Vehicle Detection and Distance Estimation Using Improved YOLOv7 Model

Abstract: In this book chapter, the authors propose a low-cost distance estimation approach to develop more accurate predictions from a 3D perspective for vehicle detection and ranging by using inexpensive monocular cameras. This distance estimation model integrates YOLOv7 model with an attention module (CBAM) and transformer, as well as extend the prediction vector as the fundamental architecture to improved high-level semantic understanding and enhanced feature extraction ability. This integration significantly improved detection and ranging performance, offering a more suitable and cost-effective solution for distance estimation.


Facial Emotion Recognition Using Ensemble Learning

Abstract: Facial emotion recognition (FER) is the task of identifying human emotions from facial expressions. The purpose of this book chapter is to improve accuracy of facial emotion recognition using integrated learning of lightweight networks without increasing the complexity or depth of the network. Compared to single lightweight models, it made a significant improvement. For a solution, the authors proposed an ensemble of mini-Xception models, where each expert is trained for a specific emotion and lets confidence score for the vote. Therefore, the expert model will transform the original multiclass task into binary tasks. The authors target the model to differentiate between a specific emotion and all others, facilitating the learning process. The principal innovation lies in our confidence-based voting mechanism, in which the experts “vote” based on their confidence scores rather than binary decisions.


Human Action Recognition Based on YOLOv7

Abstract: Human action recognition is a fundamental research problem in computer vision. The accuracy of human action recognition has important applications. In this book chapter, the authors use a YOLOv7-based model for human action recognition. To evaluate the performance of the model, the action recognition results of YOLOv7 were compared with those using CNN+LSTM, YOLOv5, and YOLOv4. Furthermore, a small human action dataset suitable for YOLO model training is designed. This data set is composed of images extracted from KTH, Weizmann, MSR data sets. In this book chapter, the authors make use of this data set to verify the experimental results. The final experimental results show that using the YOLOv7 model for human action recognition is very convenient and effective, compared with the previous YOLO model.


Analysis Model at the Sentence Level for Phishing Detection

Abstract: Global cyber dangers related to phishing emails have increased dramatically, particularly after the COVID-19 epidemic broke out. Many companies have suffered significant financial losses as a result of this kind of assault. Even though many models have been developed to distinguish between phishing efforts and genuine emails, attackers always come up with new ways to trick their targets into falling for their scams. Many companies have suffered significant financial losses as a result of this kind of assault. Although phishing detection algorithms are being developed, their accuracy and speed in recognizing phishing emails are not up to par right now. Furthermore, the number of phished emails has alarmingly increased lately. To lessen the negative effects of such bogus communications, there is an urgent need for more effective and high-performing phishing detection algorithms. Inside the framework of this study, a thorough examination of an email message's email header and content is carried out. A novel phishing detection model is built using the features of sentences that are extracted. The new dimension of sentence-level analysis is introduced by this model, which makes use of k-nearest neighbor (KNN). Kaggle's well-known datasets were used both to train and evaluate the model. Important performance indicators, including the F1-measure, precision, recall, and accuracy of 0.97, are used to assess the efficacy of this approach.


Decentralized Finance (DeFi) and Blockchain Technology in Healthcare

Abstract: The chapter examines how block chain technology can revolutionize pharmaceutical supply chain management. Through real-time tracking and traceability, the authenticity of medications can be ensured, thereby combating the persistent issue of counterfeit drugs and enhancing patient safety. Additionally, the clinical trial process stands to benefit from block chain and transparent and secure nature. Smart contracts can streamline patient recruitment, informed consent, and trial result reporting, promoting trust and accountability in the clinical trial ecosystem. Despite the potential benefits, the adoption of DeFi and block chain in healthcare presents several challenges, including regulatory compliance, scalability, and integration with existing healthcare systems. This chapter discusses the current state of block chain integration in healthcare and identifies future research directions to overcome these hurdles.


New online project “Collectors of The Russian Museum of Ethnography”: concept, preliminary results

Abstract: The article contains a presentation of the concept and preliminary results of the new virtual project "Collectors of the Russian Ethnographic Museum", launched in 2021. Its purpose is to recreate individual portraits of scientists, correspondents and museum staff, personal and creative biographies of those who during the XIX — early XXI c., and were associated with the museum field. The fates of some of them are quite well known, others are little studied due to various circumstances. It's not just the famous specialists (ethnographers, linguists, folklorists, archaeologists, historians, etc.) and correspondents, but also representatives of different social circles, scientific and political views, levels of education, nationalities, confessions, ages, characters; with different motivation for ethnographic research and subjective approaches to their own collecting activities. Currently, according to the concept and well-thought-out scheme, materials about more than 130 people have been published, more than 30 museum employees participated in their creation. URL:https://ethnomuseum.ru/collections/projects/collectors/.


Quantum random number generation using an on-chip nanowire plasmonic waveguide

Abstract: Abstract Quantum random number generators employ the inherent randomness of quantum mechanics to generate truly unpredictable random numbers, which are essential in cryptographic applications. While a great variety of quantum random number generators have been realised using photonics, few exploit the high-field confinement offered by plasmonics, which enables device footprints an order of magnitude smaller in size. Here we integrate an on-chip nanowire plasmonic waveguide into an optical time-of-arrival based quantum random number generation setup. Despite loss, we achieve a random number generation rate of $14.4\,\text{Mbits/s}$ using low light intensity, with the generated bits passing industry standard tests without post-processing. By increasing the light intensity, we were then able to increase the generation rate to $41.4\,\text{Mbits/s}$, with the resulting bits only requiring a shuffle to pass all tests. This is an order of magnitude increase in the generation rate and decrease in the device size compared to previous work. Our experiment demonstrates the successful integration of an on-chip nanoscale plasmonic component into a quantum random number generation setup. This may lead to new opportunities in compact and scalable quantum random number generation.


Gender differences in online abuse: the case of Dutch politicians

Abstract: Abstract Online abuse and threats towards politicians have become a significant concern in the Netherlands, like in many other countries across the world. This paper analyses gender differences in abuse received by Dutch politicians on X (formerly Twitter), while taking into account the possible additional impact of ethnic minority status. All tweets directed at party leaders throughout the entire year of 2022 were collected. The effect of gender and ethnic minority status were estimated for six different linguistic measures of abuse, namely, toxicity, severe toxicity, identity attacks, profanity, insults, and threats. Contrary to expectations, tweets directed at male politicians scored higher on all forms of abuse. Significant interaction effects between gender and ethnic minority status were found for a number of abuse measures. Tweets directed at ethnic minority female politicians scored higher on severe toxicity, identity attacks, and profanity, compared to those directed at ethnic majority female politicians. Importantly, female ethnic minority politicians received the highest levels of threats compared to all groups. Given that online abuse and threats are reported to have a negative effect on political participation and retention, these results are particularly worrying.


Singer identification model using data augmentation and enhanced feature conversion with hybrid feature vector and machine learning

Abstract: Abstract Analyzing songs is a problem that is being investigated to aid various operations on music access platforms. At the beginning of these problems is the identification of the person who sings the song. In this study, a singer identification application, which consists of Turkish singers and works for the Turkish language, is proposed in order to find a solution to this problem. Mel-spectrogram and octave-based spectral contrast values are extracted from the songs, and these values are combined into a hybrid feature vector. Thus, problem-specific situations such as determining the differences in the voices of the singers and reducing the effects of the year and album differences on the result are discussed. As a result of the tests and systematic evaluations, it has been shown that a certain level of success has been achieved in the determination of the singer who sings the song, and that the song is in a stable structure against the changes in the singing style and song structure. The results were analyzed in a database of 9 singers and 180 songs. An accuracy value of 89.4% was obtained using the reduction of the feature vector by PCA, the normalization of the data, and the Extra Trees classifier. Precision, recall and f-score values were 89.9%, 89.4% and 89.5%, respectively.


Trends and Transformations of E-Retailing in Jordan Sales Market: Challenges, Growth, and Potential Future Developments

Abstract: This study aimed to explore the trends and transformations of the e-retail market in Jordan, besides the aspects that have contributed to these changes. It also aimed to examine the behaviour and preferences of consumers about e-shopping. Additionally, it focused on the key players in the e-retail market in Jordan. The current study collected primary data by using a questionnaire where 207 e-shoppers and e-retailers were selected as a typical sample through convenience and obliging sampling techniques. The data was analyzed using SPSS and descriptive statistics were used to sum up the survey answers. The outcomes of the study advisable that e-retail trade had a positive impact on the business sector in Jordan, with its growth probable and trends. The study also set up those changes played a vital role in the growth of e-retail trade in Jordan, due to the rising use of the internet and inconstant consumer preferences. Surveyed companies concentrate on providing personalized shopping experiences, logistics services, and fulfilment services that effectively magnify customer satisfaction and loyalty. To increase online sales, practitioners in the e-retail industry in Jordan can leverage IT and create outreach strategies while investing in IT capabilities. However, the study findings are context-specific, and future research should consider examining the impact of e-retailing on competitive advantage across different industries and countries.


Information technology, gender economic inclusion and environment sustainability in sub-Sahara Africa

Abstract: Purpose This study examines the relevance of information and communication technologies in the effect of gender economic inclusion on environmental sustainability. Design/methodology/approach The focus is on a panel of 42 sub-Saharan African countries over the period 2005–2020. The empirical evidence is based on generalized method of moments. The environmental sustainability indicator used is CO2 emissions per capita. Three indicators of women’s economic inclusion are considered: female labour force participation, female employment and female unemployment. The chosen ICT indicators are mobile phone penetration, Internet penetration and fixed broadband subscriptions. Findings The results show that: (1) fixed broadband subscriptions represent the most relevant ICT moderator of gender economic inclusion for an effect on CO2 emissions; (2) negative net effects are apparent for the most part with fixed broadband subscriptions (3) both positive ICT thresholds (i.e., critical levels for complementary policies) and negative ICT thresholds (i.e., minimum ICT levels for negative net effects) are provided; (4) ICT synergy effects are apparent for female unemployment, but not for female employment. In general, the joint effect of ICTs or their synergies and economic inclusion should be a concern for policymakers in order to better ensure sustainable development. Moreover, the relevant ICT policy thresholds and mobile phone threshold for complementary policy are essential in promoting a green economy. Originality/value The study complements the extant literature by assessing linkages between information technology, gender economic inclusion and environmental sustainability.


Mineral and S-Isotope Compositions of Cu-Sulfide Deposits in Southern Siberia (Kodar–Udokan Region), Russia

Abstract: The Kodaro–Udokan region is a huge Cu metallogenic province in Southern Siberia, one of the largest on Earth. It contains world-class copper sandstone-hosted Udokan (Cu reserves of 26.7 Mt) and PGE-Ni-Cu Chineysky deposits related to gabbro–anorthosite pluton (Cu—10 Mt; Fe-Ti-V, 30 Gt of ore). Furthermore, there are many small deposits of sulfide ores in sedimentary and igneous rocks in this region as well. For many decades, their genesis has been hotly debated. We studied the mineral composition and the sulfur isotopes in several deposits located at different levels of the stratigraphic sequence and in gabbro intruded in sandstones of the Udokan complex. The differences in ore compositions were found. The Burpala and Skvoznoy deposits consisting of the chalcocite–bornite association are characterized only by negative δ34S. The δ34S values for the Udokan deposits are mostly <0 (up to −28‰). The positive δ34S data characterize the ores of the Chineysky and Luktursky intrusions. Two Cu sandstone-hosted deposits are characterized by complex ore composition, i.e., the Krasny deposit, comprising chalcopyrite–pyrrhotite ores, is enriched in Co, Ni, Bi, Sb, Mo, Pb, Zn, Se, Te, and U and has a wide range of δ34S = −8.1–+13.5‰, and the Pravoingamakitsky deposit (Basaltovy section), consisting of quartz–chalcopyrite veins, has high PGE contents in ores with δ34S = +2.9–+4.0‰. These deposits are located near the gabbro massifs, and it is supposed that their ore compositions were influenced by magmatic fluids. The general regularities of the localization of the deposits in rift zones, and the proximity of mineral and isotopic composition allow us to conclude that the main source of copper could be rocks of basic composition because only they contain high Cu contents. Fluids from deep zones could penetrate to the surface and form Cu sandstone-hosted deposits.


A Python code for adjusting periodic soil temperature data

Abstract: This work aims to present a code in Python language for adjusting periodic curves from soil temperature simulation data. We present a methodology to obtain a sinusoidal function using the least squares method that represents the soil temperature of a given locality. The code also uses Newton's method to solve the resulting nonlinear system of equations. Soil temperature simulation data were used in a city in the state of Rio Grande do Sul, Brazil. The results of these curves can help in the study and definition of boundary conditions of new computational models of ground-to-air heat exchangers (TCSA). It is observed that the least squares method is not new , but there is a gap in the literature regarding the presentation of codes for the adjustment of trigonometric functions as presented here.


Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation

Abstract: Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many algorithms to aid neural network understanding and provide additional information to the human expert. One of the most popular methods being Layer-Wise Relevance Propagation (LRP). This method assigns local relevance based on the pixel-wise decomposition of nonlinear classifiers. With the rise of attribution method research, there has emerged a pressing need to assess and evaluate their performance. Numerous metrics have been proposed, each assessing an individual property of attribution methods such as faithfulness, robustness or localization. Unfortunately, no single metric is deemed optimal for every case, and researchers often use several metrics to test the quality of the attribution maps. In this work, we address the shortcomings of the current LRP formulations and introduce a novel method for determining the relevance of input neurons through layer-wise relevance propagation. Furthermore, we apply this approach to the recently developed Vision Transformer architecture and evaluate its performance against existing methods on two image classification datasets, namely ImageNet and PascalVOC. Our results clearly demonstrate the advantage of our proposed method. Furthermore, we discuss the insufficiencies of current evaluation metrics for attribution-based explainability and propose a new evaluation metric that combines the notions of faithfulness, robustness and contrastiveness. We utilize this new metric to evaluate the performance of various attribution-based methods. Our code is available at: https://github.com/davor10105/relative-absolute-magnitude-propagation


The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection

Abstract: Some fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low. In response to the above issue, this study proposes a distributed Gaussian mixture cardinality jumping Markov-cardinalized probability hypothesis density (GM-JMNS-CPHD) filter based on a generalized inverse covariance intersection. The state estimation of the JMNS-CPHD filter combines the state evaluation of traditional CPHD filters with the state estimation of jump Markov systems, estimating the target state of multiple motion models without knowing the current motion models. The performances of the generalized covariance intersection (GCI)GCI-GM-JMNS-CPHD and generalized inverse covariance intersection (GICI)GICI-GM-JMNS-CPHD methods are evaluated via simulation results. The simulation results show that, compared with algorithms such as Sensor1, Sensor2, GCI-GM-CPHD, and GICI-GM-CPHD, this algorithm has smaller optimal subpattern assignment (OSPA) errors and a higher fusion accuracy.


Leveraging FinTech Compliance to Mitigate Cryptocurrency Volatility for Secure US Employee Retirement Benefits: Bitcoin ETF Case Study

Abstract: The integration of cryptocurrencies, particularly Bitcoin, into retirement savings plans has recently garnered significant attention. This interest has been amplified by the U.S. Securities and Exchange Commission's approval of Bitcoin Exchange-Traded Funds (ETFs) in January 2024 and Fidelity Investments' decision to include Bitcoin in their 401(k) plans. These landmark developments represent a paradigm shift in retirement investment strategies, merging traditional financial planning with the dynamic and volatile world of cryptocurrencies. The entry of Bitcoin introduces novel challenges, including increased volatility and regulatory uncertainty, necessitating a comprehensive examination of its impacts on retirement savings. The study sought to explore the role of Financial Technology (FinTech) in managing these risks and assess the adequacy of current regulatory frameworks. Employing a quantitative research approach, the study collected data from 386 participants, including FinTech practitioners and investment portfolio managers through a survey that combining closed-ended and open-ended questions. Multiple regression was used to analyze the relationships between variables such as FinTech integration, regulatory compliance, and the risk associated with cryptocurrency-inclusive retirement portfolios. The study revealed that the inclusion of Bitcoin significantly increases portfolio volatility. It also found that advanced FinTech data management techniques significantly enhance risk mitigation, while current regulatory frameworks are inadequate for governing the inclusion of cryptocurrencies in retirement plans. A comprehensive framework combining FinTech and regulatory compliance was shown to effectively reduce investment risks. The study recommends cautious consideration of cryptocurrencies in retirement portfolios, with an emphasis on assessing the risk appetite of participants. It advocates for dynamic regulatory frameworks and enhanced use of FinTech for real-time risk management. The study suggests that retirement plan providers should adopt an integrated approach, combining technological innovations with regulatory oversight, to navigate the complexities of cryptocurrency investments effectively.


The Model of the Digital Competence Profile of Pedagogical University Students

Abstract: The article actualizes the assessment of the competencies of students of a pedagogical university through the systematic collection and analysis of the level of competence formation in the process of mastering the main professional educational program. The need to monitor and evaluate the development of students’ competencies makes it possible to identify the absence or insufficient level of competence formation among students and make appropriate managerial and pedagogical decisions to improve the quality of the educational process. One of such means is the digital competence profile of a student of a pedagogical university, by which the authors understand the system of collecting, analyzing and visualizing a set of competencies that are formed and developed by students as a result of mastering the basic educational program and extracurricular activities necessary for the implementation of professional pedagogical activity, presented digitally in the information and educational environment of the university. The article presents a model of the digital competence profile of pedagogical university students, identifies structural elements and functional relationships between them. The purpose of the article is to develop a model of the digital competence profile of pedagogical university students, to identify objects and subjects of the digital profile, to determine the structural elements of the digital profile of a pedagogical university student, to identify functional relationships and relationships between the structural elements of the model. Methodology and methods of research. The study was carried out on the basis of the modeling method and the structural and functional method, which provides identification of the structural elements of the digital profile of a student of a pedagogical university, determination of functional relationships and relationships between structural elements and their functions relative to each other. Conclusion. The model proposed by the authors is aimed at tracking the level of formation of the digital profile of competencies during the transition from formal assessment of knowledge to informal assessment of abilities, forming an individual route of formation in the context of a successful individual career trajectory of a student of a pedagogical university. The model is the basis for the development of an information system of a digital tool and can be used to monitor and evaluate the professional competencies of students of a pedagogical university.


Social and Professional Adaptation of Students in the Educational Environment of the University in the Last Third of the 20th Century: Experience of Organization and Interpretation

Abstract: The relevance of the problems of the presented research is connected with the understanding and evaluation of some forms of organization of social and professional adaptation of first-year students and their integration into the educational environment of the university, established and implemented in the practice of domestic universities in the last third of the 20th century, their influence on personal, social and professional development of future teachers. The purpose of the article is to reflect on the experience of adaptation and integration of first-year students into the educational environment of the university, which has developed in the activities of historical and pedagogical faculties that operated in the USSR from 1966 to 1996. The reflection of such experience is focused on the search for productive ideas that allow students to adapt to the norms and values of the educational environment in the modern practice of professional psychological and pedagogical education, form sustainable motives for self-development, self-education, and intensively prepare for professional activities in the Russian education system. The leading methodological ideas in the analysis of the experience of adaptation and integration of students into the educational environment of the university are the principles of objectivity, consistency, historicism, reliance on the results of practical activities, on expert judgments and assessments of direct participants and organizers of this work in the last third of the 20th century. For these purposes, the method of monographic description of the practical experience of the activities of the installation camp of the historical and pedagogical faculty of the Kursk State Pedagogical Institute, conversations with teachers and students, participant observation, study of scientific texts, press materials and Internet resources was used. Research results. The study made it possible to reveal the place and role of installation camps in ensuring the adaptation and integration of first-year students into the educational environment of the university, to substantiate the main functions of the camp in the general logic of the personal, social and professional development of future teachers, to characterize some effective types and forms of work, their practical effectiveness. In particular, the essence and content of the adaptive, integration, value-orientation functions of the camp of first-year students are revealed; presents the types and forms of work on the socio-moral, personal and professional development of first-year students; the role of senior students in organizing the life of future teachers in the educational environment of the university is characterized. Conclusion. The materials of the article can be used by heads of humanitarian faculties, teachers of psychological and pedagogical disciplines, specialists in the field of history of higher professional pedagogical education.


MMED: GEOGRAPHICAL KNOWLEDGE ENHANCEMENT FOR GRADE 7 STUDENTS AT REINA MERCEDES VOCATIONAL SCHOOL

Abstract: This action research investigates the effectiveness of the MMED (Map Me Educational Booklet) in enhancing the geographical knowledge of Grade 7 students at Reina Mercedes Vocational and Industrial School. Grounded in the interdisciplinary nature of social science, particularly the symbiotic relationship between geography and history, the study addresses challenges in Araling Panlipunan, revealing disinterest due to a lack of relevance and interactive learning experiences. Inspired by research on teaching aids and supplementary materials, the researcher developed the "Map Me Educational Booklet" as an evidence-based intervention. Employing a Quasi-Experimental design, the study randomly assigned students to control and experimental groups. Results indicate a substantial improvement in both groups, with the experimental group displaying a more significant enhancement. Statistical analyses support the effectiveness of the booklet, emphasizing its positive impact on student performance. The study contributes to existing literature by aligning with real-world studies and providing a practical, evidence-based platform. Utilizing mean scores, variance analysis, t-tests, and Cohen's D, the research establishes the significance of the intervention. The findings support the booklet's role in active learning and highlight its potential for educational practices, emphasizing the need for innovative tools to address challenges in geography education. The study recommends the implementation of the "Map Me Educational Booklet" as a valuable supplement to enhance geographical knowledge, fostering implications for teaching and learning modalities.


Data Complexity: A New Perspective for Analyzing the Difficulty of Defect Prediction Tasks

Abstract: Defect prediction is crucial for software quality assurance and has been extensively researched over recent decades. However, prior studies rarely focus on data complexity in defect prediction tasks, and even less on understanding the difficulties of these tasks from the perspective of data complexity. In this paper, we conduct an empirical study to estimate the hardness of over 33,000 instances, employing a set of measures to characterize the inherent difficulty of instances and the characteristics of defect datasets. Our findings indicate that: (1) instance hardness in both classes displays a right-skewed distribution, with the defective class exhibiting a more scattered distribution; (2) class overlap is the primary factor influencing instance hardness and can be characterized through feature, structural, and instance-level overlap; (3) no universal preprocessing technique is applicable to all datasets, and it may not consistently reduce data complexity, fortunately, dataset complexity measures can help identify suitable techniques for specific datasets; (4) integrating data complexity information into the learning process can enhance an algorithm’s learning capacity. In summary, this empirical study highlights the crucial role of data complexity in defect prediction tasks, and provides a novel perspective for advancing research in defect prediction techniques.


Quantifying trade-offs in satellite hardware configurations using a super-resolution framework with realistic image degradation

Abstract: When designing and operating Earth observation satellites, trade-offs must often be made between different hardware components, operational considerations, etc… For example, considerations include how much of the mass budget should be spent on imaging lenses?, or how long should image exposure times be?. Resulting limitations in image resolution and quality can be partially compensated for using super-resolution (SR) techniques. However, deep SR networks recently applied to satellite imagery are often trained and tested on data that lacks the typical image degrading noise present in real satellite images. In this work, we combine a method for generating realistically degraded satellite images with a deep SR network, in the context of different satellite hardware configurations and geographical types. We use this framework to assess deep SR performance given realistic remote sensing payloads across different terrain types, by evaluating payload- and terrain-dependent SR performance in reconstructing realistically degraded images. The framework allows us to model the effect of alternative satellite hardware configurations on resulting SR image quality, providing insight into optimal satellite operations and payload design in the context of SR-based image quality enhancements.


Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations

Abstract: The scene graph is a novel data structure describing objects and their pairwise relationship within image scenes. As the size of scene graphs in vision and multimedia applications increases, the need for lossless storage and transmission of such data becomes more critical. However, the compression of scene graphs is less studied because of the complicated data structures involved and complex distributions. Existing solutions usually involve general-purpose compressors or graph structure compression methods, which are weak at reducing the redundancy in scene graph data. This paper introduces a novel lossless compression framework with adaptive predictors for the joint compression of objects and relations in scene graph data. The proposed framework comprises a unified prior extractor and specialized element predictors to adapt to different data elements. Furthermore, to exploit the context information within and between graph elements, Graph Context Convolution is proposed to support different graph context modeling schemes for different graph elements. Finally, an overarching framework incorporates the learned distribution model to predict numerical data under complicated conditional constraints. Experiments conducted on labeled or generated scene graphs demonstrate the effectiveness of the proposed framework for scene graph lossless compression.


A unified approach to goodness-of-fit testing for spherical and hyperspherical data

Abstract: Abstract We propose a general and relatively simple method to construct goodness-of-fit tests on the sphere and the hypersphere. The method is based on the characterization of probability distributions via their characteristic function, and it leads to test criteria that are convenient regarding applications and consistent against arbitrary deviations from the model under test. We emphasize goodness-of-fit tests for spherical distributions due to their importance in applications and the relative scarcity of available methods.


Beyond the ‘critical incident’: COVID-19, data journalism and the slow road to editorial automation in Australian newsrooms

Abstract: This article draws on a qualitative interview-based study and the framework of the ‘critical incident’ to explore whether, how and for whom the first year of the COVID-19 pandemic saw an increased uptake of data-driven automation in Australian newsrooms and with what implications for the field. Our findings show that, while news workers combined and adapted existing technologies to meet increased demands for rolling, data-driven coverage of the pandemic, structural and institutional factors prevented the uptake and embedding of forms of data journalism and editorial automation that may have assisted with providing more timely and effective public health information. The findings highlight the importance of COVID-19 as both an acute event and an ongoing situation that has revealed and prompted reflection on the practical and political challenges of data flows between government agencies and news organisations.


Pengenalan Fitur Free Pickup Service Pada Aplikasi “PosAja” Sebagai Upaya Mempermudah Masyarakat Dalam Mengirim Paket

Abstract: Seeing the competition in the business market world is getting tighter, PT. Pos Indonesia (Persero) which provides goods or mail delivery services if it does not innovate it will be less competitive, currently PT. Pos Indonesia (Persero) is ranked number 3 nationally in the national ranking of services. Free pickup servive service or free package pick-up is an effort made by PT. Pos Indonesia (Persero) so that customers do not go to other delivery services. The PosAja Indonesia application has great potential with various services provided, but the concern of the Indonesian people for the PosAja application is still lacking because of a number of problems, including PosAja is still fairly new, so the author is very interested in introducing the application as a form of community service with the title "The introduction of the free pickup service feature in the PosAja application as an effort to make it easier for people to send packages". Application introduction activities are carried out through Instagram social media by distributing posters addressed to all Indonesian people. The community service period starts on December 14 – 18, 202. For technical implementation, it starts from planning, action, and evaluation. The results of the introduction of the PosAja application through Instagram received a positive response from the public, and simply added information to the community. With the introduction of the PosAja application, it is expected that users of this application will increase. The introduction of the free pickup service feature of the PosAja application is expected that the public can know the existence of the Pos Aja application so as to increase the adoption of the PosAja application.


Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images

Abstract: Omnidirectional images have gained significant popularity and drawn great attention nowadays, which poses challenges to omnidirectional image processing in solving the bottleneck of storage and transmission. Projecting onto a two-dimensional image plane is generally used to compress an omnidirectional image. However, the most commonly used projection format, the equirectangular projection (ERP), results in a significant amount of redundant samples in the polar areas, thus incurring extra bitrate and geometric distortion. We derive the optimal latitude-adaptive bit allocation for each image tile. Subsequently, we propose a greedy algorithm for non-negative integer bit allocation (NNIBA) for non-uniform quantization under an omnidirectional image quality metric WMSE. In our experiment, we design quantization tables based on JPEG and compare our approach with other sampling-related methods. Our method achieves an average bit saving of 7.9% compared with JPEG while outperforming other sampling-related methods. Besides, we compare our non-uniform quantization approach with two proposed bit allocation methods, achieving an average improvement of 0.35 dB and 2.66 dB under WS-PSNR, respectively. The visual quality assessment also confirms the superiority of our method.


Age‐related differences in the interplay of fluency and complexity in Chinese‐speaking seniors’ oral narratives

Abstract: Previous studies on language production in normal ageing have primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency and complexity. However, little attention has been paid to the complex, interconnected relations between these dimensions. Additionally, older adults have been treated as a homogeneous group, with little consideration for the differential characteristics of language performance across different stages of ageing.This study aims to investigate how increasing age impacts Chinese seniors' oral language performance, focusing on fluency (articulation rate and dysfluency rate), complexity (lexical and syntactic) and the potential interactions between these dimensions.Spontaneous oral narratives were collected from 60 normally ageing individuals, who were categorised into three groups: young-old (60-69 years old), middle-old (70-79 years old) and old-old (≥80 years old). Four measures for assessing language performance, namely, articulation rate, dysfluency rate, lexical complexity and syntactic complexity, were derived from the oral narratives. Dynamic systems techniques, including moving correlations, locally estimated scatterplot smoothing and Monte Carlo simulations, were employed for data analysis.This study revealed two major findings. First, across different age groups, the seniors' oral narratives significantly differed in the aspect of articulation rate and syntactic complexity. Specifically, both the young-old and the middle-old groups exhibited significantly higher articulation rates than the old-old group; the middle-old group also demonstrated significantly higher syntactic complexity compared to the old-old group. Second, the distinct subsystems (i.e., articulation rate, dysfluency rate, lexical and syntactic complexity) of seniors' oral narratives demonstrated varying interactions across different stages of ageing. While these subsystems tended to coordinate with each other in young-old individuals, they exhibited a greater tendency to compete in middle-old and old-old individuals.The findings reveal that subsystems of older adults' oral narratives display varying interactions with the increase of age, indicating that focusing solely on one dimension of language performance may result in inaccurate or misleading conclusions. Therefore, a multi-index comprehensive assessment should be employed for the enhancement of clinical evaluations of language performance in older adults. Additionally, it is vital to consider the interactional patterns (i.e., support or competition) between language subsystems when assessing language performance in normal ageing.What is already known on the subject Previous research on language production in normal ageing has primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency, vocabulary richness and grammatical complexity, overlooking the intricate interconnections between these dimensions. However, investigating these connections is significant for a thorough and in-depth understanding of language production in late adulthood, which operates as a system comprising interconnected components. Furthermore, existing studies have predominantly treated older adults as a homogeneous group, comparing their language use to that of young people. This approach lacks justification, given the substantial variations in language use among seniors at different stages of ageing. What this paper adds to existing knowledge The present findings illuminate the dynamic nature of language production in normal ageing. Not only do the distinct dimensions or subsystems of older adults' oral narratives (e.g., articulation rate, dysfluency rate, syntactic complexity) change with age, but more important, the ways in which these subsystems interact with each other also evolve with age, resulting in changing states of spoken language production in the ageing process. More specifically, our study provides evidence that the interactions among subsystems of older adults' oral narratives predominately transition from supportive relationships in earlier stages to competitive ones at later stages of ageing. What are the potential or actual clinical implications of this work? The present study suggests the use of multiple linguistic indices related to various aspects of language performance to assess the spoken language production of older adults. This is highlighted by the observation that various dimensions of older adults' oral narratives continuously interact with one another across different stages of ageing, and hence focusing solely on one dimension of language performance tends to lead to inaccurate or misleading results. Additionally, the patterns of interaction (i.e., support or competition) between different dimensions of language performance may reflect older adults' capacity to coordinate and control attentional resources and therefore should be taken into consideration in clinical evaluations of language performance in older adults.


Prosody in linguistic journals: a bibliometric analysis

Abstract: Abstract The present study provides a systematic review of prosody research in linguistic journals through a bibliometric analysis. Using the bibliographic data from 2001 to 2021 in key linguistic journals that publish prosody-related research, this study adopted co-citation analysis and keyword analysis to investigate the state of the intellectual structure and the emerging trends of research on prosody in linguistics over the past 21 years. Additionally, this study identified the highly cited authors, articles and journals in the field of prosody. The results offer a better understanding of how research in this area has evolved and where the boundaries of prosody research might be pushed in the future.


A Study on Developmental Strategies for Improving the Quality of Secondary Special Education for Students with Disabilities

Abstract: In the era of the fourth industrial revolution, addressing the multifaceted learning needs of students with special needs has become increasingly pivotal. This study aims to explore and enhance the quality of secondary special education, adapting to new educational methodologies in tandem with technological advancements while fostering the development of teachers and educational stakeholders. To identify the key areas necessitating development in secondary special education, interviews were conducted with special education teachers. These interviews informed the creation of a questionnaire, which was subsequently disseminated through a Google survey to special education teachers in various special schools and classes nationwide. The survey responses were analyzed using descriptive statistics to gain comprehensive insights. This study's findings integrate the perspectives from the interviews and survey data, leading to the formulation of strategies for the advancement of secondary special education. The analysis categorized the developmental needs into four primary domains: teacher development, curriculum enhancement, school infrastructure, and external factors related to the school environment. The paper concludes with a discussion that synthesizes these findings, offering a pathway for future improvements in the quality of secondary special education, and highlighting the importance of adapting to the changing educational landscape in the era of technological progress.


Teaching Process for Modules in the Information Technology Major for Higher Students According to the Competency Approach

Abstract: The first part of the article studies the concept of “competency” and approaches to competency, setting goals and output standards of the teaching process. The main part of the article focuses on analyzing the teaching process according to the competency approach, introducing the program content of the Information Technology knowledge block. The content of this program includes basic information technology, programming, operating systems, networks, databases, software, information security, artificial intelligence, web and mobile development, and management. projects, and cloud computing. The teaching process according to the competency approach is described in detail, starting from determining learning goals and output standards, followed by learning and practice. Using AI Chatbot and Website is recommended as an effective means of learning. Special emphasis is placed on student support through AI Chatbot, helping students to be autonomous and confident in the learning process. Diverse exercises and practices are provided to develop students’ abilities. Next is the testing and assessment step, emphasizing the importance of multi-dimensional assessment to ensure objectivity and fairness to test students’ knowledge and abilities. The last part of the article is to perfect the teaching process based on the competency approach in the Information Technology industry, with benefits such as the competency to personalize learning and develop learning competency for students.


RoboBoat: A robotic boat for 3D mapping of partially flooded underground sites

Abstract: Abstract This paper presents the development and field testing of RoboBoat, an unmanned surface vehicle designed to acquire three‐dimensional (3D) point cloud representations of narrow flooded galleries in caves, mines, and other similar environments where conventional surveying and mapping procedures cannot be applied. We developed techniques tailored to these scenarios, including a contour‐following reactive navigation method and an iterative closest point‐based simultaneous localization and mapping method. Custom metrics have been defined to iteratively validate and improve the 3D model of the passageways. The successful field tests in natural caves and mining galleries demonstrate the accuracy of the 3D representation, making it suitable for digital preservation of large‐volume cultural heritage sites.


Personalizing Government Services through Artificial Intelligence: Opportunities and Challenges

Abstract: Using artificial intelligence (AI) to customize government services brings advantages and challenges. On the one side, artificial intelligence (AI) can assist government organizations in better comprehending the needs and preferences of citizens, improving service delivery and raising citizen happiness. On the other hand, there are concerns around privacy, security, and ethical considerations related to the use of AI in government services. This article reviews the existing literature on the use of AI in personalizing government services, identifies key opportunities and challenges, and presents case studies of successful AI implementations in government services. The article concludes with recommendations for future research and practice in the area of AI and government services.


Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Abstract: Summary Advances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state‐of‐the‐art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text‐audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state‐of‐the‐art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research.


Suboptimal video coding for machines method based on selective activation of in‐loop filter

Abstract: Abstract A conventional codec aims to increase the compression efficiency for transmission and storage while maintaining video quality. However, as the number of platforms using machine vision rapidly increases, a codec that increases the compression efficiency and maintains the accuracy of machine vision tasks must be devised. Hence, the Moving Picture Experts Group created a standardization process for video coding for machines (VCM) to reduce bitrates while maintaining the accuracy of machine vision tasks. In particular, in‐loop filters have been developed for improving the subjective quality and machine vision task accuracy. However, the high computational complexity of in‐loop filters limits the development of a high‐performance VCM architecture. We analyze the effect of an in‐loop filter on the VCM performance and propose a suboptimal VCM method based on the selective activation of in‐loop filters. The proposed method reduces the computation time for video coding by approximately 5% when using the enhanced compression model and 2% when employing a Versatile Video Coding test model while maintaining the machine vision accuracy and compression efficiency of the VCM architecture.


A distance-based kernel for classification via Support Vector Machines

Abstract: Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.


Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles

Abstract: With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.


Hardware–Software Co-Design of an Audio Feature Extraction Pipeline for Machine Learning Applications

Abstract: Keyword spotting is an important part of modern speech recognition pipelines. Typical contemporary keyword-spotting systems are based on Mel-Frequency Cepstral Coefficient (MFCC) audio features, which are relatively complex to compute. Considering the always-on nature of many keyword-spotting systems, it is prudent to optimize this part of the detection pipeline. We explore the simplifications of the MFCC audio features and derive a simplified version that can be more easily used in embedded applications. Additionally, we implement a hardware generator that generates an appropriate hardware pipeline for the simplified audio feature extraction. Using Chisel4ml framework, we integrate hardware generators into Python-based Keras framework, which facilitates the training process of the machine learning models using our simplified audio features.


A method to increase video streaming speed and efficiency of storage video using multithreading and Random Access Memory

Abstract: There are numerous vital applications for online video storage and network transmission today. With exceptional scalability and browser and device compatibility, Hypertext Transfer Protocol (HTTP) live streaming (HLS) is today's most popular video and audio transmission protocol. In the article, the authors proposed a method of using multithreading and Random Access Memory (RAM) to store and transmit videos to help the server achieve higher speed, improve video transmission speed and video storage efficiency. The results of this research will help multimedia server developers evaluate the appropriate balance of resource usage while still ensuring the quality of online video transmission with a large number of videos and viewers.


Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures

Abstract: Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems’ vulnerabilities, attack structures, motives, and targets for assisting CIs’ designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community.


Quantum complementarity from a measurement-based perspective

Abstract: One of the most remarkable features of quantum physics is that attributes of quantum objects, such as the wavelike and particlelike behaviors of single photons, can be complementary in the sense that they are equally real but cannot be observed simultaneously. Quantum measurements, serving as windows providing views into the abstract edifice of quantum theory, are basic tools for manifesting the intrinsic behaviors of quantum objects. However, a quantitative formulation of complementarity that highlights its manifestations in general measurement scenarios remains elusive. Here we develop a general framework for demonstrating quantum complementarity in the form of information exclusion relations (IERs), which incorporates the wave-particle duality relations as particular examples. Moreover, we explore the applications of our theory in entanglement detection and elucidate that our IERs lead to an extended form of entropic uncertainty relations, providing insights into the connection between quantum complementarity and the preparation uncertainty.


A randomized operator splitting scheme inspired by stochastic optimization methods

Abstract: Abstract In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale optimization problems. The combination results in a randomized splitting scheme, which in a given time step does not necessarily use all the parts of the split operator. This is in contrast to deterministic splitting schemes which always use every part at least once, and often several times. As a result, the computational cost can be significantly decreased in comparison to such methods. We rigorously define a randomized operator splitting scheme in an abstract setting and provide an error analysis where we prove that the temporal convergence order of the scheme is at least 1/2. We illustrate the theory by numerical experiments on both linear and quasilinear diffusion problems, using a randomized domain decomposition approach. We conclude that choosing the randomization in certain ways may improve the order to 1. This is as accurate as applying e.g. backward (implicit) Euler to the full problem, without splitting.


Evaluation of security framework for BYOD device in cloud environment

Abstract: The widespread practice of Bring your own device (BYOD) to work significantly raises cybersecurity risks. All organization’s employees and employers will greatly benefit from this trend. The growth of spyware, malware and other dubious downloads on individual devices has compelled the government to review its data security guidelines. Without user knowledge, hazardous apps are downloaded to personal devices. Both people and governments could suffer tragic consequences as a result of this. In this situation, BYODs are problematic since they can alter policies without permission and release private information. The main goal of the research was to detect fraudulent communications coming from BYOD environments. A network monitoring and managing information configuration settings of mobile devices in the network is established by providing policies and authenticating endpoint devices in the BYOD network using a mix of NAC and MDM. This framework deals with Security Manager (SaaS) as the second module is briefly described. The study's early results were positive and suggested that the framework would lessen access control-related issues.


Quantum Molecular Devices

Abstract: Miniaturization has been the driving force in contemporary technologies. However, two main obstacles limit further progress: additional reduction in size has reached its quantum limit, and lithography has reached its threshold. Future progress requires tackling three challenges: chemical synthesis of a complete device, active cooling for exploiting quantum characteristics, and quantum coherent control for operation. Chemical synthesis replaces the current top-bottom approach to manufacturing with bottom-up synthesis from elementary building blocks. New ultracold synthetic methods should be developed. An additional challenge is the active cooling of molecules, where the bottleneck is entropy removal. Notably, the current solution, namely, diffusion, is too slow. A coherent approach offers a possible solution; specifically, quantum coherent control is the method of choice for manipulating ultracold matter. Finally, the many degrees of freedom of molecules should be an asset that allows the design and implementation of complex tasks such as sensing communication and computing.


Blockchain-based deep learning in IoT, healthcare and cryptocurrency price prediction: a comprehensive review

Abstract: Purpose Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages. Design/methodology/approach The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks. Findings By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency. Originality/value There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.


Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning

Abstract: Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.


Exploring semantic consistency in unpaired image translation to generate data for surgical applications

Abstract: In surgical computer vision applications, data privacy and expert annotation challenges impede the acquisition of labeled training data. Unpaired image-to-image translation techniques have been explored to automatically generate annotated datasets by translating synthetic images into a realistic domain. The preservation of structure and semantic consistency, i.e., per-class distribution during translation, poses a significant challenge, particularly in cases of semantic distributional mismatch.This study empirically investigates various translation methods for generating data in surgical applications, explicitly focusing on semantic consistency. Through our analysis, we introduce a novel and simple combination of effective approaches, which we call ConStructS. The defined losses within this approach operate on multiple image patches and spatial resolutions during translation.Various state-of-the-art models were extensively evaluated on two challenging surgical datasets. With two different evaluation schemes, the semantic consistency and the usefulness of the translated images on downstream semantic segmentation tasks were evaluated. The results demonstrate the effectiveness of the ConStructS method in minimizing semantic distortion, with images generated by this model showing superior utility for downstream training.In this study, we tackle semantic inconsistency in unpaired image translation for surgical applications with minimal labeled data. The simple model (ConStructS) enhances consistency during translation and serves as a practical way of generating fully labeled and semantically consistent datasets at minimal cost. Our code is available at https://gitlab.com/nct_tso_public/constructs .


Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods

Abstract: A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods.


Control and Position Tracking for UAVs

Abstract: There has been exponential development of UAV technology and related research areas such as artificial intelligence, which will raise UAVs’ ability for autonomous flights to a higher level [...]


Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Abstract: Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.


Transfer Learning for Computer Vision

Abstract: Computer vision has benefited from deep learning, making it possible to create complex systems. Computer vision has seen radical changes using deep learning techniques, specifically transfer learning for computer vision. It enables computers to understand and interpret visual data, such as images and videos, with great precision. Transfer learning in particular, is a deep learning technique that has transformed several areas of computer vision, including face recognition, semantic segmentation, object detection, and image categorization. This powerful technology is essential in applications such as autonomous vehicles, healthcare, surveillance, and more, since it has improved our capacity to identify, locate, and classify objects in images as well as comprehend complex visual scenes. In computer vision, transfer learning is a method that allows us to use previously taught models to tackle new problems. Benefits include shortened training times and efficient feature extraction. This chapter provides a brief help for implementation of transfer learning for computer vision.


VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT

Abstract: Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also incurs significant computational costs. To tackle these challenges, we propose VTG-GPT, a GPT-based method for zero-shot VTG without training or fine-tuning. To reduce prejudice in the original query, we employ Baichuan2 to generate debiased queries. To lessen redundant information in videos, we apply MiniGPT-v2 to transform visual content into more precise captions. Finally, we devise the proposal generator and post-processing to produce accurate segments from debiased queries and image captions. Extensive experiments demonstrate that VTG-GPT significantly outperforms SOTA methods in zero-shot settings and surpasses unsupervised approaches. More notably, it achieves competitive performance comparable to supervised methods. The code is available on GitHub.


An Introduction to Reinforcement Learning and Its Application in Various Domains

Abstract: Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that focuses on training intelligent agents to learn and adapt through interactions with their environment. This introductory article provides an overview of the fundamental concepts and principles of RL, elucidating its core components, such as the agent, environment, actions, and rewards. This study aims to give readers an in-depth introduction to RL and show examples of its different uses in various domains. RL can allow agents to learn through interaction with an environment, which has led to its enormous interest. The core ideas of RL and its essential elements will be covered in this study, after which it will go into applications in industries including robotics, gaming, finance, healthcare, and more. The fundamental ideas of RL will become clearer to readers, and they will recognize how transformative it can be when used to address challenging decision-making issues. These applications demonstrate the versatility and significance of RL in shaping the future of technology and automation.


Non-Autoregressive Line-Level Code Completion

Abstract: Software developers frequently use code completion tools to accelerate software development by suggesting the following code elements. Researchers usually employ AutoRegressive (AR) decoders to complete code sequences in a left-to-right, token-by-token fashion. To improve the accuracy and efficiency of code completion, we argue that tokens within a code statement have the potential to be predicted concurrently. In this paper, we first conduct an empirical study to analyze the dependency among the target tokens in line-level code completion. The results suggest that it is potentially practical to generate all statement tokens in parallel. To this end, we introduce SANAR, a simple and effective syntax-aware non-autoregressive model for line-level code completion. To further improve the quality of the generated code, we propose an adaptive and syntax-aware sampling strategy to boost the model’s performance. The experimental results obtained from two widely used datasets indicate that our model outperforms state-of-the-art code completion approaches of similar model size by a considerable margin, and is faster than these models with up to 9 × speed-up. Moreover, the extensive results additionally demonstrate that the enhancements achieved by SANAR become even more pronounced with larger model sizes, highlighting their significance.


Comparing Online and Virtual Reality Moral Dilemma Discussions: Focusing on Morality, Perspective-Taking, and Communication Skills

Abstract: inScience Press is pleased to publish the book entitled Psychology Applications & Developments IX as part of the Advances in Psychology and PsychologicalTrends series. These series of books comprise authors’ and editors’ work to address generalized research, focused on specific sections in the Psychology area. In this ninth volume, a committed set of authors explore the Psychology field, therefore contributing to reach the frontiers of knowledge. Success depends on the participation of those who wish to find creative solutions and believe in their potential to change the world, altogether, to increase public engagement andcooperation from communities. Part of our mission is to serve society with these initiatives and promote knowledge. Therefore, it is necessary the strengthening of research efforts in all fields and cooperation between the most assorted studies and backgrounds. In particular, this book explores 4 major areas (divided into 4 sections) within the broad context of Psychology: Clinical Psychology, Educational Psychology, Social Psychology and Cognitive and Experimental Psychology. Each section comprises chapters that have emerged from extended and peer reviewed selected papers originally published in the proceedings of the International Psychological Applications Conference and Trends (InPACT 2023) conference series(http://www.inpact-psychologyconference.org/). This conference occurs annually with successful outcomes, for that reason original papers have been selected and its authors were invited to extend them significantly to once again undergo an evaluation process. Subsequently, the authors of the accepted chapters were requested to make corrections and improve the final submitted chapters. This process has resulted in the final publication of 20 high quality chapters.


Deep Learning Estimating of Epicentral Distance for Earthquake Early Warning Systems

Abstract: ABSTRACT To enhance the performance of earthquake early warning (EEW) systems that aim to issue alerts as quickly as possible, it is crucial to improve the accuracy of the epicentral distance Δ estimated via the single-station method. Although the conventional method estimates Δ from the slope of the initial P-wave envelope, this study applies deep learning techniques that can extract a variety of information from the waveform data. By analyzing ∼20,000 records observed at Kyoshin Network stations in Japan, the convolutional neural network (CNN) method achieved higher accuracy than the conventional method. Increasing the data length or the number of iterations of convolution, activation, and pooling layers in the typical CNN model did not significantly improve the accuracy of Δ estimation. An automatic structure search (AutoSS) technique, in which model structure and hyperparameters are randomly varied, was employed to identify models that yield higher accuracy. A typical CNN model was used as the initial structure. The models obtained through this technique showed improved accuracy with increased data length or computational cost. The models that delivered the highest accuracy among those generated using the AutoSS technique outperformed the typical CNN model in terms of accuracy, although their computational costs were comparable. The AutoSS technique offers a significant advantage in that it allows the selection of a model that matches the computational capabilities of the hardware used for its implementation, thereby ensuring optimal computational efficiency. This exhibits that deep learning technologies can be used to improve the performance of EEW systems.


A Fully Test-Time Training Framework for Semi-Supervised Node Classification on Out-of-Distribution Graphs

Abstract: Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT propose a fully test-time training (FTTT) framework for GNNs to enhance the model’s generalization capabilities for node classification tasks. Specifically, our proposed HomoTTT designs a homophily-based and parameter-free graph contrastive learning task with adaptive augmentation to guide the model’s adaptation during the test time training, allowing the model to adapt for specific target data. In the inference stage, HomoTTT proposes to integrate the original GNN model and the adapted model after TTT using a homophily-based model selection method, which prevents potential performance degradation caused by unconstrained model adaptation. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of our proposed framework. Additionally, the exploratory study further validates the rationality of the homophily-based graph contrastive learning task with adaptive augmentation and the homophily-based model selection designed in HomoTTT .


Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?

Abstract: We observe a recent trend towards applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use would mean. We argue that it is important to reassert the central research focus of the field of information retrieval, because information access is not merely an application to be solved by the so-called ‘AI’ techniques du jour. Rather, it is a key human activity, with impacts on both individuals and society. As information scientists, we should be asking what do people and society want and need from information access systems and how do we design and build systems to meet those needs? With that goal, in this conceptual paper we investigate fundamental questions concerning information access from user and societal viewpoints. We revisit foundational work related to information behavior, information seeking, information retrieval, information filtering, and information access to resurface what we know about these fundamental questions and what may be missing. We then provide our conceptual framing about how we could fill this gap, focusing on methods as well as experimental and evaluation frameworks. We consider the Web as an information ecosystem and explore the ways in which synthetic media, produced by LLMs and otherwise, endangers that ecosystem. The primary goal of this conceptual paper is to shed light on what we still do not know about the potential impacts of LLM-based information access systems, how to advance our understanding of user behaviors, and where the next generations of students, scholars, and developers could fruitfully invest their energies.


Williamson’s Epistemicism and Properties Accounts of Predicates

Abstract: Abstract If the semantic values of predicates are, as Williamson assumes ( Philsophical Perspectives, 13 , 505–517, 1999, 509) properties in the intensional sense, then epistemicism is immediate. Epistemicism fails, so also this properties account of predicates. I deploy examination of Williamson’s account as a foil against properties as semantic values, showing that his two positive arguments for bivalence fail, as do his efforts to rescue epistemicism from obvious problems. In Part II I argue that, despite the properties account’s problems, it has an important role to play in compositional semantics. We may separate the problem of how smallest parts of language get attached to the world from the problem of how those parts compose to form complex semantic values. For the latter problem we idealize and treat the smallest semantic values as properties (and referents). So doing functions to put to one side how the smallest parts get worldly attachment, a problem that would just get in the way of understanding composition. Attachment to the world must be studied separately, and I review some of the options. As a bonus we see why the requirement of higher order vagueness is an artifact of taking properties as semantic values literally instead of as a simplifying idealization.


The role of nudge-based messages on the acceptability and download of COVID-19 contact tracing apps: survey experiments

Abstract: Abstract We investigated the effectiveness of nudge-based messages in promoting the download of contact tracing apps, particularly in the context of low uptake due to serious distrust in the product. Even in the presence of distrust towards the app, promoting downloads can be a beneficial means of preventing infection spread at a stage before pharmacological interventions, such as vaccines or therapeutic drugs, are established. Two studies were conducted with Japanese residents who had not yet downloaded any contact tracing apps. Study 1, based on smartphone location data, targeted 2690 individuals who had gone out despite public instructions to stay at home. Study 2 targeted 4126 individuals whose web-search behavior could be tracked. Nudge-based messages did not increase app downloads in either study. In Study 1, where participants were considered non-cooperative, these messages also did not enhance acceptability such as willingness to accept and intention to download the apps. Conversely, in Study 2, a more representative sample, nudge-based messages emphasizing altruism, economic losses, and medical losses increased app acceptability, although they did not increase searches related to the app's keywords.


A Hyperintensional Logic of Non-prime Evidence

Abstract: Abstract We present a logic of evidence that reduces agents’ epistemic idealisations by combining classical propositional logic with substructural modal logic for formulas in the scope of epistemic modalities. To this aim, we provide a neighborhood semantics of evidence, which provides a modal extension of Fine’s semantics for relevant propositional logic. Possible worlds semantics for classical propositional logic is then obtained by defining the set of possible worlds as a special subset of information states in Fine’s semantics. Finally, we prove that evidence is a hyperintensional and non-prime notion in our logic, and provide a sound and complete axiomatisation of our evidence logic.


An oil and gas pipeline inspection UAV based on improved YOLOv7

Abstract: This study proposes a method of autonomous navigation UAV for oil and gas pipeline (OGP) dial detection based on the improved YOLOv7 model. The canny edge detection algorithm is applied in identifying the edges of the pipeline, and the Hough transform algorithm is used to detect the pipeline in a straight line. The intelligent UAV P600 is guided to patrol the oil and gas dials (OGD) along the pipeline, and the trained improved YOLOv7-based model is adopted to identify the OGD data. Dial recognition is divided into two stages, that is, dial contour detection and dial reading recognition. For the dial recognition rate (RR), the Levenstein distance, a commonly used method, is introduced, thereby calculating the distance between two character sequences. Meanwhile, an integrated global attention mechanism (GAM) is proposed based on the YOLOv7 model, aiming at extracting more informative features. With this mechanism, the channel and spatial aspects of the features are effectively captured, and the importance of cross-dimensional interactions is increased. By introducing GAM attention mechanism in the backbone and head of YOLOv7, the network’s ability in efficiently extracting depth and primary features is enhanced. ACmix (a hybrid model combining the advantages of self-attentiveness and convolution) is also included, with ACmix module improved. The improved ACmix module has the objectives of enhancing feature extraction capability of backbone network and accelerating network convergence. By substituting 3 × 3 convolutional block with 3 × 3 ACmixBlock and adding a jump connection and a 1 × 1 convolutional structure between the ACmixBlock modules, E-ELAN module in YOLOv7 network is also improved, thus optimizing E-ELAN network, enriching features extracted by E-ELAN network, and reducing inference time of YOLOv7 model. As indicated by comparing the experimental results of the six model algorithms (improved YOLOv7, YOLOv7, YOLOX, YOLOv5, YOLOv6 and Faster R-CNN), the improved YOLOv7 model has higher mAP, faster RR, faster network convergence, and higher IOU. In addition, a generic real dataset, called custom dial reading dataset, is presented. With well-defined evaluation protocol, this dataset allows for a fair comparison of various methods in future work.


An Escape Time Formulation for Subgraph Detection and Partitioning of Directed Graphs

Abstract: .We provide a rearrangement based algorithm for detection of subgraphs of k vertices with long escape times for directed or undirected networks that is not combinatorially complex to compute. Complementing other notions of densest subgraphs and graph cuts, our method is based on the mean hitting time required for a random walker to leave a designated set and hit the complement. We provide a new relaxation of this notion of hitting time on a given subgraph and use that relaxation to construct a subgraph detection algorithm that can be computed easily and a generalization to K-partitioning schemes. Using a modification of the subgraph detector on each component, we propose a graph partitioner that identifies regions where random walks live for comparably large times. Importantly, our method implicitly respects the directed nature of the data for directed graphs while also being applicable to undirected graphs. We apply the partitioning method for community detection to a large class of models and real-world data sets.Keywordsescape timepartitioningbang-bangMSC codes68R10


QR codes and automated decision-making in the COVID-19 pandemic

Abstract: In this article, we explore Quick Response (QR) codes (machine-readable optical labels that link to information) and how, after a period of having fallen out of favor, they have been reactivated and have come to underpin COVID-19 automation and contact-tracing efforts. During the pandemic, they were used especially for “safe entry” and other kinds of check-in to locations to facilitate contact tracing. In this context, QR codes facilitate automated decision-making in relation to infectious disease surveillance and disease outbreak control. However, the use of QR codes for contact-tracing purposes has enjoyed mixed success and its implementation has encountered several challenges, as we illustrate through a case study that explores QR codes and COVID contact tracing in Singapore and Australia.


Motion-artifact-augmented pseudo-label network for semi-supervised brain tumor segmentation

Abstract: MRI image segmentation is widely used in clinical practice as a prerequisite and a key for diagnosing brain tumors. The quest for an accurate automated segmentation method for brain tumor images, aiming to ease clinical doctors' workload, has gained significant attention as a research focal point. Despite the success of fully supervised methods in brain tumor segmentation, challenges remain. Due to the high cost involved in annotating medical images, the dataset available for training fully supervised methods is very limited. Additionally, medical images are prone to noise and motion artifacts, negatively impacting quality. In this work, we propose MAPSS, a motion-artifact-augmented pseudo-label network for semi-supervised segmentation. Our method combines motion artifact data augmentation with the pseudo-label semi-supervised training framework. We conduct several experiments under different semi-supervised settings on a publicly available dataset BraTS2020 for brain tumor segmentation. The experimental results show that MAPSS achieves accurate brain tumor segmentation with only a small amount of labeled data and maintains robustness in motion-artifact-influenced images. We also assess the generalization performance of MAPSS using the Left Atrium dataset. Our algorithm is of great significance for assisting doctors in formulating treatment plans and improving treatment quality.


TOMDS (Topic-Oriented Multi-Document Summarization): Enabling Personalized Customization of Multi-Document Summaries

Abstract: In a multi-document summarization task, if the user can decide on the summary topic, the generated summary can better align with the reader’s specific needs and preferences. This paper addresses the issue of overly general content generation by common multi-document summarization models and proposes a topic-oriented multi-document summarization (TOMDS) approach. The method is divided into two stages: extraction and abstraction. During the extractive stage, it primarily identifies and retrieves paragraphs relevant to the designated topic, subsequently sorting them based on their relevance to the topic and forming an initial subset of documents. In the abstractive stage, building upon the transformer architecture, the process includes two parts: encoding and decoding. In the encoding part, we integrated an external discourse parsing module that focuses on both micro-level within-paragraph semantic relationships and macro-level inter-paragraph connections, effectively combining these with the implicit relationships in the source document to produce more enriched semantic features. In the decoding part, we incorporated a topic-aware attention mechanism that dynamically zeroes in on information pertinent to the chosen topic, thus guiding the summary generation process more effectively. The proposed model was primarily evaluated using the standard text summary dataset WikiSum. The experimental results show that our model significantly enhanced the thematic relevance and flexibility of the summaries and improved the accuracy of grammatical and semantic comprehension in the generated summaries.


Supraharmonic Detection Algorithm Based on Interpolation of Self-Convolutional Window All-Phase Compressive Sampling Matching Pursuit

Abstract: With the increase in the use of high-frequency power electronic devices, the harmonics injected into the power grid show a trend of high-frequency development. The continuous rise of the supraharmonic emission level in the distribution network has become one of the power quality problems that needs to be solved urgently in the power grid. In this paper, an algorithm based on the Interpolation of the Self-convolutional Window All-phase Compressive Sampling Matching Pursuit (ISWApCoSaMP) is proposed. Firstly, the self-convolution operation is used for the maximum sidelobe decay (MSD) window, and then the compressed sampling matching pursuit model based on the All-phase is constructed, leading to the All-phase Compressive Sampling Matching Pursuit (ApCoSaMP). Finally, the four-spectrum-line interpolation is combined to utilize spectrum line information to improve the accuracy of signal parameter detection in the frequency domain. The introduced All-phase greatly improves the phase measurement accuracy because the initial phase of the supraharmonic signal is selected for phase estimation. In addition, the self-convolutional window and four-spectrum-line interpolation make full use of the information in the time and frequency domains, thus optimizing the measurement results of amplitude and frequency. The algorithm achieves high accuracy in the measurement results of simulated signals and accurately measures supraharmonics.


Organizational Cybersecurity Systems and Sustainable Business Performance of Small and Medium Enterprises (SMEs) in Saudi Arabia: The Mediating and Moderating Role of Cybersecurity Resilience and Organizational Culture

Abstract: Cybersecurity challenges in Saudi Arabia’s service and manufacturing sectors are escalating due to increased digital adoption, highlighting the need for robust security measures and awareness in SMEs. Therefore, this research is significant due to the increasing reliance on digital technologies and the unique cybersecurity challenges faced by SMEs in these vital economic sectors. With rapid technological advancements, IT capabilities and cybersecurity have become paramount, particularly in the post-COVID-19 era. The service and manufacturing sectors in Saudi Arabia have seen significant shifts towards digital operations. This study aimed to explore the impact of organizational cybersecurity systems on organizational resilience and sustainable business performance in Saudi Arabia’s service and manufacturing sectors, examining the mediating and moderating effects of organizational resilience and culture. A quantitative research method was employed, combining a thorough literature review with empirical data from a sample of 394 respondents in Saudi Arabia, split evenly between the service and manufacturing sectors. Smart PLS 3.3.3 was used to test the proposed hypotheses. The findings suggested a positive effect of the factors of organizational cybersecurity systems on organizational resilience. Organizational cybersecurity systems also significantly influenced sustainable business performance; however, organizational resilience and culture did not play mediating and moderating roles. This study is one of the first to offer a nuanced analysis of IT capabilities and cybersecurity within Saudi Arabia’s service and manufacturing sectors, especially in a post-COVID-19 context. The insights gleaned contribute to the academic discourse and have pivotal managerial implications for organizations navigating the digital era in Saudi Arabia.