problems, in this work, we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art, including deep solutions and feature-based solutions.
Structuring knowledge-building in online design education

Design education has traditionally been deemed a face-to-face endeavor causing online learning to be disregarded as a viable teaching option. Nonetheless, the recent impact of COVID-19 pressured design schools to rapidly migrate online, impelling many educators to utilize this unfamiliar and largely dismissed methodology. The impending problems exposed with this sudden shift point to a significant gap in research. Accordingly, this study proposes a set of guidelines targeting design knowledge-building, based on an in-depth look at student experience during an online design course. Data were collected through a 63-item course efficiency survey (n = 59) and a series of semi-structured focus group interviews (n = 16) with the enrolled students. The following overarching themes emerged through iterative thematic analysis of the interview data: (1) flexibility and handling stress, (2) managing self-pacing issues (3) formal conversation platform, (4) content variety and access options. The themes were interpreted in relation to the survey findings and the broader research on learning. The proposed guidelines emphasize initially clear goals and objectives, pacing flexibility with progress guidance, content and communication variety, sense of presence and peer exposure, and individualized feedback. It is expected that the guidelines will be helpful in building, conducting, and evaluating future online design knowledge-building experiences.
The link between Fisher information and geometric discord

By considering an arbitrary two-qubit state, it is shown that the Fisher information is intrinsically linked to the geometric discord which allows a measure for quantum correlations beyond entanglement. The complex amplitude of oscillations of the probability density function is upper bounded by the geometric discord which subsequently results in the Fisher information being bounded by the geometric discord. This gives an experimental observable which can be used to quantify quantum correlations beyond entanglement. This observable can be used to witness quantum correlations in an interferometry experiment, and provide another avenue for quantum technologies to continue to develop.
Feasibility Analysis of the Application of Virtual Reality Technology in College English Culture Teaching

Although the traditional English teaching mode has changed qualitatively, it still has not broken through the two-dimensional limitation, which limits the students’ creative thinking to a certain extent. The application of virtual reality technology in teaching is a qualitative leap in the development of educational informatisation. Based on the data collected from questionnaires and in-depth interviews, this paper studies the feasibility of applying virtual reality immersion teaching in primary and secondary school English teaching. On this basis, this paper attempts to combine virtual reality technology with immersion teaching, and make use of its diversity, flexibility, interactivity and other characteristics to prospect the realisation of virtual reality in English learning, and strive to explore a virtual reality immersion English teaching mode suitable for primary and secondary school children, so as to make up for the shortcomings of traditional language education and optimise the learning effect, It provides a feasible reference for the application of virtual reality technology in English teaching in the future.
How agile teams make Objectives and Key Results (OKRs) work

Today, many companies allow their employees to work from anywhere, which has changed how employees coordinate their work and align toward the same goals. Objectives and Key Results (OKRs) is a goal-setting framework applied in such distributed settings. This research aimed to investigate how OKRs are used in large-scale agile contexts. We interviewed team members and analyzed documents, including a survey. Our study's results provide both enabling and limiting situations that make team members' utilization of the framework either easier or more difficult. We found that OKRs aided knowledge sharing and improved transparency between teams. We present four strategies used for overcoming challenges and maximizing the benefits of using a goal-setting framework. An important takeaway is that companies that employ OKRs must support their employees, especially in defining key outcomes that align and encourage teams toward a common goal.
A Behavioral Assessment Model for Emotional Persuasion Driven by Agent-Based Decision-Making

• An agent’s emotion, time belief and opponent’s concession behavior are assessed. • An assessment behavior model is built around emotion and learning ability of agent. • An improved offer update algorithm is built through reinforcement learning. • The proposed assessment behavior model achieves higher negotiation performance. Emotional persuasion driven by agent-based decision-making has shown greater prospects for negotiation between business partners. However, the interactive assessment of emotional persuasion, one of the essential preconditions for formulating an appropriate proposal, is still in its infancy. To that end, this research proposes a modeling framework that integrates three types of assessment behavior: emotion, time belief, and the evaluation of the opponent’s concession behavior. Based on the Weber-Fechner’s law, we designed a method to calculate an agent’s emotional assessment of the opponent’s proposal. Next, we construct a time belief function which can dynamically assess the pace of negotiation. Then, a Q-Learning algorithm is used to integrate those two assessments with evaluating another agent’s concession behavior to update the focal agent’s proposal. Moreover, a series of numerical experiments are conducted. The results combined with sensitivity and comparative analyses show that the proposed model framework is superior to the existing agent-based persuasion methods in terms of negotiation efficiency and fairness.
Microscopic fringe projection profilometry systems in Scheimpflug condition and performance comparison

Abstract The high-frequency fringe images provided by magnifying lenses with low distortion render the microscopic fringe projection profilometry (MFPP) with better measurement accuracy. In order to extend the depth of field limited in the microscopic environment, the Scheimpflug principle is commonly used in both single-camera-based and multi-camera-based MFPP systems by increasing the mutual overlap range of different views in the object space. In this paper, to compare the performance of single-camera-based and dual-camera-based MFPP systems, we set up MFPP systems and performed 3D measurements of plates, standard balls, and some specifically designed samples. By analyzing the various factors affecting measurement accuracy and integrity, we verified and concluded that the dual-camera-based system wins in measurement accuracy while the single-camera-based system has better integrity, which may provide a reference for the system design in the implementation of industrial applications.
Application of Augmented Reality for Aviation Equipment Inspection and Maintenance Training

In the procedure of aviation equipment maintenance, a healthy status and prior knowledge are essential to every person. Making the newcomers get familiar with their tasks rapidly and reducing human error are important issues. Augmented reality (AR) integrated with the popular artificial intelligence (AI) technology can provide smart system inspections and train maintenance professionals on how to perform important maintenance procedures effectively and accurately. Utilization of the AR not only replaces classic manual training but also provides high mobility pre-training opportunities. In other words, AR is capable of taking equipment maintenance to the next level. In this paper, an application for aviation equipment inspection is developed. By using AR and AI, traditional standard operation procedures (SOP) can be visualized and standardized. Therefore, people who are not familiar with the maintenance procedure can still finish the standard inspections. The developed software gives a great contribution to human resources for maintenance training. Demonstrations including turbofan and landing gear are provided by using HoloLens 2 to illustrate the application novelty. Finally, pros and cons regarding maintenance using AR are also discussed.
Evaluating Membership Inference Through Adversarial Robustness

Abstract The usage of deep learning is being escalated in many applications. Due to its outstanding performance, it is being used in a variety of security and privacy-sensitive areas in addition to conventional applications. One of the key aspects of deep learning efficacy is to have abundant data. This trait leads to the usage of data which can be highly sensitive and private, which in turn causes wariness with regard to deep learning in the general public. Membership inference attacks are considered lethal as they can be used to figure out whether a piece of data belongs to the training dataset or not. This can be problematic with regard to leakage of training data information and its characteristics. To highlight the significance of these types of attacks, we propose an enhanced methodology for membership inference attacks based on adversarial robustness, by adjusting the directions of adversarial perturbations through label smoothing under a white-box setting. We evaluate our proposed method on three datasets: Fashion-MNIST, CIFAR-10 and CIFAR-100. Our experimental results reveal that the performance of our method surpasses that of the existing adversarial robustness-based method when attacking normally trained models. Additionally, through comparing our technique with the state-of-the-art metric-based membership inference methods, our proposed method also shows better performance when attacking adversarially trained models. The code for reproducing the results of this work is available at https://github.com/plll4zzx/Evaluating-Membership-Inference-Through-Adversarial-Robustness.
Privacy-Preserving Active Learning on the Internet of 5G Connected Artificial Intelligence of Things

With the advancement in 5G/6G networks, secure communication can help to develop data security applications. A layered network in the Artificial Intelligence of Things helps to assist data monitoring for the operation of critical components of smart cities. Unmanned aerial vehicles (UAVs) are often connected to the mobile network and are vulnerable to identify thefts and attacks. Safe UAV communication can help a number of data-driven applications to perform critical tasks. We introduce a deep reinforcement learning technique for sensitizing private information for a specific vehicle link across vehicular ad hoc networks. We use entropy-based active learning to classify the sensitive sensors data. The entropy-based active-learning-based method significantly increases training instances for the deep attention-based model. The proposed method helps introduce the new features based on the 5G/6G network while balancing safety, privacy, security, and confidentiality through a sanitization procedure.
IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System

Event-based Social Networks (EBSN), combining online networks with offline users, provide versatile event recommendations for offline users through complex social networks. However, there are some issues that need to be solved in EBSN: (1) The online static data could not satisfy the online dynamic recommendation demand; (2) the implicit behavior information tends to be ignored, reducing the accuracy of the recommendation algorithm; and (3) the online recommendation description is inconsistent with the offline activity. To address these issues, an Incentive Improved DQN (IIDQN) based on Deep Q-Learning Networks (DQN) is proposed. More specifically, we introduce the agents to interact with the environment through online dynamic data. Furthermore, we consider two types of implicit behavior information: the length of the user’s browsing time and the user’s implicit behavior factors. As for the problem of inconsistency, based on blockchain technology, a new activities event approach on EBSN is proposed, where all activities are recorded on the chain. Finally, the simulation results indicate that the IIDQN algorithm greatly outperforms in mean rewards and recommendation performance than before DQN.