The integrated “perception‐memory” system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform‐boosted system that enables the sensing, recognition, and memory for human‐computer interaction is reported by the combination of ultra‐thin Ag/Al/Paster‐based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)‐based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative “perception‐memory” integrated system not only serves the disabled, enhancing human‐computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.
The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding
The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data Fabrication/Falsification Statement
The author(s) declare that no data has been fabricated, falsified, or manipulated in this study.
Participant Consent
The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained.
Copyright and Licensing
For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
Alejandro Espinosa, Xavier Samos, Daniel Ulied
et al.
Abstract As the usage of the edge-cloud continuum increases, Kubernetes presents itself as a solution that allows easy control and deployment of applications in these highly-distributed and heterogeneous environments. In this context, Artificial Intelligence methods have been proposed to aid in the task allocation process to optimize different aspects of the system, such as application execution time, load balancing or energy consumption. In this paper, we present a comparative study focused on optimizing energy consumption through dynamic task allocation in a realistic V2X application scenario. We evaluate and compare two methods representing the most common algorithmic families for resource allocation: Particle Swarm Optimization (PSO) and Proximal Policy Optimization (PPO). Our methodology includes the design of a custom Kubernetes Operator to enforce the models’ node recommendations, allowing for rigorous, real-world validation against the base Kubernetes scheduler. Experiments demonstrate that while both PSO and PPO models successfully reduce energy consumption, PSO delivers the highest savings, reducing energy use by up to 20%. Crucially, our study highlights a key trade-off: although PSO is performance-superior for energy, the PPO model remains a faster and more computationally lightweight option that can be used widely on any kind of device, even with limited resources.
This volume contains papers accepted by the 2025 4th International Conference on Theoretical Physics, Computers and Electronic Engineering (TPCEE 2025), which was held at Toronto, Canada during October 25-26, 2025. This conference brought together innovative scholars and industry experts to jointly hold a forum. The main objective of the conference is to promote the research and development activities of theoretical physics, astrophysics, quantum physics, computer engineering, information technology, and electronic engineering. The other objective is to promote the exchange of scientific information among researchers, developers, engineers, students, and practitioners around the world. Over 50 participants from many countries attended this conference, which included 4 keynote speeches and 20 oral presentations on different aspects in 4 sections. The cutting-edge research works were presented by such renowned keynote speakers. The TPCEE 2025 was a success where all the participants gathered on this platform and share experiences and research findings in their respective fields. Organizing Committees of TPCEE 2025 Toronto, Canada
Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami
et al.
Abstract This study introduces a novel encoder–decoder framework based on deep neural networks and provides a thorough investigation into the field of automatic picture captioning systems. The suggested model uses a “long short‐term memory” decoder for word prediction and sentence construction, and a “convolutional neural network” as an encoder that is skilled at object recognition and spatial information retention. The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. For both training and testing, the study uses a variety of photos from open‐access datasets, such as Flickr8k, Flickr30k, and MS COCO. The Python platform is used for implementation, with Keras and TensorFlow as backends. The experimental findings, which were assessed using the “bilingual evaluation understudy” metric, demonstrate the effectiveness of the suggested methodology in automatically captioning images. By addressing spatial relationships in images and producing logical, contextually relevant captions, the paper advances image captioning technology. Insightful ideas for future study directions are generated by the discussion of the difficulties faced during the experimentation phase. By establishing a strong neural network architecture for automatic picture captioning, this study creates opportunities for future advancement and improvement in the area.
This volume contains papers accepted by the 2024 3rd International Conference on Theoretical Physics, Computers and Electronic Engineering (TPCEE 2024), which was held at Toronto, Canada during October 19-20, 2024. TPCEE 2024 brought together innovative scholars and industry experts to jointly hold a forum. The main objective of the conference is to promote the research and development activities of theoretical physics, astrophysics, quantum physics, computer engineering, information technology, and electronic engineering. The other objective is to promote the exchange of scientific information among researchers, developers, engineers, students, and practitioners around the world. Over 70 participants from many countries attended this conference, which included 4 keynote speeches and 24 oral presentations on different aspects in 4 sections. The cutting-edge research works were presented by such renowned keynote speakers. The TPCEE 2024 was a success where all the participants gathered on this platform and share experiences and research findings in their respective fields. Organizing Committees of TPCEE 2024 Toronto, Canada
Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.
Qijie Ruan, Mengyu Li, Wan Anita Binti Wan Abas
et al.
Abstract How human-computer interaction affects customer electronic service quality satisfaction is an important issue that electronic service-based companies need to consider. This paper constructs a conceptual model based on service dominant logic theory and task technology fit theory. It conducts a questionnaire survey of 559 Taobao platform users to verify the proposed hypothesis. The results show that human-computer interaction comprises three basic factors (technology functionality, task routineness, and technology readiness) and two core factors (interaction between task routineness and technology functionality, and interaction between technology readiness and technology functionality). This research enriches the theoretical basis of electronic service quality. It promotes the development of task technology fit theory in service science, providing guidance for electronic service-based companies to improve service interfaces and enhance electronic service quality.
To address the difficulty faced by network security operation and maintenance personnel in timely and accurate identification of required data during network security event analysis, a recommendation algorithm based on a knowledge graph for network security events was proposed.The algorithm utilized the network threat framework ATT＆amp;CK to construct an ontology model and establish a network threat knowledge graph based on this model.It extracted relevant security data such as attack techniques, vulnerabilities, and defense measures into interconnected security knowledge within the knowledge graph.Entity data was extracted based on the knowledge graph, and entity vectors were obtained using the TransH algorithm.These entity vectors were then used to calculate data similarity between entities in network threat data.Disposal behaviors were extracted from literature on network security event handling and treated as network security data entities.A disposal behavior matrix was constructed, and the behavior matrix enabled the vector representation of network threat data.The similarity of network threat data entities was calculated based on disposal behaviors.Finally, the similarity between network threat data and threat data under network security event handling behavior was fused to generate a data recommendation list for network security events, which established correlations between network threat domains based on user behavior.Experimental results demonstrate that the algorithm performs optimally when the fusion weight α=7 and the recommended data volume K=5, achieving a recall rate of 62.37% and an accuracy rate of 68.23%.By incorporating disposition behavior similarity in addition to data similarity, the algorithm better represents factual disposition behavior.Compared to other algorithms, this algorithm exhibits significant advantages in recall rate and accuracy, particularly when the recommended data volume is less than 10.
Crop yield forecasting has been well studied in recent decades and is significant in protecting food security. Crop growth is a complex phenomenon that depends on various factors. Machine learning and deep learning trends have emerged as important innovations in this field. We propose to utilize crop, weather, and soil data from agricultural datasets to evaluate yield prediction behavior. Paddy being a staple food crop in India is chosen for this research. In this paper, we propose hybrid architecture for paddy yield prediction, namely, MLR-LSTM, which combines Multiple Linear Regression and Long Short-Term Memory to utilize their complementary nature. The results are compared with traditional machine learning methods such as Support vector machine, Long short-term memory and Random forest method. Evaluation metrics such as Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), F1 score, Recall, and Precision are used to evaluate the hybrid method and traditional models. The results obtained from the proposed hybrid method indicates that the hybrid model delivers better R2, RMSE, MAE, MSE values of 0.93, 0.1549, 0.199, and 0.024 respectively.
Dependence on the internet and computer programs demonstrates the significance of computer programs in our day-to-day lives. Such demands motivate malware developers to create more malware, both in terms of quantity and variety. Researchers are constantly faced with hurdles while attempting to protect themselves from potential hazards and risks due to malware authors’ usage of code obfuscation techniques. Metamorphic and polymorphic variations are easily able to elude the widely utilized signature-based detection procedures. Researchers are more interested in deep learning approaches than machine learning techniques to analyze the behavior of such a vast number of virus variants. Researchers have been drawn to the categorization of malware within itself in addition to the classification of malware against benign programs to examine the behavioral differences between them. In order to investigate the relationship between the application programming interface (API) calls throughout API sequences and classify them, this work uses the one-dimensional convolutional neural network (1D-CNN) model to solve a multiclass classification problem. On API sequences, feature vectors for distinctive APIs are created using the Word2Vec word embedding approach and the skip-gram model. The one-vs.-rest approach is used to train 1D-CNN models to categorize malware, and all of them are then combined with a suggested ModifiedSoftVoting algorithm to improve classification. On the open benchmark dataset Mal-API-2019, the suggested ensembled 1D-CNN architecture captures improved evaluation scores with an accuracy of 0.90, a weighted average F1-score of 0.90, and an AUC score of more than 0.96 for all classes of malware.
Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many tasks such as machine translation, information extraction, and semantic parsing. In this work, a new approach for WSD is proposed which uses a neural network as a surrogate fitness function in a metaheuristic algorithm. Also, a new method for simultaneous training of word and sense embeddings is proposed in this work. Accordingly, the node2vec algorithm is employed on the WordNet graph to generate sequences containing both words and senses. These sequences are then used along with paragraphs from Wikipedia in the word2vec algorithm to generate embeddings for words and senses at the same time. In order to address data imbalance in this task, sense probability distribution data extracted from the training corpus is used in the search process of the proposed simulated annealing algorithm. Furthermore, we introduce a new approach for clustering and mapping senses in the WordNet graph, which considerably improves the accuracy of the proposed method. In this approach, nodes in the WordNet graph are clustered on the condition that no two senses of the same word be present in one cluster. Then, repeatedly, all nodes in each cluster are mapped to a randomly selected node from that cluster, meaning that the representative node can take advantage of the training instances of all the other nodes in the cluster. Training the proposed method in this work is done using the SemCor dataset and the SemEval-2015 dataset has been used as the validation set. The final evaluation of the system is performed on SensEval-2, SensEval-3, SemEval-2007, SemEval-2013, SemEval-2015, and the concatenation of all five mentioned datasets. The performance of the system is also evaluated on the four content word categories, namely, nouns, verbs, adjectives, and adverbs. Experimental results show that the proposed method achieves accuracies in the range of 74.8 to 84.6 percent in the ten aforementioned evaluation categories which are close to and in some cases better than the state of the art in this task.
Abstract The evolving intercloud enables idle resources to be traded among cloud providers to facilitate utilization optimization and to improve the cost-effectiveness of the service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, in which cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, in this paper a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process is proposed. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can improve the negotiation performance and the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and demonstrates the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.
Abstract Studying the wagon accumulation process and the laws of accumulation cost is of great significance for determining the suitable conditions of wagon accumulation and shortening the accumulation time. Here, the process of relaxed fixed time accumulation is first taken as a stochastic service system, and derives the theoretical formula for the accumulation cost. Then based on actual data of wagon flows, a simulation model is built to analyse the influence of parameters in the theoretical formula such as the coordination of the traffic diagram and the accumulation process, the sizes and intervals of the arriving wagon groups and the minimum number of wagons. Finally, through comparing with the accumulation cost of fixed train length accumulation mode and considering the benefit of changing the minimum number of wagons in train sets, the optimal minimum number of wagons in the relaxed fixed time accumulation mode under different wagon flow intensities is determined.
This study investigated the influence of computer self-efficacy, computer anxiety, and cognitive skills on the use of electronic library resources by social science undergraduates in a tertiary institution in Nigeria. Survey research design was adopted and stratified random sampling technique was used to select 869 sample size from a population of 1452 social science undergraduates across five departments. A total of 793 questionnaire was properly filled and collated which equals a response rate of 91.3% from the population sample. Findings from the study revealed that there were significant relationships among computer selfefficacy, computer anxiety, cognitive skills, and use of electronic library resources by the respondents. Computer self-efficacy, computer anxiety, and cognitive skills individually and jointly had a significant influence on the use of electronic library resources of the respondents. Therefore, library management in the tertiary institution should give due consideration to computer self-efficacy, computer anxiety, and cognitive skills of the respondents when planning to enhance their use of electronic library resources among others.
Eleftherios Tzagkarakis, Haridimos Kondylakis, George Vardakis
et al.
Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indifferent. Public administration is evolving and taking on a new form through e-government. A large number of organizations have set up websites, establishing an online interface with the citizens and businesses with which it interacts. However, most organizations, especially the decentralized agencies of the ministries and local authorities, do not offer their information electronically despite the fact that they provide many information services that are not integrated with other e-government services. Besides, these services are mainly focused on serving citizens and businesses and less on providing services to employees. In this paper, we describe the process of developing an ontology to support the administrative procedures of decentralized government organizations. Finally, we describe the development of an e-government portal that provides employees services that are processed online, using the above ontology for modeling and data management.
ABSTRACT Background and context Promoting open-ended projects presents new opportunities and challenges for inclusive teaching in CS classrooms. While efforts have been made to develop inclusive curricula, little research has focused on ways teachers apply curricula in their classrooms to promote inclusion. Objective To understand the challenges faced in facilitating an open-ended unit and the pedagogical strategies enacted to address those challenges, we analyze the self-reported teaching practices that experienced teachers developed in their implementation of a constructionist electronic textiles unit in Exploring Computer Science. Method We inductively analyzed and coded 17 experienced teachers’ weekly surveys and post-interviews. Findings Teachers leveraged local resources and contexts to adapt classroom activities as well as developed new perspectives on computing as ways to foster inclusivity. Implications We propose further opportunities for CS teachers to consciously reflect and draw upon the assets and funds of knowledge of their students’ communities when facilitating open-ended, inclusive activities.