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计算机科学研究(英文)

计算机科学研究(英文)

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    国际刊号/ISSN：2630-5151
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计算机科学研究(英文) >2023年第3期

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  • 摘要：2022 The paper seeks to demonstrates the likelihood of embedding a 3D gaze point on a 3D visual field,the visual field is inform of a game console where the user has to play from one level to the other by overcoming obstacles that will lead them to the next level.Complex game interface is sometimes difficult for the player to progress to next level of the game and the developers also find it difficult to regulate the game for an average player.The model serves as an analytical tool for game adaptations and also players can track their response to the game.Custom eye tracking and 3D object tracking algorithms were developed to enhance the analysis of the procedure.This is a part of the contributions to user interface design in the aspect of visual transparency.The development and testing of human computer interaction uses and application is more easily investigated than ever,part of the contribution to this is the embedding of 3-D gaze point on a 3-D visual field.This could be used in a number of applications,for instance in medical applications that includes long and short sightedness diagnosis and treatment.Experiments and Test were conducted on five different episodes of user attributes,result show that fixation points and pupil changes are the two most likely user attributes that contributes most significantly in the performance of the custom eye tracking algorithm the study.As the advancement in development of eye movement algorithm continues user attributes that showed the least likely appearance will prove to be redundant.
  • 摘要：For a tridiagonal two-layer real six-neuron model,the Hopf bifurcation was investigated by studying the eigenvalue equations of the related linear system in the literature.In the present paper,we extend this two-layer real six-neuron network model into a complex-valued delayed network model.Based on the mathematical analysis method,some sufficient conditions to guarantee the existence of periodic oscillatory solutions are established under the assumption that the activation function can be separated into its real and imaginary parts.Our sufficient conditions obtained by the mathe­matical analysis method in this paper are simpler than those obtained by the Hopf bifurcation method.Computer simulation is provided to illustrate the correctness of the theoretical results.
  • 摘要：1.Introduction In cities,the world’s population is rising at an expo­nential rate,and the more people there are,the greater the strain on resources.Even though cities have medical resources and facilities,which are growing by the day,the level of adequacy has not yet been reached,putting an undue strain on government resources.
  • 摘要：Cybersecurity is a global goal that is central to national security planning in many countries.One of the most active research fields is design of practices for the development of so-called highly secure software as a kind of protection and reduction of the risks from cyber threats.The use of a secure software product in a real environment enables the reduction of the vulnerability of the system as a whole.It would be logical to find the most optimal solution for the integration of secure coding in the classic SDLC(software development life cycle).This paper aims to suggest practices and tips that should be followed for secure coding,in order to avoid cost and time overruns because of untimely identification of security issues.It presents the implementation of secure coding practices in software development,and showcases several real-world scenarios from different phases of the SDLC,as well as mitigation strategies.The paper covers techniques for SQL injection mitigation,authentication management for staging environments,and access control verification using JSON Web Tokens.
  • 摘要：At present,Animal Exercise courses rely too much on teachers’subjective ideas in teaching methods and test scores,and there is no set of standards as a benchmark for reference.As a result,students guided by different teachers have an uneven understanding of the Animal Exercise and cannot achieve the expected effect of the course.In this regard,the authors propose a scoring system based on action similarity,which enables teachers to guide students more objectively.The authors created QMonkey,a data set based on the body keys of monkeys in the coco dataset format,which contains 1,428 consecutive images from eight videos.The authors use QMonkey to train a model that recognizes monkey body movements.And the authors propose a new non-standing posture normalization method for motion transfer between monkeys and humans.Finally,the authors utilize motion transfer and structural similarity contrast algorithms to provide a reliable evaluation method for animal exercise courses,eliminating the subjective influence of teachers on scoring and providing experience in the combination of artificial intelligence and drama education.
  • 摘要：This article presents a cardinality approach to big data,a fuzzy logic-based approach to big data,a similarity-based approach to big data,and a logical approach to the marketing strategy of social networking services.All these together constitute a mathematical theory of big data.This article also examines databases with infinite attributes.The research results reveal that relativity and infinity are two characteristics of big data.The relativity of big data is based on the theory of fuzzy sets.The relativity of big data leads to the continuum from small data to big data,big data-driven small data analytics to become statistical significance.The infinity of big data is based on the calculus and cardinality theory.The infinity of big data leads to the infinite similarity of big data.The proposed theory in this article might facilitate the mathematical research and development of big data,big data analytics,big data computing,and data science with applications in intelligent business analytics and business intelligence.
  • 摘要：There are numerous studies about Z-numbers since its inception in 2011.Because Z-number concept reflects human ability to make rational decisions,Z-number based multi-criteria decision making problems are one of these studies.When the problem is translated from linguistic information into Z-number domain,the important question occurs that which Z-number should be selected.To answer this question,several ranking methods have been proposed.To compare the performances of these methods,benchmark set of fuzzy Z-numbers has been created in time.There are relatively new methods that their performances are not examined yet on this benchmark problem.In this paper,we worked on these studies which are relative entropy based Z-number ranking method and a method for ranking discrete Z-numbers.The authors tried to examine their performances on the benchmark problem and compared the results with the other ranking algorithms.The results are consistent with the literature,mostly.The advantages and the drawbacks of the methods are presented which can be useful for the researchers who are interested in this area.
  • 摘要：Wireless sensor networks process and exchange mission-critical data relating to patients’health status.Obviously,any leakages of the sensed data can have serious consequences which can endanger the lives of patients.As such,there is need for strong security and privacy protection of the data in storage as well as the data in transit.Over the recent past,researchers have developed numerous security protocols based on digital signatures,advanced encryption standard,digital certificates and elliptic curve cryptography among other approaches.However,previous studies have shown the existence of many security and privacy gaps that can be exploited by attackers to cause some harm in these networks.In addition,some techniques such as digital certificates have high storage and computation complexities occasioned by certificate and public key management issues.In this paper,a certificateless algorithm is developed for authenticating the body sensors and remote medical server units.Security analysis has shown that it offers data privacy,secure session key agreement,untraceability and anonymity.It can also withstand typical wireless sensor networks attacks such as impersonation,packet replay and man-in-the-middle.On the other hand,it is demonstrated to have the least execution time and bandwidth requirements.
  • 摘要：The growth in wireless technologies applications makes the necessity of providing a reliable communication over wireless networks become obvious.Guaranteeing real time communication in wireless medium poses a significant challenge due to its poor delivery reliability.In this study,a recovery and redundancy model based on sequential time division multiple access(S-TDMA)for wireless communication is developed.The media access control(MAC)layer of the S-TDMA determines which station should transmit at a given time slot based on channel state of the station.Simulations of the system models were carried out using MATLAB SIMULINK software.SIMULINK blocks from the signal processing and communication block sets were used to model the communication system.The S-TDMA performance is evaluated with total link reliability,system throughput,average probability of correct delivery before deadline and system latency.The evaluation results displayed in graphs when compared with instant retry and drop of frame were found to be reliable in recovering loss packets.
  • 摘要：The Internet of Things devices and users exchange massive amount of data.Some of these exchanged messages are highly sensitive as they involve organizational,military or patient personally identifiable information.Therefore,many schemes and protocols have been put forward to protect the transmitted messages.The techniques deployed in these schemes may include blockchain,public key infrastructure,elliptic curve cryptography,physically unclonable function and radio frequency identification.In this paper,a review is provided of these schemes including their strengths and weaknesses.Based on the obtained results,it is clear that majority of these protocols have numerous security,performance and privacy issues.
  • 摘要：The paper presents the distributed control system for rice mill using C#language.The real-time manufacturing system can be implemented by utilizing the signal from the real time control units that is more operative than other old-fashioned control systems in the extent of modern industrial days.The software-based Distributed Control System(DCS)is novel fashionable than any other conventional control systems in the state-ofthe-art manufacturing developments.This research study emphasizes on the implementation of the DCS-based rice mill using visual C#.net.The Industrial Ethernet(IE)is realized between the top level controller for the operator and the controlled station for the remote devices.The model of client-server approach is more appropriate for the automation and manufacturing research purposes.In this study,the computer graphical simulation of the complete control development is depicted in real-time status quo by visual C#language under Visual Studio 2008 software.The parallel ports in the computers of remote terminal level and the master terminal level controllers have been interconnected with port interface coding by visual C#program.
  • 摘要：This article proposes a new approach based on linear programming optimization to solve the problem of determining the color of a complex fractal carpet pattern.The principle is aimed at finding suitable dyes for mixing and their exact concentrations,which,when applied correctly,gives the desired color.The objective function and all constraints of the model are expressed linearly according to the solution variables.Carpet design has become an emerging technological field known for its creativity,science and technology.Many carpet design concepts have been analyzed in terms of color,contrast,brightness,as well as other mathematical concepts such as geometric changes and formulas.These concepts represent a common process in the carpet industry.This article discusses the use of complex fractal images in carpet design and simplex optimization in color selection.
  • 摘要：The coronavirus(nCOV-19),which was discovered,has now spread around the world.However,managing the flow of a large number of cases has proven to be a significant issue for hospitals or healthcare professionals.It is becoming increasingly challenging to speak with a medical expert after the epidemic’s initial wave has passed,particularly in rural areas.Thus,it becomes clear that a Chatbot that is well-designed and implemented can assist patients who are located far away by advocating preventive actions,and viral updates in various cities,and minimising the psychological harm brought on by dread.In this study,a sophisticated Chabot’s design for diagnosing individuals who have been exposed to COVID-19 is presented,along with recommendations for immediate safety measures.Additionally,when symptoms grow serious,this virtual assistant makes contact with specialised medical professionals.
  • 摘要：The entirety of Amazon’s sales being powered by Amazon Search,one of the leading e-commerce platforms around the globe.As a result,even slight boosts in appropriateness can have a major impact on profits as well as the shopping experience of millions of users.Throughout the beginning,Amazon’s product search engine was made up of a number of manually adjusted ranking processes that made use of a limited number of input features.Since that time,a significant amount has transpired.Many people overlook the fact that Amazon is a search engine,and even the biggest one for e-commerce.It is indeed time to begin treating Amazon truly as the top e-commerce search engine across the globe because it currently serves 54%of all product queries.In this paper,the authors have considered two most important Amazon search engine algorithms viz.A10 and A11 and comparative study has been discussed.
  • 摘要：Electrocardiogram(ECG)is a commonly used tool in biological diagnosis of heart diseases.ECG allows the representation of electrical signals which cause heart muscles to contract and relax.Recently,accurate deep learning methods have been developed to overcome manual diagnosis in terms of time and effort.However,most of current automatic medical diagnosis use long electrocardiogram(ECG)signals to inspect different types of heart arrhythmia.Therefore,ECG signal files tend to require large storage to store and may cause significant overhead when exchanged over a computer network.This raises the need to come up with effective compression methods for ECG signals.In this work,the authors investigate using BERT(Bidirectional Encoder Representations from Transformers)model,which is a bidirectional neural network that was originally designed for natural language.The authors evaluate the model with respect to its compression ratio and information preservation,and measure information preservation in terms of the of the accuracy of a convolutional neural network in classifying the decompressed signal.The results show that the method can achieve up to 83%saving in storage.Also,the classification accuracy of the decompressed signals is around 92.41%.Furthermore,the method enables the user to balance the compression ratio and the required accuracy of the CNN classifiers.
  • 摘要：Underwater Wireless Sensor Networks(UWSNs)are becoming increasingly popular in marine applications due to advances in wireless and microelectronics technology.However,UWSNs present challenges in processing,energy,and memory storage due to the use of acoustic waves for communication,which results in long delays,significant power consumption,limited bandwidth,and packet loss.This paper provides a comprehensive review of the latest advancements in UWSNs,including essential services,common platforms,critical elements,and components such as localization algorithms,communication,synchronization,security,mobility,and applications.Despite significant progress,reliable and flexible solutions are needed to meet the evolving requirements of UWSNs.The purpose of this paper is to provide a framework for future research in the field of UWSNs by examining recent advancements,establishing a standard platform and service criteria,using a taxonomy to determine critical elements,and emphasizing important unresolved issues.
  • 摘要：Session-based recommendation aims to predict user preferences based on anonymous behavior sequences.Recent research on session-based recommendation systems has mainly focused on utilizing attention mechanisms on sequential patterns,which has achieved significant results.However,most existing studies only consider individual items in a session and do not extract information from continuous items,which can easily lead to the loss of information on item transition relationships.Therefore,this paper proposes a session-based recommendation algorithm(SGT)based on Gated Recurrent Unit(GRU)and Transformer,which captures user interests by learning continuous items in the current session and utilizes all item transitions on sessions in a more refined way.By combining short-term sessions and long-term behavior,user dynamic preferences are captured.Extensive experiments were conducted on three session-based recommendation datasets,and compared to the baseline methods,both the recall rate Recall@20 and the mean reciprocal rank MRR@20 of the SGT algorithm were improved,demonstrating the effectiveness of the SGT method.
  • 摘要：This paper presents a methodology driven by database constraints for designing and developing(database)software applications.Much needed and with excellent results,this paradigm guarantees the highest possible quality of the managed data.The proposed methodology is illustrated with an easy to understand,yet complex medium-sized genealogy software application driven by more than 200 database constraints,which fully meets such expectations.
  • 摘要：This paper attempts to approach the interface of a robot from the perspective of virtual assistants.Virtual assistants can also be characterized as the mind of a robot,since they manage communication and action with the rest of the world they exist in.Therefore,virtual assistants can also be described as the brain of a robot and they include a Natural Language Processing(NLP)module for conducting communication in their human-robot interface.This work is focused on inquiring and enhancing the capabilities of this module.The problem is that nothing much is revealed about the nature of the human-robot interface of commercial virtual assistants.Therefore,any new attempt of developing such a capability has to start from scratch.Accordingly,to include corresponding capabilities to a developing NLP system of a virtual assistant,a method of systemic semantic modelling is proposed and applied.For this purpose,the paper briefly reviews the evolution of virtual assistants from the first assistant,in the form of a game,to the latest assistant that has significantly elevated their standards.Then there is a reference to the evolution of their services and their continued offerings,as well as future expectations.The paper presents their structure and the technologies used,according to the data provided by the development companies to the public,while an attempt is made to classify virtual assistants,based on their characteristics and capabilities.Consequently,a robotic NLP interface is being developed,based on the communicative power of a proposed systemic conceptual model that may enhance the NLP capabilities of virtual assistants,being tested through a small natural language dictionary in Greek.
  • 摘要：In recent years,a wide variety of fuzzy time series(FTS)forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources.However,the accuracy of a model is problem-specific and varies across data sets.But a model’s precision varies between different data sets and depends on the situation at hand.Even though many models assert that they are better than statistics and a single machine learning-based model,increasing forecasting accuracy is still a challenging task.In the fuzzy time series models,the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities.This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique(GBC)and particle swarm optimization(PSO)for adjusting interval lengths in the universe of discourse(UoD).The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama.For all orders of fuzzy relationships,the suggested model outperforms its counterparts in terms of forecasting accuracy.
  • 摘要：This study compares websites that take live data into account using search engine optimization(SEO).A series of steps called search engine optimization can help a website rank highly in search engine results.Static websites and dynamic websites are two different types of websites.Static websites must have the necessary expertise in programming compatible with SEO.Whereas in dynamic websites,one can utilize readily available plugins/modules.The fundamental issue of all website holders is the lower level of page rank,congestion,utilization,and exposure of the website on the search engine.Here,the authors have studied the live data of four websites as the real-time data would indicate how the SEO strategy may be applied to website page rank,page difficulty removal,and brand query,etc.It is also necessary to choose relevant keywords on any website.The right keyword might assist to increase the brand query while also lowering the page difficulty both on and off the page.In order to calculate Off-page SEO,On-page SEO,and SEO Difficulty,the authors examined live data in this study and chose four well-known Indian university and institute websites for this study:www.caluniv.ac.in,www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in.Using live data and SEO,the authors estimated the Off-page SEO,On-page SEO,and SEO Difficulty.It has been shown that the Off-page SEO of www.caluniv.ac.in is lower than that of www.jnu.ac.in,www.iima.ac.in,and www.iitb.ac.in by 9%,7%,and 7%,respectively.On-page SEO is,in comparison,4%,1%,and 1%more.Every university has continued to keep up its own brand query.Additionally,www.caluniv.ac.in has slightly less SEO Difficulty compared to other websites.The final computed results have been displayed and compared.
  • 摘要：Data analytics of an information system is conducted based on a Markov decision process(MDP)and a partially observable Markov decision process(POMDP)in this paper.Data analytics over a finite planning horizon and an infinite planning horizon for a discounted MDP is performed,respectively.Value iteration(VI),policy iteration(PI),and Q-learning are utilized in the data analytics for a discounted MDP over an infinite planning horizon to evaluate the validity of the MDP model.The optimal policy to minimize the total expected cost of states of the information system is obtained based on the MDP.In the analytics for a discounted POMDP over an infinite planning horizon of the information system,the effects of various parameters on the total expected cost of the information system are studied.
  • 摘要：Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy.In order to accurately predict precipitation,a new precipitation prediction model based on extreme learning machine ensemble(ELME)is proposed.The integrated model is based on the extreme learning machine(ELM)with different kernel functions and supporting parameters,and the submodel with the minimum root mean square error(RMSE)is found to fit the test data.Due to the complex mechanism and factors affecting precipitation change,the data have strong uncertainty and significant nonlinear variation characteristics.The mean generating function(MGF)is used to generate the continuation factor matrix,and the principal component analysis technique is employed to reduce the dimension of the continuation matrix,and the effective data features are extracted.Finally,the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June,July and August,and a comparative experiment is carried out by using ELM,long-term and short-term memory neural network(LSTM)and back propagation neural network based on genetic algorithm(GA-BP).The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models,and it has high stability and reliability,which provides a reliable method for precipitation prediction.
  • 摘要：The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.However,the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring.Consequently,air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity.Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation.In this study,the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm(E-LCDGRA).LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways.The adapted variant,named E-LCDGRA,enhances the LCDGRA algorithm by incorporating a non-beacon-aided CSMA layer-2 un-slotted protocol with a back-off mechanism.The performance of the proposed E-LCDGRA is examined with other classical gathering schemes,including IEESEP and CERP,in terms of average lifetime,energy consumption,and delay.
  • 摘要：Recent advancements in technology have opened up new avenues for educators to facilitate teaching and leverage more learning access in the digital age.As the demand for computational skills continues to grow in preparation for future careers,both teachers and students face the challenge of developing problem-solving,critical thinking,communication,and collaboration skills within an emerging digital landscape.Technology adoption,big data,cloud computing and artificial intelligence pose ongoing challenges for both teachers and students in adapting to the changing workforce development landscape.To tackle these challenges,the paper highlights the importance of exploring the implications of learning sciences in classroom teaching,developing a holistic vision for professional development in education,and understanding the complexities of teacher change.To effectively implement these components,it is crucial to adopt design approaches that prioritize student ownership in education and embrace the principles of inclusive education to reconceptualize the teaching practices in education and technology.
  • 摘要：Humans,as intricate beings driven by a multitude of emotions,possess a remarkable ability to decipher and respond to socio-affective cues.However,many individuals and machines struggle to interpret such nuanced signals,including variations in tone of voice.This paper explores the potential of intelligent technologies to bridge this gap and improve the quality of conversations.In particular,the authors propose a real-time processing method that captures and evaluates emotions in speech,utilizing a terminal device like the Raspberry Pi computer.Furthermore,the authors provide an overview of the current research landscape surrounding speech emotional recognition and delve into our methodology,which involves analyzing audio files from renowned emotional speech databases.To aid incomprehension,the authors present visualizations of these audio files in situ,employing dB-scaled Mel spectrograms generated through TensorFlow and Matplotlib.The authors use a support vector machine kernel and a Convolutional Neural Network with transfer learning to classify emotions.Notably,the classification accuracies achieved are 70% and 77%,respectively,demonstrating the efficacy of our approach when executed on an edge device rather than relying on a server.The system can evaluate pure emotion in speech and provide corresponding visualizations to depict the speaker’s emotional state in less than one second on a Raspberry Pi.These findings pave the way for more effective and emotionally intelligent human-machine interactions in various domains.
  • 摘要：In this study,a machine learning algorithm is proposed to be used in the detection of Obstructive Sleep Apnea(OSA)from the analysis of single-channel ECG recordings.Eighteen ECG recordings from the PhysioNet Apnea-ECG dataset were used in the study.In the feature extraction stage,dynamic time warping and median frequency features were obtained from the coefficients obtained from different frequency bands of the ECG data by using the wavelet transform-based algorithm.In the classification phase,OSA patients and normal ECG recordings were classified using Random Forest(RF)and Long Short-Term Memory(LSTM)classifier algorithms.The performance of the classifiers was evaluated as 90% training and 10%testing.According to this evaluation,the accuracy of the RF classifier was 82.43% and the accuracy of the LSTM classifier was 77.60%.Considering the results obtained,it is thought that it may be possible to use the proposed features and classifier algorithms in OSA classification and maybe a different alternative to existing machine learning methods.The proposed method and the feature set used are promising because they can be implemented effectively thanks to low computing overhead.
  • 摘要：Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.
  • 摘要：Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mo­bility aspects.In addition,much of the previous mobile usability conception was built on desktop computing measure­ments,such as desktop and web application checklists,or scarcely addressed the mobile user interface.Moreover,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and spinners.Therefore,conducting usability evaluation using conventional us­ability measurement would result in irrelevant results.In addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development team.The lack of expertise could lead to unreliable usability evaluations.This paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous work.The framework is dedicated to smartphone apps,which integrate evaluator skills and design concerns.However,there is no evidence of its usefulness in practice.Therefore,the usefulness of the frame­work measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert review.The expert review involved eleven industrial developers and was complemented by a semi-structured interview.The method is replicated in comparison with a framework from another study.The findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.

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