bluuebunny/crossref_metadata_2025 huggingface dataset:
Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
Authors: Abro Ghulam , Zulkifli Saiful , Masood Rana , Asirvadam Vijanth , Laouiti Anis
Published: October 2022

It has been observed that unmanned aerial vehicles (UAVs), also known as drones, have been used in a very different way over time. The advancements in key UAV areas include detection (including radio frequency and radar), classification (including micro, mini, close range, short range, medium range, medium-range endurance, low-altitude deep penetration, low-altitude long endurance, and medium-altitude long endurance), tracking (including lateral tracking, vertical tracking, moving aerial pan with moving target, and moving aerial tilt with moving target), and so forth. Even with all of these improvements and advantages, security and privacy can still be ensured by researching a number of key aspects of an unmanned aerial vehicle, such as through the jamming of the control signals of a UAV and redirecting them for any high-assault activity. This review article will examine the privacy issues related to drone standards and regulations. The manuscript will also provide a comprehensive answer to these limitations. In addition to updated information on current legislation and the many classes that can be used to establish communication between a ground control room and an unmanned aerial vehicle, this article provides a basic overview of unmanned aerial vehicles. After reading this review, readers will understand the shortcomings, the most recent advancements, and the strategies for addressing security issues, assaults, and limitations. The open research areas described in this manuscript can be utilized to create novel methods for strengthening the security and privacy of an unmanned aerial vehicle.
Minimal Surfaces as an Innovative Solution for the Design of an Additive Manufactured Solar-Powered Unmanned Aerial Vehicle (UAV)
Authors: García-Gascón César , Castelló-Pedrero Pablo , García-Manrique Juan Antonio
Published: October 2022

This paper aims to describe the methodology used in the design and manufacture of a fixed-wing aircraft manufactured using additive techniques together with the implementation of technology based on solar panels. The main objective is increasing the autonomy and range of the UAV’s autonomous missions. Moreover, one of the main targets is to improve the capabilities of the aeronautical industry towards sustainable aircrafts and to acquire better mechanical properties owing to the use of additive technologies and new printing materials. Further, a lower environmental impact could be achieved through the use of renewable energies. Material extrusion (MEX) technology may be able to be used for the manufacture of stronger and lighter parts by using gyroids as the filling of the printed material. The paper proposes the use of minimal surfaces for the reinforcement of the UAV aircraft wings. This type of surface was never used because it is not possible to manufacture it using conventional techniques. The rapid growth of additive technologies led to many expectations for new design methodologies in the aeronautical industry. In this study, mechanical tests were carried out on specimens manufactured with different geometries to address the design and manufacture of a UAV as a demonstrator. In addition, to carry out the manufacture of the prototype, a 3D printer with a movable bench similar to a belt, that allows for the manufacture of parts without limitations in the Z axis, was tested. The parts manufactured with this technique can be structurally improved, and it is possible to avoid manufacturing multiple prints of small parts of the aircraft that will have to be glued later, decreasing the mechanical properties of the UAV. The conceptual design and manufacturing of a solar aircraft, SolarÍO, using additive technologies, is presented. A study of the most innovative 3D printers was carried out that allowed for the manufacture of parts with an infinite Z-axis and, in addition, a filler based on minimal surfaces (gyroids) was applied, which considerably increased the mechanical properties of the printed parts. Finally, it can be stated that in this article, the potential of the additive manufacturing as a new manufacturing process for small aircrafts and for the aeronautical sector in the future when new materials and more efficient additive manufacturing processes are already developed is demonstrated.
Flying Washer: Development of High-Pressure Washing Aerial Robot Employing Multirotor Platform with Add-On Thrusters
Authors: Miyazaki Ryo , Paul Hannibal , Kominami Takamasa , Martinez Ricardo Rosales , Shimonomura Kazuhiro
Published: October 2022

In this study, we propose a multirotor aerial robot for high-pressure washing tasks at high altitudes. The aerial robot consists of a multirotor platform, an add-on planar translational driving system (ATD), a visual sensing system, and a high-pressure washing system. The ATD consists of three ducted fans, which can generate force in all directions on the horizontal plane. The ATD also allows the multirotor to suppress the reaction force generated by the nozzle of a high-pressure washing system and inject water accurately. In this study, we propose a method to precisely inject water by installing an ATD in the multirotor and using its driving force to suppress the reaction force and move the multirotor while keeping its posture horizontal. The semi-autonomous system was designed to allow the operator to maneuver the multirotor while maintaining a constant distance from the wall by the sensor feedback with onboard LiDAR or stereo camera. In the experiment, we succeeded in performing the high-pressure washing task in a real environment and verified that the reaction force generated from the nozzle was actually suppressed during the task.
Obstacle Avoidance-Based Autonomous Navigation of a Quadrotor System
Authors: Alanezi Mohammed A. , Haruna Zaharuddeen , Sha’aban Yusuf A. , Bouchekara Houssem R. E. H. , Nahas Mouaaz , Shahriar Mohammad S.
Published: October 2022

Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller records a settling time of 0.43 s, an overshoot of 2.50%, and a zero steady-state error. The implementation of the obstacle avoidance scheme shows that the distance threshold of 1 m is sufficient for the autonomous navigation of the quadrotor. Hence, the developed method is suitable for managing livestock with the average size of an adult sheep.
A Novel Method of Small Object Detection in UAV Remote Sensing Images Based on Feature Alignment of Candidate Regions
Authors: Wang Jinkang , Shao Faming , He Xiaohui , Lu Guanlin
Published: October 2022

To solve the problem of low detection accuracy of small objects in UAV optical remote sensing images due to low contrast, dense distribution, and weak features, this paper proposes a small object detection method based on feature alignment of candidate regions is proposed for remote sensing images. Firstly, AFA-FPN (Attention-based Feature Alignment FPN) defines the corresponding relationship between feature mappings, solves the misregistration of features between adjacent levels, and improves the recognition ability of small objects by aligning and fusing shallow spatial features and deep semantic features. Secondly, the PHDA (Polarization Hybrid Domain Attention) module captures local areas containing small object features through parallel channel domain attention and spatial domain attention. It assigns a larger weight to these areas to alleviate the interference of background noise. Then, the rotation branch uses RROI to rotate the horizontal frame obtained by RPN, which avoids missing detection of small objects with dense distribution and arbitrary direction. Next, the rotation branch uses RROI to rotate the horizontal box obtained by RPN. It solves the problem of missing detection of small objects with dense distribution and arbitrary direction and prevents feature mismatch between the object and candidate regions. Finally, the loss function is improved to better reflect the difference between the predicted value and the ground truth. Experiments are conducted on a self-made dataset. The experimental results show that the mAP of the proposed method reaches 82.04% and the detection speed reaches 24.3 FPS, which is significantly higher than that of the state-of-the-art methods. Meanwhile, the ablation experiment verifies the rationality of each module.
Quantum Misuse Attack on Frodo
Authors: Wang Yaru , Jiang Haodong , Ma Zhi
Published: October 2022

Research on the security of lattice-based public-key encryption schemes against misuse attacks is an important part of the cryptographic assessment of the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process. In particular, many NIST-PQC cryptosystems follow the same meta-cryptosystem. At EUROCRYPT 2019, Ba˘etu et al. mounted a classical key recovery under plaintext checking attacks (KR-PCA) and a quantum key recovery under chosen ciphertext attacks (KR-CCA). They analyzed the security of the weak version of nine submissions to NIST. In this paper, we focus on learning with error (LWE)-based FrodoPKE, whose IND-CPA security is tightly related to the hardness of plain LWE problems. We first review the meta-cryptosystem and quantum algorithm for solving quantum LWE problems. Then, we consider the case where the noise follows a discrete Gaussian distribution and recompute the success probability for quantum LWE by using Hoeffding bound. Finally, we give a quantum key recovery algorithm based on LWE under CCA attack and analyze the security of Frodo. Compared with the existing work of Ba˘etu et al., our method reduces the number of queries from 22 to 1 with the same success probability.
On the Bayesian Mixture of Generalized Linear Models with Gamma-Distributed Responses
Authors: Susanto Irwan , Iriawan Nur , Kuswanto Heri
Published: October 2022

This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed responses. The simulation-based inferential processes related to the Bayesian-MCMC method is carried out using the Gibbs sampler algorithm. The performance of proposed model is conducted through two real data applications on the gross domestic product per capita at purchasing power parity and the annual household income per capita. Graphical posterior predictive checks are carried out to verify the adequacy of the fitted model for the observed data. The predictive accuracy of this model is compared with other Bayesian models using the widely applicable information criterion (WAIC). We find that the Bayesian mixture of GLMs with gamma-distributed responses performs properly when the appropriate prior distributions are applied and has better predictive accuracy than the Bayesian mixture of linear regression model and the Bayesian gamma regression model.
Empirical Analysis on Public Expenditure for Education, Human Capital and Economic Growth: Evidence from Honduras
Authors: Villela Roldán , Paredes Juan Jacobo
Published: October 2022

The objective of this study is to evaluate the relationship between public expenditure for education and human capital on economic growth in Honduras from 1990 to 2020, using the instrumental variables (IV) method, which incorporates the components of public spending on education and human capital, in addition to a set of control variables. The time series were extracted from the World Bank online databases. The results show that there is no correlation between public expenditure for education and economic growth; they also suggest that human capital is not contributing to economic growth, confirming that human capital accumulation is not fully developing. Finally, of the set of control variables considered key by the literature and on which social and economic development depends to a large extent, these would be preventing sustained economic growth, so the government and the population have enormous challenges to overcome.
A Comparative Analysis of Vulnerability to Poverty between Urban and Rural Households in China
Authors: Ding Shuo
Published: October 2022

This study proposes a subjective poverty line for each household to quantify the vulnerability to poverty in urban and rural households by considering residents’ expectations and their propensity to compare their perceived welfare level with those of other community members. The findings show that the overall vulnerability incidence in urban households is lower than in rural households. The regional differential in terms of vulnerability to poverty continues to exist, but the western province in both urban and rural households has not shown a significantly higher vulnerability rate than in other regions. Educational qualification is a determinant of the vulnerability of rural residents, whereas it does not have remarkable positive effects on urban households. Meanwhile, the impacts of welfare systems upon both urban and rural households are larger than expected, while the coverage of them is incomplete and calls for government to implement more social reforms in order to mitigate the risk and buffer the vulnerability, and to adopt a more equalising approach (instead of unrestrained growth).
Analysis of the Perceptions Shared by Young People about the Relevance and Versatility of Religion in Culturally Diverse Contexts
Authors: García-Segura Sonia , Martínez-Carmona María-José , Gil-Pino Carmen
Published: October 2022

This paper analyses the relevance of religion for adolescents in the realm of peer relationships, both within and outside of educational institutions. The sample consisted of 385 young individuals attending different Secondary Education institutions located in a Spanish province characterized by its cultural diversity. The tool used to collect the information was the REDCo questionnaire. The results of the data analysis indicate that young people confer a certain relevance to religion on an internal and personal level, and that they attribute a historical value to it as a discipline. They also relate the versatility of religion to changing processes regarding beliefs and belonging to a religious community. Therefore, dialogue becomes the key tool for social cohesion in multicultural societies beyond mere tolerance, creating spaces for mutual transformation and generating a symmetrical relationship between the “Self” and the “Other”.
Assessing Students’ Ability to Apply the Control of Variables Strategy When Engaged with Inquiry-Based Worksheets during the COVID Era
Authors: Petridou Eleni , Molohidis Anastasios , Hatzikraniotis Euripides
Published: October 2022

In this study, an inquiry-based sequence was designed, developed and implemented using facets of flipped classroom and aspects of inquiry learning using the ADDIE model. The sequence intends to promote the students’ scientific literacy. The aim of this paper is to examine the effectiveness of specially designed inquiry-based online worksheets (e-WS) to actively engage students in the design of unconfounded experiments by applying the Control of Variables Strategy (CVS). Second-year senior high school (11th grade) students participated in an intervention in which facets of flipped classroom approach with asynchronous and synchronous distance learning sessions were adopted, during the COVID-19 lockdown. Results show that the flipped classroom approach with synchronous and asynchronous sessions was acceptable and adoptable by the students; the existence of probe questions in e-WS, combined with explicit reference to inquiry procedure enhanced students’ awareness of scientific practice and on CVS, while at the end of the intervention, students were capable of applying the CVS in the design of unconfounded experiments.
Pilot Research into the Perceived Importance of Educational Elements and an Application for Detecting Progress through the Perspective of Practice
Authors: Berková Kateřina , Frendlovská Dagmar , Chalupová Martina , Kubišová Andrea , Hrmo Roman , Krelová Katarína Krpálková
Published: October 2022

Data analysis and the development of learning skills based on monitoring students’ progress are aspects in demand by schools and students. Quite a lot of studies deal with Learning Analytics Dashboards. There is a limited number of studies that take into account the supply of such tools on the market. In this pilot study, the researchers present findings on the attitudes of 19 higher education institutions from the Czech Republic, Belgium, Germany, Greece, the Netherlands and Poland, along with 14 secondary schools from the Czech Republic, towards the proposed web-based application for supporting learning and providing automated feedback on student progress in accounting education. The aim of this section was to find out how schools perceive the importance of the proposed application and its specific parameters. The study also presents the current product offer on the Czech market and the interest among 112 companies in developing such an application. The findings revealed that there is no such tool offered on the Czech market, and the majority of the analyzed companies are interested in its development. The schools evaluated the learning tool as being most important in the area of distance learning, and most useful for the visualization of accounting methods based mainly on imagination. The value of such an application is seen in supporting self-study, providing information on attitudes and current abilities, and tracking students’ learning progress.
Students’ Experiences with Online Teaching and Learning in Norway: A Qualitative Study into Nutrition Education One Year after the COVID-19 Lockdown
Authors: Tørris Christine , Gjølstad Eli , Morseth Marianne , Debesay Jonas , Almendingen Kari
Published: October 2022

The COVID-19 pandemic forced a rapid shift to emergency remote teaching due to the lockdown in March 2020. Following up on a mixed-methods study on emergency teaching among students in public health nutrition (PHN) at the beginning of the pandemic, the aim of this study was to explore the students’ experiences with digital teaching one year after the first lockdown. We especially want to shed light on factors that promoted or inhibited the online learning environment one year into the pandemic. Ten in-depth interviews were conducted (Zoom) among third year bachelor’s and first and second year master’s students in PHN. Using thematic analysis, four main themes emerged from the data: (1) ‘increased participation in digital teaching’, (2) ‘reduced learning quality’, (3) ‘motivation and social contact’, and (4) ‘important factors in digital teaching’. The students identified several advantages of digital teaching. More students attended class due to its accessibility, flexibility, and efficiency; however, the lack of social contact and collaboration with peers were challenging. Despite these challenges, active learning technologies, such as student response systems (SRS), shared documents, and the use of cameras, were important factors for successful digital teaching. The results indicate that active learning improves students’ digital learning environment.
Sustainable Development and Soils in the Portuguese Education System: Open Problems and Further Challenges
Authors: João Patrícia , Henriques Maria Helena , Rodrigues Ana V.
Published: October 2022

The curricular documents for Basic Education Sciences in Portuguese schools (ages 6 to 15) that currently affect teachers and students across the world integrate Sustainable Development concerns regarding soils. A sharp gap emerges between the official documents and the teachers’ perceptions of how to approach such issues. Urgent measures are required to bridge both worlds: reformulations of the official documents that guide teachers’ practices and educational resources focused on soils from a Sustainable Development perspective. A new organization of the curricular documents for this theme that aims to promote quality education among Portuguese Basic Education schools worldwide is proposed.
Teaching How to Research: A Case Study on Chemical and Industrial Engineering Degrees
Authors: Nogales-Delgado Sergio , Encinar Martín José María , Román Suero Silvia
Published: October 2022

Final Degree Projects (FDPs) in scientific and technical studies are often the first significant approach to real research and can be a challenging step for students. Their own experience at this stage can influence the students’ professional calling to a research career instead of other technical occupations. In any case, it should be an interesting experience for students, regardless of their future career. Thus, suitable guiding by academic tutors is vital to favor the engagement for scientific research as a feasible professional career in youngsters. The aim of this work was to point out our own experience when it comes to teaching how to research, inspired by research-based learning. In other words, this is an example about how to introduce final degree students to research, to prepare them for a possible future career in the research world. Specifically, the expectation for each FDP was to materialize their work in the publication of a research paper or a conference communication, in the context of research carried out in the frame of a transference project with a firm purpose: to emphasize how their work will be applied as a solution to a real problem. To achieve this goal, a general didactic guide (which should be customized for each student) is presented, adapted to a chemical engineering laboratory, mainly devoted to biodiesel and biolubricant production.
Visioning Indigenous Futures: Centering Sovereignty and Relationality in Belonging
Authors: Bill Denise , Minthorn Robin Zape-tah-hol-ah , Montgomery Michelle
Published: October 2022

In this article, we share how a Tribal–University partnership fosters and centers sovereignty and relationality in creating a Tribally based doctoral cohort. This is the first tribal–university partnership that offers a doctoral program on tribal lands. We have embedded a collective approach to support Indigenous students since its inception. We have approached belonging through an Indigenous way of being. This has included building relationality in how we offer opening and closing each quarter for students to share their tribal communities’ ways. We have built in connections through offering monthly writing time with an Indigenous scholar to support their development, growth, and contributions. During their second year, we built into their community-grounded praxis an opportunity to honor Indigenous knowledges where they earned an Indigenous Knowledge and Community Grounded Certificate. As we move into their last year of their doctoral program, we center belongingness in how we support them through their dissertation process through a co-chair model of Indigenous faculty supporting and integrating opportunities to connect holistically. This storying is a model of how to build authentic partnerships that center place and belonging in intentional ways.
Predicting Learners’ Agility and Readiness for Future Learning Ecosystem
Authors: Ab Jalil Habibah , Ismail Ismi Arif , Ma’rof Aini Marina , Lim Chee Leong , Hassan Nurhanim , Che Nawi Nur Raihan
Published: October 2022

Agility and future readiness are fundamental 21st-century skills that could guide university students globally to thriving and benefiting from a VUCA—volatile, uncertain, complex, and ambiguous—world. The ability to respond flexibly, make informed decisions, and adapt to rapid change reflects future-readiness capabilities. However, little is known about the empirical role of the university curriculum, learning ecosystem, and learning experience as perceived by university students in developing these skills. Therefore, we analysed data collected from 209 Malaysian university students from 16 universities to assess whether these three pertinent factors impact the students’ learning agility and determine how well learning agility predicts learners’ future readiness. The present study empirically assessed a theoretical model using a partial least squares structural equation modelling (PLS-SEM) approach. The analysis supported all the hypotheses proposed in this study, which implies that the extended model could effectively predict learners’ agility and future readiness. The results revealed that the university learning experience, ecosystem, and curriculum positively, directly, and significantly affected learning agility and future readiness. Furthermore, the findings showed that student agility significantly mediated the relationships between the student learning experience, university learning ecosystem, and curriculum and student future readiness. Taken together, these results highlight the importance of a future-ready education nurtured by a vibrant learning ecosystem that delivers lasting values and experiences for students and communities to appreciate the opportunities for a challenging yet exciting future offered by a VUCA environment. The established empirical model describing the empirical interplays between these correlates could, in turn, aid better evidence-based policy making in higher education.
Assessment of COVID-19’s Impact on EdTech: Case Study on an Educational Platform, Architecture and Teachers’ Experience
Authors: Lara Nieto-Márquez Natalia , Baldominos Alejandro , Iglesias Soilán Manuel , Martín Dobón Elisa , Zuluaga Arévalo J. Alexandra
Published: October 2022

The education sector has been confronted with different challenges due to the situation caused by the pandemic, when families were asked to be confined at home as well as return when schools were opened. This lockdown situation presented both a challenge for the EdTech sector and for teachers and families. Consequently, this study analyzes the importance of online methodologies, usage of an educational resource example, and the impact of the lockdown. Thus, these objectives are assessed from different perspectives such as users’ consumption, technical challenges of cloud architecture and experience from teachers who have used the platform during the lockdown. In this way, to understand the challenges of Cloud architecture, the structure of the Pre-COVID-19 platform and the changes implemented to adapt to the new needs are described. An increase in schools’ subscriptions was observed when home lockdown was decreed, the differences in usage with the return to the classroom are also discussed. The research methodology entailed an assessment instrument developed for teachers. Teachers highlight the contents of Smile and Learn platform and their motivational characteristics for the students’ learning. The assessment points out the limitations that many teachers face while using these resources.
Perception of Vulnerability and Ruminant Thoughts about COVID-19 in Spanish Students
Authors: Figueredo José-María , García-Ael Cristina , Topa Gabriela
Published: October 2022

The current situation in schools in relation to COVID-19 can generate a decrease in academic performance due to factors intrinsic to students. Therefore, rumination about COVID-19 could interfere with students’ attention, resulting in a decrease in their academic performance. Therefore, the objective of this study was to explore the relationship between the perception of vulnerability to the disease and rumination about COVID-19 from a cross-sectional sample of post-compulsory education students. The differences in the perception of vulnerability to disease and rumination in different groups were analyzed, separated by gender. Our data suggest a positive relationship between the perception of vulnerability to the disease and ruminants’ thoughts about COVID-19 (r = 0.29). Gender differences are significant, with women having higher scores than men in both variables.
Introduction to the Special Issue on Recent Advances in Mathematics Education
Authors: Voskoglou Michael Gr. , Mamona-Downs Joanna
Published: October 2022

The Special Issue with the title “Recent Advances in Mathematics Education” presents contemporary research outcomes regarding the teaching and learning of Mathematics at all levels of Education [...]
Framing Network Flow for Anomaly Detection Using Image Recognition and Federated Learning
Authors: Toldinas Jevgenijus , Venčkauskas Algimantas , Liutkevičius Agnius , Morkevičius Nerijus
Published: October 2022

The intrusion detection system (IDS) must be able to handle the increase in attack volume, increasing Internet traffic, and accelerating detection speeds. Network flow feature (NTF) records are the input of flow-based IDSs that are used to determine whether network traffic is normal or malicious in order to avoid IDS from difficult and time-consuming packet content inspection processing since only flow records are examined. To reduce computational power and training time, this paper proposes a novel pre-processing method merging a specific amount of NTF records into frames, and frame transformation into images. Federated learning (FL) enables multiple users to share the learned models while maintaining the privacy of their training data. This research suggests federated transfer learning and federated learning methods for NIDS employing deep learning for image classification and conducting tests on the BOUN DDoS dataset to address the issue of training data privacy. Our experimental results indicate that the proposed Federated transfer learning (FTL) and FL methods for training do not require data centralization and preserve participant data privacy while achieving acceptable accuracy in DDoS attack identification: FTL (92.99%) and FL (88.42%) in comparison with Traditional transfer learning (93.95%).
Optimal Selection and Integration of Batteries and Renewable Generators in DC Distribution Systems through a Mixed-Integer Convex Formulation
Authors: Basto-Gil Jerson , Maldonado-Cardenas Angel , Montoya Oscar
Published: October 2022

The problem concerning the optimal placement and sizing of renewable energy resources and battery energy storage systems in electrical DC distribution networks is addressed in this research by proposing a new mathematical formulation. The exact mixed-integer nonlinear programming (MINLP) model is transformed into a mixed-integer convex model using McCormick envelopes regarding the product between two positive variables. Convex theory allows ensuring that the global optimum is found due to the linear equivalent structure of the solution space and the quadratic structure of the objective function when all the binary variables are defined. Numerical results in the 21-bus system demonstrate the effectiveness and robustness of the proposed solution methodology when compared to the solution reached by solving the exact MINLP model. Numerical results showed that the simultaneous allocation of batteries and renewable energy resources allows for the best improvements in the daily operating costs, i.e., about 53.29% with respect to the benchmark case of the 21-bus grid, followed by the scenario where the renewable energy resources are reallocated while considering a fixed location for the batteries, with an improvement of 43.33%. In addition, the main result is that the difference between the exact modeling and the proposed formulation regarding the final objective function was less than 3.90% for all the simulation cases, which demonstrated the effectiveness of the proposed approach for operating distributed energy resources in monopolar DC networks.
swAFL: A Library of High-Performance Activation Function for the Sunway Architecture
Authors: Xu Jinchen , Li Fei , Hou Ming , Wang Panjie
Published: October 2022

The Sunway supercomputers have recently attracted considerable attention to execute neural networks. Meanwhile, activation functions help extend the applicability of neural networks to nonlinear models by introducing nonlinear factors. Despite the numerous activation function-supported AI frameworks, only PyTorch and TensorFlow were ported to the Sunway platforms. Although these libraries can meet the minimum functional requirements to deploy a neural network on the Sunway machines, there still exist some drawbacks including the limited number of usable functions and unsatisfactory performances remaining unresolved. Therefore, two activation function algorithms with different computing accuracies were developed in this study, and an efficient implementation scheme was designed using the single instruction/multiple data extension and multiply–add instructions of the platform. Finally, an efficient library-swAFL-composed of 48 function interfaces was designed and implemented on the Sunway platforms. Experimental results indicate that swAFL outperformed PyTorch and TensorFlow by 19.5 and 23 times, respectively, on average.
PDF Malware Detection Based on Optimizable Decision Trees
Authors: Abu Al-Haija Qasem , Odeh Ammar , Qattous Hazem
Published: October 2022

Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead.
A High Flexible Shift Transformation Unit Design Approach for Coarse-Grained Reconfigurable Cryptographic Arrays
Authors: Qu Tongzhou , Dai Zibin , Liu Yanjiang , Chen Lin
Published: October 2022

Shift transformations are the fundamental operation of cryptographic algorithms, and the arithmetic unit implementing different types of shift transformations are utilized in the coarse-grain reconfigurable cryptographic architectures (CGRCA) to meet the different cryptographic algorithms. In this paper, a reconfigurable shift transformation unit (RSTU) is proposed to meet the complicated shift requirement of CGRCA, which achieves high flexibility and a good cost–performance ratio. The mathematical properties of shift transformation are analyzed, and several theorems are introduced to design a reconfigurable shifter. Furthermore, the reconfigurable data path of the proposed unit is presented to implement the random combination of shift operations in different granularity, and configuration word and routing algorithms are proposed to generate control information for RSTU. Moreover, the control information generation module is designed to invert the configuration word into the control information, according to the routing algorithms. As a proof-of-concept, the proposed RSTU is built using the CMOS 65 nm technology. The experimental results show that RSTU supports more shift operations, increases 18.2% speed at most, and reduces 13% area occupation, compared to the existing shifters.
A Comprehensive Study on Healthcare Datasets Using AI Techniques
Authors: Mistry Sunit , Wang Lili , Islam Yousuf , Osei Frimpong Atta Junior
Published: October 2022

Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain to similar entities across several datasets in public healthcare. Based on the methods proposed in this study, all sources are allocated using AI-based approaches to consider non-unique features and calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, and linkage processes. Accurate measurements develop methods for manually evaluating and validating matched pairs to purify connecting parameters and boost the process efficacy. This study aims to assess and raise the standard of healthcare datasets that aid doctors’ comprehension of patients’ physical characteristics by using NARX to detect errors and machine learning models for the decision-making process. Consequently, our findings on the mortality rate of patients with COVID-19 revealed a gender bias: female 15.91% and male 22.73%. We also found a gender bias with mild symptoms such as shortness of breath: female 31.82% and male 32.87%. With congestive heart disease symptoms, the bias was as follows: female 5.07% and male 7.58%. Finally, with typical symptoms, the overall mortality rate for both males and females was 13.2%.
Semantically Annotated Cooking Procedures for an Intelligent Kitchen Environment
Authors: Kondylakis George , Galanakis George , Partarakis Nikolaos , Zabulis Xenophon
Published: October 2022

Food preparation is one of the essential tasks in daily life and involves a large number of physical interactions between hands, utensils, ingredients, etc. The fundamental unit in the food preparation activity is the concept of a recipe. The recipe describes the cooking process—the way to make a dish in a sequential order of cooking steps. Frequently, following these steps can be an extremely complicated process, which requires coordination, monitoring and execution of multiple tasks simultaneously. This work introduces a cooking assistance system powered by Computer Vision techniques that provide the user with guidance in the accomplishment of a cooking activity in terms of a recipe and its correct execution. The system can provide the user with guidance for carrying out a recipe through the appropriate messages, which appear in a panel specifically designed for the user. Throughout the process, the system can validate the correctness of each step by (a) detection and motion estimation of the ingredients and utensils in the scene and (b) spatial arrangement of them in terms of where each one is located to another. The system was first evaluated on individual algorithmic steps and on the end-to-end execution of two recipes with promising results.
Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms
Authors: Aldhyani Theyazn H. H. , Alzahrani Ali
Published: October 2022

The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.
Internet of Things-Based Intelligent Attendance System: Framework, Practice Implementation, and Application
Authors: Nguyen Van Dung , Khoa Huynh Van , Kieu Tam Nguyen , Huh Eui-Nam
Published: October 2022

Tracking coronavirus patients and determining their close contacts (as part of COVID-19 mapping) have been huge challenges. In universities, in particular, there are many students and large gatherings who are at a higher risk of obtaining COVID-19. Many smart attendance management systems have been proposed that are based on RFID and fingerprint sensor modules, facial recognition, etc. However, these techniques operate with specific requirements, such as GPUs and large memories/datasets, or by combining recognizance and thermal cameras. To solve these issues and reduce costs, we designed an Internet of Things (IoT)-based intelligent attendance management system. In this paper, we compare the advantages/disadvantages of existing smart attendance management systems. We designed an IoT-based intelligent attendance management system based on the cloud, a web server, Google API, a non-contact body temperature sensor, and the Raspberry Pi 4 module (4G). We conducted a survey at a university and summarized the satisfaction levels of using our system.
G-Band Broad Bandwidth Extended Interaction Klystron with Traveling-Wave Output Structure
Authors: Xu Xiaotao , Li Hengliang , Yuan Xuesong , Chen Qingyun , Zu Yifan , Li Hailong , Yin Yong , Yan Yang
Published: October 2022

In this paper, we investigate a large-sized beam tunnel, G-band extended interaction klystron (EIK) with a traveling wave output structure for the development of broad bandwidth EIKs. The high-quality factor F was introduced to estimate the bandwidth characteristics of the cluster cavities, and the optimal cluster cavity structure parameters were obtained based on this factor. The simulation mode of the device was designed by the 3D particle-in-cell (PIC) commercial simulation software. Four cluster cavities with a staggered distribution of frequencies were employed to expand the bunching bandwidth, and two traveling wave modes, 2π−π/10 and 2π−2π/10, were used as the operating modes in the output structure, effectively increasing the output bandwidth. The simulation findings show that the maximum output power is 170 W, the corresponding gain is 37.5 dB, and the 3-dB bandwidth is up to 1.25 GHz. The three-hole coupling structure with a large-sized beam tunnel provides convenience for the fabrication of devices in the G-band, and our study shows a potential method for the realization of a G-band broadband EIK.
Ensemble Self-Paced Learning Based on Adaptive Mixture Weighting
Authors: Liu Liwen , Wang Zhong , Bai Jianbin , Yang Xiangfeng , Yang Yunchuan , Zhou Jianbo
Published: October 2022

Self-paced learning (SPL) is a learning mechanism inspired by human and animal learning processes that gives variable weights to samples, gradually introducing simple to more complicated samples into the learning set as the “age” parameter increases. To regulate the learning process, a self-paced weighting regularization term with an “age” parameter is introduced to the learning function. Several self-paced weighting methods have been proposed, and different regularization terms might result in varied learning performance. However, on the one hand, it is difficult to select a suitable weighting method for SPL. On the other hand, it is challenging to determine the “age” parameter, and it is easy for SPL to obtain poor results as the “age” of the model increases. To solve the aforementioned difficulties, an ensemble SPL approach with an adaptive mixture weighting mechanism is proposed in this study. First, as the “age” parameter increases, a set of base classifiers is collected to produce a new data set, which is used to learn the second-level classifier. Then, the ensemble model is used to generate the final output to avoid the selection of the optimal “age” parameter. An adaptive mixture weighting method is designed to reduce the dependence of parameters on human experience. The previous methods find it difficult to determine the “age” parameters or self-paced parameters. In this paper, these parameters can be adjusted adaptively during the learning process. In comparison with the previous SPL techniques, the proposed method achieves the best results in 27 of the 32 datasets in the experiments with the adaptive parameters. The statistical tests are carried out to show that the proposed method is superior to other state-of-the-art algorithms.
Chaos-Based Cryptography: Text Encryption Using Image Algorithms
Authors: Lawnik Marcin , Moysis Lazaros , Volos Christos
Published: October 2022

Currently, chaotic cryptography is mainly concentrated on image encryption. Once-popular stream-encryption algorithms, e.g., textual data, are now rarely considered. This article studies how chaotic image-encryption algorithms can be used to encrypt text. The proposed approach to this problem consists of two stages: in the first stage, the text message is intended for encryption into an image; in the second step, the selected image-encryption algorithm is used. An example illustrates the efficiency of this method. In addition, the article presents measures used in image-encryption analysis that confirm the security of the obtained cipher-image, such as entropy (value close to 8), correlation of adjacent pixels (values close to 0), or measures related to differential cryptanalysis. The proposed method offers a new look at text encryption using chaos, by applying image-encryption algorithms already known from the literature.