Reviewerly/exHarmony huggingface dataset:
A Novel Hybrid Firefly Algorithm with Double-Level Learning Strategy

The firefly algorithm (FA) is a swarm intelligence algorithm capable of solving global optimization problems exactly; it has been used to solve many practical problems. However, traditional firefly algorithms solve complex optimization problems with a simple update method, which leads to premature stagnation due to the limitation of firefly diversity. To overcome these drawbacks, a novel hybrid firefly algorithm (HFA-DLL) with a double-level learning strategy is proposed. In HFA-DLL, a double-level learning strategy is proposed to avoid premature convergence and enhance the algorithm’s global search capability. At the same time, a competitive elimination mechanism is introduced to increase the accuracy of solving complex optimization problems and improve the convergence rate of the algorithm. Moreover, a stochastic disturbance strategy is designed to help the best solution jump out of the local optimum and minimize the time cost in the wrong direction. To understand the advantages and disadvantages of HFA-DLL, experiments were conducted on the CEC 2017 benchmark suite. Experimental results show that HFA-DLL outperforms other state-of-art algorithms in terms of convergence rate and exploration efficiency.
The state of Augmented Reality in aerospace navigation and engineering

The concept of Augmented Reality (AR) has existed in the field of aerospace for several decades in the form of Head-Up Display (HUD) or Head-Worn Display (HWD). These displays enhance Human-Machine Interfaces and Interactions (HMI2) and allow pilots to visualize the minimum required flight information while seeing the physical environment through a semi-transparent visor. Numerous research studies are still being conducted to improve pilot safety during challenging situations, especially during low visibility conditions and landing scenarios. Besides flight navigation, aerospace engineers are exploring many modern cloud-based AR systems to be used as remote and/or AI-powered assist tools for field operators, such as maintenance technicians, manufacturing operators, and Air Traffic Control Officers (ATCO). Thanks to the rapid advancement in computer vision and deep neural network architectures, modern AR technologies can also scan or reconstruct the 3D environment with high precision in real time. This feature typically utilizes the depth cameras onboard or independent from the AR devices, helping engineers rapidly identify problems during an inspection and implement the appropriate solutions. Some studies also suggest 3D printing of reconstructed models for additive manufacturing. This chapter covers several aspects and potentials of AR technology in the aerospace sector, including those already adopted by the companies and those currently under research.
Increasing E-learning Modalities through Creative use of Technology in the Post-Pandemic Era

The post-Pandemic Era leaves us with the question of whether to pursue the move toward digital education or step back into face-to-face education. The reasons for the debate can vary from the lack of infrastructure to stringent national qualification agencies. Regardless, the pandemic has taught us to be adaptable. As such, progress must be made in both formats, digital and conventional education. In moving e-learning forward while coping with diverse needs of global education across geographical, age, ethnicity, and socioeconomic boundaries, the inclusion of read, watch, and listen (RWL) modalities becomes essential. Successful deliveries of different modalities lie in creativity and creative use of technology that is driven by the achievement of targeted learning outcomes. This chapter attempts to discuss the experience of creating RWL modalities by embracing the universal design, and other creative use of technology to develop twenty-first century learners.
Optimized Feature Extraction for Sample Efficient Deep Reinforcement Learning

In deep reinforcement learning, agent exploration still has certain limitations, while low efficiency exploration further leads to the problem of low sample efficiency. In order to solve the exploration dilemma caused by white noise interference and the separation derailment problem in the environment, we present an innovative approach by introducing an intricately honed feature extraction module to harness the predictive errors, generate intrinsic rewards, and use an ancillary agent training paradigm that effectively solves the above problems and significantly enhances the agent’s capacity for comprehensive exploration within environments characterized by sparse reward distribution. The efficacy of the optimized feature extraction module is substantiated through comparative experiments conducted within the arduous exploration problem scenarios often employed in reinforcement learning investigations. Furthermore, a comprehensive performance analysis of our method is executed within the esteemed Atari 2600 experimental setting, yielding noteworthy advancements in performance and showcasing the attainment of superior outcomes in six selected experimental environments.
Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.
MAN: Memory-augmented Attentive Networks for Deep Learning-based Knowledge Tracing

Knowledge Tracing (KT) is the task of modeling a learner’s knowledge state to predict future performance in e-learning systems based on past performance. Deep learning-based methods, such as recurrent neural networks, memory-augmented neural networks, and attention-based neural networks, have recently been used in KT. Such methods have demonstrated excellent performance in capturing the latent dependencies of a learner’s knowledge state on recent exercises. However, these methods have limitations when it comes to dealing with the so-called Skill Switching Phenomenon (SSP), i.e., when learners respond to exercises in an e-learning system, the latent skills in the exercises typically switch irregularly. SSP will deteriorate the performance of deep learning-based approaches for simulating the learner’s knowledge state during skill switching, particularly when the association between the switching skills and the previously learned skills is weak. To address this problem, we propose the Memory-augmented Attentive Network (MAN), which combines the advantages of memory-augmented neural networks and attention-based neural networks. Specifically, in MAN, memory-augmented neural networks are used to model learners’ longer term memory knowledge, while attention-based neural networks are used to model learners’ recent term knowledge. In addition, we design a context-aware attention mechanism that automatically weighs the tradeoff between these two types of knowledge. With extensive experiments on several e-learning datasets, we show that MAN effectively improve predictive accuracies of existing state-of-the-art DLKT methods.
Antipodal two-weight rank metric codes

We consider the class of linear antipodal two-weight rank metric codes and discuss their properties and characterization in terms of t-spreads. It is shown that the dimension of such codes is two and the minimum rank distance is at least half of the length. We construct antipodal two-weight rank metric codes from certain MRD codes. A complete classification of such codes is obtained, when the minimum rank distance is equal to half of the length. As a consequence of our construction of two-weight rank metric codes, we get some explicit two-weight Hamming metric codes.
Analysis of Object Detection Datasets for Machine Learning with Small and Tiny Objects

Deep Learning models are trained to detect humans, cars, and other large objects which are centered in the images.The same models struggle with detecting small and tiny objects because of architecture design decisions that reduce the entropy of small and tiny objects during training.These small and tiny objects are essential for damage identification and maintenance including inspection and documentation of aeroplanes, constructions, offshore structures, and forests.Our work defines the terms tiny and small in context of deep learning models to evaluate possible approaches to resolve the issue of low accuracy in detecting these objects.We analyse the currently applied common datasets Common Objects in Context, ImageNet and Tiny Object Detection Challenge dataset.In addition we compare these datasets and present the differences in terms of object instance size.The COCO dataset, ImageNet dataset and TinyObjects dataset are analysed regarding size categorization and relative object size.The results show the large differences between the size ratios of the three chosen datasets, with ImageNet having by far the largest object instances, COCO being in the middle and TinyObjects having the smallest objects as its name would indicate.Since the objects themselves are larger in terms of total pixel width and height, they therefore make up a bigger percentage on the superordinate picture.Looking at the size categories of the COCO dataset and our extension of the tiny and very small category, the results confirm the size hierarchy of the datasets.With ImageNet having most of its objects in the large category, COCO respectively in the medium category and TinyObjects in the very small category.By taking these results into account, the
The business strategy of the utility token adoption by investors in a developing country

Utility tokens, which are cryptocurrencies on a smart contract blockchain that serve a specific purpose within the ecosystem of a crypto project (Shirole et al., 2020), have exploded in popularity among Thai investors over the past few years due to the fact that their value tends to increase much more rapidly than that of traditional assets. This study attempts to analyse the variables influencing Thai investors’ adoption of utility tokens. A binary logistic regression was conducted to find factors of statistical significance. The results indicate that gender, level of education, income, savings, the types of financial instruments an investor owns, and the investor’s use of social media platforms are significant determinants of utility token adoption. The article argues that when planning marketing efforts, organisations issuing utility tokens should consider their clients’ gender, level of education, income, and savings, as these factors have a substantial influence on utility token adoption. In addition, in order to increase the adoption of utility tokens by experienced investors, brokers or financial advisors must evaluate the customer’s current financial holdings in order to develop successful financial plans and provide appropriate guidance to customers.
Image-based polarization detection and material recognition based on liquid crystals

We demonstrated an image-based polarization detection system comprising 4 tunable liquid crystal wave plate and 4 polarizers for the measurement of full Stokes parameters and material recognition with a single shot. In this paper, based on polarization property, the metal plate and the glass substrate could be recognized. The one of the applications is to provide a practical way in image-based polarization detection in Advanced Driver Assistance Systems for material recognition which could help in driving safety.
Super-additive interaction of homo- and heterosynaptic plasticity in a hot electron transfer optosynapse for visual sensing memory and logic operations

A ZTO/Au NPs heterostructure optosynaptic device proving the super-additivity of homo- and heterosynaptic plasticity is offered for visual and logical functions based on the light-bias coupling effect and hot electron transfer.
Estimation of a clustering model for non Gaussian functional data

AbstractModel-based clustering analysis of functional data often has normality assumption. This article considers clustering non Gaussian functional data. We propose a novel non Gaussian functional mixed-effects model without the prior information and clustering number. We use transformation functions to accommodate non Gaussian functional data. Smoothing spline ANOVA and cubic B-spline approximate unknown fixed effects and random effects, respectively. A penalized likelihood is used to estimate unknown parameters, and the consistency and asymptotic normality is provided after that. We take simulations for different measurement error distribution assumptions and adopt the air quality of Italian city data. Both simulation and actual data analysis show that the proposed method performs well and has a better clustering effect.KEYWORDS: Clustering analysisnon Gaussian functional datatransformation functionsmoothing spline ANOVApenalized likelihood Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was supported by the National Natural Science Foundation of China (11971171,11831008,12171310), and the Basic Research Project of Shanghai Science and Technology Commission (22JC1400800).
The role of mobile money adoption in moderating the influence of access to finance in firm performance

Purpose Africa is becoming the fastest-growing continent despite significant challenges to accessing finance and the use of technology. This paper aims to examine the direct effect of mobile money adoption on firm performance and its moderation effect by examining how it moderates the effect of access to finance on firm performance. Design/methodology/approach Quantitative data were obtained from the World Bank Enterprise Survey for Cameroon, Ivory Coast and Zimbabwe. A series of hierarchical regression analyses were done to test the hypotheses. Findings The main findings show a negative significant relationship between mobile money adoption and firm performance, while access to finance had a positive relationship. The moderation effect though positive was not significant. Research examining the effect of mobile money adoption in Africa on firm performance is limited, and existing studies have focused on the determinants of mobile money usage. By examining the direct and contingency effect on other determinants of firm performance, this research makes both theoretical and practical contributions. Theoretically, this research shows that not all strategic resources are valuable in improving firm performance. Practically, this research provides insights into how technology could be embedded into business processes for firms to benefit from such technology. Originality/value This research has complemented by the extant literature by assessing the role of mobile money adoption in moderating the influence of access to finance on firm performance.
Politics of Deliberate Inaction: The disconnect between platform justifications and user imaginaries on content moderation in a ‘free speech’ online forum

This article analyses the ‘free speech’ online forum Flashback, which adheres to a strict non-interference policy when it comes to user-generated content, but beyond this also forbids users from deleting their own content or accounts. Through a qualitative content analysis, this article sought to understand the relationship between the platform and its users with respect to this unconventional approach to moderation and content removal. This article discusses both the position(s) taken by Flashback as it pertains to its policy of minimal moderation, and the expectations as expressed by users navigating Flashbacks rules and their practical implementations. The article shows a discrepancy between how Flashback (incoherently) justifies minimal moderation and how users had imagined the platform operating. The article also discusses how Flashback maintains these policies through its community’s active encouragement via supportive posting and silencing of non-conformers, and the consequences that Flashback’s inaction has in terms of residual hate.
Mental pictures, structural constraints: Kenneth N. Waltz’s approach to theory

The aim of this article is to develop Kenneth N. Waltz’s conceptualization of system structures based on the distribution of capabilities to those described by two traits at system-level: the distribution of capabilities across states and states’ geographic positions with respect to each other, that is, the contiguity configuration. The development generates taxonomies of structures evaluated as mental pictures that guide, organize, and channel thoughts by identifying the ways system structures constrain international interactions. Mental pictures are argued to derive from a multiplicity of interrelated neurophysiological processes of the brain according to functionalism which is a monist doctrine of the philosophy of mind. Mental pictures establish structural constraints as products of an algorithm based on realism and system theory depicting a neo-Kantian view of how our minds impose order on sensory data.
Social Engineering Penetration Testing in Higher Education Institutions

Social engineering penetration testing is a complex but necessary tool to test the security of information systems. Such testing requires balancing the organization’s benefit and the comfort of an information system user. Penetration testing poses complex ethical concerns affecting people who do not expect it. At the same time, penetration testing is effective only when it mimics the real situation as much as possible, i. e. it is unexpected. The article’s authors describe the methodology of social engineering penetration testing of the educational institution information system; substantiate the design of the experiment, which allows for balancing the ethical precautions and the effectiveness of testing. The authors formulate a set of markers that they use to reduce the negative impact of the attack on users of the information system and to help users to identify the true nature of the attack. The experiment conducted by the authors shows the advance of a phishing attack aimed at a large number of system users and its effectiveness. The authors also reveal the challenges such an attack poses to the information system staff, who have to respond to such influence effectively and on time. The experiment shows that half of the responses were received in the first 40 min after mailing. Concluding the research authors analyze the suggested design of social engineering penetration testing experiment, ways to respond to real attacks of this kind, as well as to raise respondents’ awareness. The directions for possible future research are outlined. The value of this research is in the object—students of a higher educational institution who constantly work with information. The neglect of personal information indicates the need to introduce information hygiene courses from the very first courses.
Exploration of the Application of Project Teaching Method in the Course of Urban Public Transport Planning and Operation Management

On July 30, 2020, the General Office of the Ministry of Education and the General Office of the Ministry of Industry and Information Technology issued a notice on the “Guide for the Construction of Modern Industrial Colleges (for Trial Implementation)”, proposing that cultivating high-quality applied, composite and innovative talents who adapt to and lead the development of modern industries is an inevitable requirement for higher education to support high-quality economic development, and an important initiative to promote the development of classification and characteristics of universities. As a local, application-oriented university, Nanning University should actively explore the development path of modern industrial colleges in the process of training students and conducting course teaching, so that students can quickly adapt to their jobs after graduation, thus promoting the high-quality development of the local economy. This paper explores the application of the project teaching method in terms of the current situation of the construction of the course, the analysis of problems, the specific application of the project teaching method in the course and the evaluation of the course, based on the professional course Urban Bus Planning and Operation Management of Nanning University’s transportation major.
Data management

Proper data storage, management, and documentation are the responsibility of all researchers. This chapter will provide scientists with the best practices and skills required for stewardship of the data collected during any and all research activities. Data management considerations, including project data and metadata documentation, will be discussed. Data organizational topics will include data storage options and data management software and tools and will highlight the resources available to help researchers and scientists sort through the complexities of data management. Lastly, this chapter will outline data compliance standards to follow and introduce data management plans to ensure there is a low-risk environment for the loss of data or breach of confidentiality.
Konx: cross-resolution image quality assessment

Abstract Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), down-sampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent discrepancies induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX , a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX , we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column deep neural network architecture that improves performance over previous state-of-the-art IQA models for this task. We thus both raise and address a novel research problem in image quality assessment.