Reviewerly/exHarmony huggingface dataset:
Enhancing UAV Safety: Accurate Distance Measurement with YOLOV8-based Measuring Application

Abstract This article introduces a lightweight and efficient model for measuring applications, aimed at enhancing the current UAV monitoring system. The primary objective of this project is to develop a measuring application capable of determining and displaying the distance between the camera on the UAV and the facial model. The YOLOV8 framework is employed as a detection model to identify and interpret objects within the region of interest. Additionally, the algorithm incorporates the concept of focal length in lenses to calculate the distance between the facial expressions of a human face and the camera. To assess the algorithm’s accuracy, facial models were placed at various distances from the camera during testing. The predicted distance values obtained through the algorithm were then compared to the actual measured distances using a measuring tape. The results demonstrated a maximum tolerance of ±0.9 cm, indicating the algorithm’s reliable performance in predicting distance measurements.
Defect detection in carbon fiber-reinforced composites using directional dark-field imaging and tomography

Abstract This paper proposes the use of circular X-ray grating interferometry as an effective technique for defect detection with potential applications for in-line inspection of carbon fiber-reinforced pultruded profiles used inside the load-carrying spar caps of wind turbine blades. A fuzzball defect in the pultruded profile is characterized as a demonstration. The method allows for large field-of-view quantification of local fiber alignment and relative fiber volume fraction. A two-dimensional through the thickness averaged distribution of the fiber orientation, the mean scattering, and fractional anisotropy are determined. Based on this, it is possible to determine the size of the defect as well as quantify the severity of the defect.
Exploiting Millimeter-Wave Radars to Enable Accurate Gesture Recognition for the Metaverse Environment

This contribution aims to demonstrate the potential of highly integrated millimeter-wave radars in the framework of metaverse applications. In detail, a 122 GHz radar is mounted on glass frames, with the task to detect hand gestures, thus enabling more immersive and interactive user experiences. Current metaverse glasses exploit camera sensor to recognize the user gestures. The proposed system has the purpose to overcome the known limitations of the current camera-based technology, mainly due to the poor radial range detection accuracy and performance dependance on the light conditions. In this contribution, some gestures whereby a camera-based system could fail are properly detected during the experimental activity. The radar detection mainly exploits the phase-analysis to extract the displacement and the micro-Doppler signature. This work paves the way for the next generation interactive systems in the metaverse environment whereby the radar technology might be exploited along with the traditional sensors to provide an advanced, complete, and reliable interaction to the users.
Effectiveness criteria of suspicious transaction reporting for early detection of terrorist financing activities

Purpose This study aims to evaluate the suspicious transaction reporting (STR) as a financial intelligence tool to identify the potential strengths and limitations of STR and to come up with the criteria, which will make this tool an effective one in early detection of terrorist financing activities. Design/methodology/approach Considering the research aim, this research uses the funnelling method for identifying effectiveness criteria. Funnelling is a method of literature review that helps find pertinent literature by refining the search through filtering the available research (Ridley, 2008). Using this method, the researcher first applied the criteria of actionable intelligence to filter the financial intelligence tools to select the most promising and important tool (suspicious transaction reporting) for early detection of terrorist financing activities. The funnelling method was also applied to derive the effectiveness criteria from the operational features, and corresponding limitations, of the suspicious transaction reporting system. The funnelling method was also used to identify those operational features and limitations of suspicious transaction reporting that have the most direct relevance to the early detection problem of suspicious transaction reporting. Findings There are some operational features of STR that give rise to certain limitations that undermine its effectiveness in terms of early detection of terrorist financing activities. The limitations of STR necessitate a search for criteria that will make STR effective in early detection of terrorist financing activities. Based on the operational features and their corresponding limitations, effectiveness criteria for STR have been derived in this study. It is shown how these effectiveness criteria can remove the limitations of STR. Research limitations/implications The list of operational features and the corresponding limitations based on which the effectiveness criteria have been derived may not be exhaustive. There may have other operational features, and corresponding limitations that also make STR largely ineffective in the early detection of terrorist financing activities, and for which more effectiveness criteria should also be derived. Practical implications The limitations and the effectiveness criteria will pave the way for redesigning STR in such a way that will make it highly useful for detecting financing activities relating to imminent terrorist attacks. Social implications The society will experience fewer terrorist attacks that will make the society peaceful, happy and vibrant. Originality/value In this study, the effectiveness criteria of STR for early detection of terrorist financing activities have been derived in an innovative way by deducing them from the operational features of STR and the corresponding limitations.
Approximate Foveated-Based Super Resolution Method for Headset Displays

Wearable devices have become increasingly popular in the last years. Among them, mixed-reality headset displays (HSDs) represent the most critical device for several application fields. Indeed, they must provide users with immersive experiences of the real world through displays with high-resolution and high refresh rate and must be comfortable to be worn. Meeting such requirements is not trivial and custom energy efficient architectures for the video processing pipeline are needed. In this work, we evaluate the performance of an approximate foveated-based Super-Resolution method for image upscaling. Preliminary results show that the proposed approach allows significantly reducing the number of operations with respect to the prior art, without introducing detrimental effects on the perceptual visual quality.
End of Learning Design?

This highly interactive and provocative hypothetical-style panel discussion will take the audience on a collective journey through the complexity and challenges of strategically designing and delivering online learning in a post-digital, post-crisis new normal. The aim of the panel is to collectively shift the dial on how we understand, articulate, and develop the benefit of learning design to the institution, how others have been extremely successful in doing that (and what can we learn from them) and how we are collectively struggling with precarious contracts, budgets, de-professionalisation and constantly shifting priorities as we return to a campus-based teaching and learning experience. Drawing on the principles of hyperreality, participants will become part of the community of a hypothetical university. Through highly interactive and immersive scenarios the panel session will collectively traverse the transitional, uncertain spaces of a post-crisis university and design for an educational future that learns from the relationships between people and technology to make teaching and learning better.
Impacts of external factors on Ethiopia's economic growth: Insights on foreign direct investment, remittances, exchange rates, and imports

This study conducts a rigorous examination of relationships that are often assumed but rarely tested within the context of Ethiopia's journey towards sustainable development. An autoregressive distributed lag model is employed using annual time series data from 1982 to 2021 to investigate the short- and long-term impacts of foreign direct investment (FDI), remittances, real exchange rates, and imports on Ethiopia's economic growth. By incorporating additional control variables, this study contributes nuanced insights that were previously lacking in the literature. The findings reveal intriguing patterns that both align with and deviate from existing frameworks. Contrary to some prior studies, foreign investment consistently emerges as a significant driver of economic growth over time. However, remittances demonstrate only transient significance, highlighting the need for cautious policy considerations. The influence of exchange rates on economic growth proves to be unexpectedly complex and nonlinear, challenging conventional assumptions. The empirical validation of these multifaceted realities underscores the importance of this analysis. Furthermore, robustness tests conducted in this study confirm the reliability of the findings while shedding light on additional intricacies. For instance, the relationship between imports and growth is context-dependent and exhibits ambiguities that call for careful consideration. The theoretical and practical implications derived from this research offer valuable insights for researchers and policymakers alike. The recommendations put forth emphasize the promotion of sustained prosperity through evidence-based strategies that prioritize community development.
Single image dehazing via decomposition and enhancement

Abstract Hazy images suffer from two problems. The low contrast can be enhanced by estimating a transmission layer, and the colour cast can be restored by estimating an airlight. These two variables, together with the albedo layer, are the constitutive elements of a hazy image. The resulting quality of dehazed images is inextricably linked to the accurate estimation of these components. However, it is ill‐posed to decompose these variables from a single image. As such, this paper presents an innovative algorithm intended to facilitate the optimal decomposition of a hazy image. Using the Markov random field model, an optimal framework is established that allows the simultaneous estimation of the three components across the three‐colour channels. To improve the visual quality, three improvements are proposed in the variational solution for the optimal components. The dehazed result is recomposed from the components with the transmission enhanced to circumvent any potential artefacts or information loss. Extensive experiments on natural images corroborate that the proposed algorithm outperforms state‐of‐the‐art dehazing methods, both qualitatively and quantitatively.
Quantum Metrology in the Noisy Intermediate‐Scale Quantum Era

Abstract Quantum metrology pursues the physical realization of higher‐precision measurements to physical quantities than the classically achievable limit by exploiting quantum features, such as entanglement and squeezing, as resources. It has potential applications in developing next‐generation frequency standards, magnetometers, radar, and navigation. However, the ubiquitous decoherence in the quantum world degrades the quantum resources and forces the precision back to or even worse than the classical limit, which is called the no‐go theorem of noisy quantum metrology and greatly hinders its applications. Therefore, how to realize the promised performance of quantum metrology in realistic noisy situations attracts much attention in recent years. The principle, categories, and applications of quantum metrology are reviewed. Special attention is paid to different quantum resources that can bring quantum superiority in enhancing sensitivity. Then, the no‐go theorem of noisy quantum metrology and its active control under different kinds of noise‐induced decoherence situations are introduced.
Enhancement of Data Security for Cloud Computing with Cryptography Techniques

In cloud computing, users may access their crucial data that is kept on distant servers over the Internet. The amount of sensitive and important data is expanding quickly as technology develops every day. Data security is crucial in cloud computing because cloud computing involves storing and processing data on remote servers, often belonging to third-party providers. This means that data is transmitted and stored outside of the user’s immediate control, making it more vulnerable to security breaches and cyberattacks. A security breach of user data highlights the importance of user privacy. Every form of data has a varied level of protection requirements. In this paper, we have suggested a model that categorizes the data in accordance with its security characteristics. We have created a new hybrid approach to achieve this. We have built a hybrid algorithm using AES and DES. The performance of the hybrid system is compared to the current hybrid technology, showing that the recommended approach offers excellent security and confidentiality of user data. The shortcomings of both symmetric and asymmetric encryptions are solved by hybrid cryptography and steganography.
Heliostat distribution optimization model based on simulated annealing algorithm

&lt;p class="MsoNormal" style="text-indent: 22pt; text-align: justify;"&gt;&lt;span lang="EN-US" style="font-size: 11.0pt; font-family: 'Times New Roman',serif;"&gt;In order to solve the problem of calculating the efficiency of the heliostat distribution, based on the knowledge of physics and astronomy, this paper establishes a variety of physical parameter calculation models, comprehensively considers the relevant data and constraints of the heliostat field, solves and calculates the parameters of the heliostat field, and calculates the optical efficiency and output thermal power of the heliostat according to the relevant calculation formulas. In order to optimize the design of heliostat field, the optimal results of each parameter of heliostat field are obtained through multiple iterative calculations and comparative analysis by using the simulated annealing algorithm, which realizes the optimization of the heliostat field and ensures the highly efficient performance of the heliostat field.&lt;/span&gt;&lt;/p&gt;
CABDet: context-and-attention-based detector for small object detection in remote sensing images

Detecting small objects in remote sensing images is a challenging task. Existing object detectors for remote sensing images suffer from two issues: (1) insufficient feature extraction for small objects in the backbone network and (2) feature misalignment and information loss for small objects in the neck network, leading to poor detection performance on small objects. To address these challenges, a small object detector named CABDet for remote sensing images that combines context and attention mechanisms is proposed. Specifically, an enhanced ResNet50 is designed as a novel backbone network that adaptively adjusts the size of receptive fields to fully extract feature information of small objects. Additionally, an adaptive multiscale feature pyramid network (AM-FPN) is proposed. To alleviate the problem of feature misalignment for small objects, AM-FPN leverages self-attention mechanisms to establish semantic and spatial dependencies between adjacent feature layers. Then to mitigate the issue of information loss for small objects, AM-FPN captures semantic dependencies between subregions of current layer features through self-attention mechanisms to preserve channel information. Extensive experiments were conducted on two demanding remote sensing datasets, namely dataset for object detection in aerial images and UCAS-high resolution aerial object detection dataset, to demonstrate the effectiveness of the proposed methodology in achieving superior detection performance when compared with contemporary state-of-the-art approaches.
Analysis of external modification devices and framing moves in request emails

Abstract The purpose of this study was to examine the pragmatic competence of writing request emails by Chinese learners of Japanese (CLJs). The study focused on the features of the external modification devices and framing moves of request emails by CLJs when compared to emails by native speakers of Japanese (NSJs). Data were collected from 104 CLJs and 53 NSJs, using an Electronic Writing Discourse Completion Test questionnaire. One-way ANOVA results showed that as the CLJs’ levels of proficiency in Japanese increased, their use of external modification devices and framing moves tended to increase. In addition, a Fisher’s exact test showed that as the CLJs’ proficiency increased, a distinct statistical difference only existed in the use of individual framing moves, but not in that of individual external modification devices. This study provides conceptual categories and utterances used in both external modification devices and framing moves for teaching email communication to CLJs.
Way of Thinking

It is obvious, but it is more important to properly plan and design a project before predicting the feasibility of the project. The early stages of conceiving a project are the most important. If the project is conceived incorrectly, the feasibility probability of the project will probably be quite low, and no results can be expected even if the project is implemented forcibly.
Method of Cultivating College Students' Independent Learning Ability Based on Integration of Multiple Algorithm

INTRODUCTION: The research on the multi-mode fusion of college students' independent learning ability cultivation method is conducive to college students' change of learning mode and learning thinking, improvement of the utilization rate of educational resources, and the development of the academic environment as well as the reform of the educational concept.&#x0D; OBJECTIVES: Aiming at the problems of college students' current independent learning mode, such as the need for more in-depth research and the single study means.&#x0D; METHODS: A method for cultivating college students' autonomous learning ability through the integration of intelligent optimization algorithms and multiple modes has been proposed. Firstly, the practices of analyzing the current college students' autonomous learning mode and multiple learning modes are analyzed; then, using the butterfly optimization algorithm, a weight optimization method for the cultivation of college students' independent learning ability based on the fusion of multiple modes is proposed; finally, the validity and robustness of the proposed method are verified through experimental analysis.&#x0D; RESULTS: The results show that the proposed method has a high cultivation effect.&#x0D; CONCLUSION: Solves the problem of fusion of college students' independent learning ability cultivation modes.
Visual-Linguistic-Stylistic Triple Reward for Cross-Lingual Image Captioning

Generating image captions in different languages is worth exploring and essential for non-native speakers. Nevertheless, collecting paired annotation for every language is time-consuming and impractical, particularly for minor languages. To this end, the cross-lingual image captioning task is proposed, which leverages existing image-source caption annotation data and wild unrelated target corpus to generate satisfactory caption in the target language. Current methods perform a two-step translation process of image-to-pivot (source) and pivot-to-target. The distinct two-step process comes with certain caption issues, such as the weak semantic alignment between the image and the generated caption and the generated caption’s non-target language style. To address these issues, we propose an end-to-end reinforce learning framework with Visual-Linguistic-Stylistic Triple Reward named TriR. In TriR, we jointly consider the visual, linguistic, and stylistic alignments to generate factual, fluent, and natural caption in the target language. To be specific, the image-source caption annotation provides factual semantic guidance, whereas the unrelated target corpus guides the language style of generated caption. To achieve this, we construct a visual reward module to measure the cross-modal semantic embedding of image and target caption, a linguistic reward module to measure the cross-linguistic embedding of source and target captions, and a stylistic reward module to imitate the presentation style of target corpus. The TriR can be implemented with either classical CNN-LSTM or prevalent Transformer architecture. Extensive experiments are conducted with four cross-lingual settings, i.e ., Chinese-to-English, English-to-Chinese, English-to-German and English-to-French. Experimental results demonstrate the remarkable superiority of our method, and sufficient ablation experiments validate the beneficial impact of every reward.
Impact of Google searches and social media on digital assets’ volatility

Abstract Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.
Leveraging Prompt and Top-K Predictions with ChatGPT Data Augmentation for Improved Relation Extraction

Relation extraction tasks aim to predict the type of relationship between two entities from a given text. However, many existing methods fail to fully utilize the semantic information and the probability distribution of the output of pre-trained language models, and existing data augmentation approaches for natural language processing (NLP) may introduce errors. To address this issue, we propose a method that introduces prompt information and Top-K prediction sets and utilizes ChatGPT for data augmentation to improve relational classification model performance. First, we add prompt information before each sample and encode the modified samples by pre-training the language model RoBERTa and using these feature vectors to obtain the Top-K prediction set. We add a multi-attention mechanism to link the Top-K prediction set with the prompt information. We then reduce the possibility of introducing noise by bootstrapping ChatGPT so that it can better perform the data augmentation task and reduce subsequent unnecessary operations. Finally, we investigate the predefined relationship categories in the SemEval 2010 Task 8 dataset and the prediction results of the model and propose an entity location prediction task designed to assist the model in accurately determining the relative locations between entities. Experimental results indicate that our model achieves high results on the SemEval 2010 Task 8 dataset.