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This With the expansion of information and communication technology, the term ubiquitous computing is a known concept used to describe the current era of interconnected digital systems, which is silently posing a new threat to private information leakage through encryption applications. This work aims to acquaint individuals with the privacy information forms of expression in networking technology and then proposes secure multi-party computation to achieve personal information protection. This work emphasizes that, for the time being, homomorphic computing research approaches privacy in ubiquitous environments without considering the various aspects and fundamental principles of privacy. Finally, this work highlights the need for multidisciplinary research in the area and the importance that homomorphic computing algorithm research receives input from other related disciplines, such as law and psychology. This research aims to contribute to the ongoing discourse on the nature of privacy and its role in ubiquitous environments, providing insights for future research. Although research on privacy in the area of ubiquitous computing expands in many different directions and covers various topics, privacy issues are still open, and it appears that feasible and effective solutions are still quite far from being realized. A critical analysis of this research on privacy in ubiquitous environments reveals a significant shift: up to now, it has been the government's role to provide the framework for privacy protection. Homomorphic encryption is an encryption scheme that allows operations on encrypted data, and it can be applied in any system using various public key algorithms. When data is transferred to the public area, various encryption algorithms are used to secure both the operations and storage of the data. However, to process data located on a remote proxy server while preserving privacy, homomorphic encryption is crucial, especially for network-based operations.

Vision Transformer (ViT) 기반 모델들은 이미지 분류 등 다양한 시각적 인식 작업에서 강력한 성능 을 보이지만, 모든 토큰을 동일하게 활용하는 방식은 불필요한 연산 증가와 중요한 정보 희석 문제를 초래할 수 있다. 특히, Global Average Pooling (GAP)은 모든 토큰을 평균화하여 특징을 요약하는데, 이 과정에서 핵심 정보 손실 위험이 존재한다. 본 논문에서는 GAP 과정에서 중요한 토큰만을 선별적으로 샘플링하여 활용하는 새로운 방법을 제안한다. 이 방법은 추가적인 학습 가능한 파라미터 없이 동작하 며, 연산량을 줄이면서도 효과적인 특징 요약이 가능함을 실험을 통해 확인하였다.

컴퓨터 게임은 컴퓨터 기술을 통해 놀이를 새로운 형태의 텍스트로 전환시킨다. 놀이가 가지는 인간학적 층위가 대상에 대한 상상적 전유 라면, 컴퓨터 기술은 이 상상적 전유의 계기를 생산하고, 동시에 상상 적 전유를 지탱할 수 있는 요소를 생산한다. 컴퓨터 게임에서 발생하 는‘게임 행위’는 그런 의미에서 인간의 상상적 전유와 이 전유를 가능 케 하는 인공적 경험들 사이에서 이뤄진다. 그리고 이를 통해 우리는 ‘놀이-이미지’라는 새로운 이미지 형태를 생산, 경험하게 된다.

Computer game transforms play into the new text form using computer technology. Humanistic layer of play means the imaginative possession of the object. Computer technology in computer game produces the motive of the imaginative possession and the elements to support the imaginative possession.‘ Game-action’ in computer game occurs between the imaginative possession and the artificial experience. With‘game-acition’, we produce and experience the‘play-image’as new image form.

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win?win solution for cost-effective and energy-saving communication and computation services in various environments.

Similarity searching is a basic issue in information processing because of the large size of formal contexts and their complicated derivation operators. Recently, some researchers have focused on knowledge reduction methods by using granular computing. In this process, suitable information granules are vital to characterizing the quantities of attributes and objects. To address this problem, a novel approach to obtain an entropy-weighted concept lattice with inclusion degree and similarity distance (ECLisd) has been proposed. The approach aims to compute the combined weights by merging the inclusion degree and entropy degree between two concepts. In addition, another method is utilized to measure the hierarchical distance by considering the different degrees of importance of each attribute. Finally, the rationality of the ECLisd is validated via a comparative analysis.

Classical cryptography with complex computations has recently been utilized in the latest computing systems to create secret keys. However, systems can be breached by fast-measuring methods of the secret key; this approach does not offer adequate protection when depending on the computational complexity alone. The laws of physics for communication purposes are used in quantum computing, enabling new computing concepts to be introduced, particularly in cryptography and key distribution. This paper proposes a quantum computing lattice (CQL) mechanism that applies the BB84 protocol to generate a quantum key. The generated key and a one-time pad encryption method are used to encrypt the message. Then Babai's algorithm is applied to the ciphertext to find the closet vector problem within the lattice. As a result, quantum computing concepts are used with classical encryption methods to find the closet vector problem in a lattice, providing strength encryption to generate the key. The proposed approach is demonstrated a high calculation speed when using quantum computing.

The burgeoning distribution of smartphone web applications based on various mobile environments is increasingly focusing on the performance of mobile applications implemented by JavaScript and HTML5 (Hyper Text Markup Language 5). If application software has a simple functional processing structure, then the problem is benign. However, browser loads are becoming more burdensome as the amount of JavaScript processing continues to increase. Processing time and capacity of the JavaScript in current mobile browsers are limited. As a solution, the Web Worker is designed to implement multi-threading. However, it cannot guarantee the computing ability as a native application on mobile devices, and is not sufficient to improve processing speed. The method proposed in this research overcomes the limitation of resources as a mobile client and guarantees performance by native application software by providing high computing service. It shifts the JavaScript process of a mobile device on to a cloud-based computer server. A performance evaluation experiment revealed the proposed algorithm to be up to 6 times faster in computing speed compared to the existing mobile browser's JavaScript process, and 3 to 6 times faster than Web Worker. In addition, memory usage was also less than the existing technology.

Future internet aims to function as a neutral in-network storage and computation platform, essential for enabling 6G and beyond wireless use cases. Information-Centric Networking and Edge Computing are key paradigms driving this vision by offering diversified services with fast response times across heterogeneous networks. This approach requires effective coordination to dynamically utilize resources like links, storage, and computation in near real-time within a non-homogenous and distributed computing environment. Additionally, networks must be aware of resource availability and reputational information to manage unknown and partially observed dynamic systems, ensuring the desired Quality of Experience (QoE). This paper provides a comprehensive evaluation of edge computing technologies, starting with an introduction to its architectural frameworks. We examine contemporary research on essential aspects such as resource allocation, computation delegation, data administration, and network management, highlighting existing research gaps. Furthermore, we explore the synergy between edge computing and 5G, and discuss advancements in 6G that enhance solutions through edge computing. Our study emphasizes the importance of integrating edge computing in future considerations, particularly regarding sustainable energy and standards.

Closed Circuit Television (CCTV) based Surveillance has become the fundamental part of the security Systems. In most cases, surveillance feeds are only used as evidence. The emergence of Edge Computing gives hope for enabling real time surveillance systems that focuses on prevention of crimes. The proposed architecture consists of a Convolutional Neural Network (CNN) enabled in an edge device, with reduced computational complexity, which classifies various actions like Pulling, pushing and other hand movements and locates the identified activities in the image frame using bounding boxes. The proposed architecture gives an alert whenever a suspicious activity is detected. The system was found efficient when validated against the Dataset taken from the SRM IST Campus.

In this work, we study how to minimize the average system delay in a cloud computing center with heterogeneous servers, where each server may have a different average service rate. We consider M?Mi?C?K with a single server threshold and double congestion thresholds. The analytical models and performance measures are derived for the systems considered. The effect of the average arrival rate on performance measures is studied. It is shown that M?Mi?C?K with a single server threshold and double congestion thresholds outperforms M?Mi?C?K in terms of the average system delay. Finally, a computer simulation is written to verify the accuracy of the analytical results.

The purpose of this study is to seek new u-business services in restaurant management. Using the concept of business model methodology in family restaurant management domain, this study identifies customers' needs in services at the stage of management of purchase of materials, the production management, and the sales management. In addition, this study suggests two killer applications of a family restaurant management linking with the latest ubiquitous computing technologies: the service of the customer-oriented menu recommendation and the service of the inventory-oriented menu recommendation. These findings may offer practical insights in the context of ubiquitous service model of restaurant management.

This paper proposes high-speed computing decoders for polar codes based on a neural network. To compensate for the performance gap with the successive cancellation decoder, we propose applying the recurrent neural network to the BP decoder with a modified factor graph to reduce the computational complexity of the decoder without any performance degradation. The results of the performance simulation conducted in this paper reveal that the proposed decoder requires substantially less computational complexity than the conventional decoders to achieve the same bit error rate performance.

More and more cloud computing services are being applied in various fields; however, it is difficult for users and cloud computing service platforms to establish trust among each other. The trust value cannot be measured accurately or effectively. To solve this problem, we design a service-oriented cloud trust assessment model using a cloud model. We also design a subjective preference weight allocation (SPWA) algorithm. A flexible weight model is advanced by combining SPWA with the entropy method. Aiming at the fuzziness and subjectivity of trust, the cloud model is used to measure the trust value of various cloud computing services. The SPWA algorithm is used to integrate each evaluation result to obtain the trust evaluation value of the entire cloud service provider.

Cloud computing is the concept of providing information technology services on the Internet, such as software, hardware, networking, and storage. These services can be accessed anywhere at any time on a pay-per-use basis. However, storing data on servers is a challenging aspect of cloud computing. This paper utilizes cryptography and access control to ensure the confidentiality, integrity, and proper control of access to sensitive data. We propose a model that can protect data in cloud computing. Our model is designed by using an enhanced RSA encryption algorithm and a combination of role-based access control model with extensible access control markup language (XACML) to facilitate security and allow data access. This paper proposes a model that uses cryptography concepts to store data in cloud computing and allows data access through the access control model with minimum time and cost for encryption and decryption.

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

Due to the resource-constraint nature and lack of lightweight computing solutions for diagnostic devices in healthcare IoT, provisioning time-critical responses is still challenging. In this paper, we propose DC-Health, a Dew Computing enabled IoT healthcare solution for offline and ultra-low latency decisions. The proposed solution connects a large number of healthcare devices and provisions user-specific services even when Internet connectivity is not available. The computation module is placed at the extreme edge rather than the cloud to reduce the complexity and to improve the user-specific services. In addition to the other computing facilities provided by the cloud, fog, and edge, our solution performs with a negligible dependency on the Internet.We develop a prototype of DC-Health, which monitors the heart condition using the ECG sensors with end-mile services, flexibility in terms of user-control, and mobility feature. The experimental implementations show that the proposed architecture minimizes the network response time by at least 92% and 98%, compared to the fog and cloud-based approaches, respectively. Along with this, the proposed technique also reduces the CPU and memory usages, and response time by around 30% compared to the conventional method.

Cloud computing, also known as "country as you go", is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders' number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software's profitability. Our axis of research is the load balancing between a data center's virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels ('odd levels' or 'even levels') in ascending order based on the meta-heuristic "Bat-algorithm". The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.

Cryptosystems have received very much attention in recent years as importance of information security is increased. Most of Cryptosystems are defined over finite or Galois fields GF($2^m$) . In particular, the finite field GF($2^m$) is mainly used in public-key cryptosystems. These cryptosystems are constructed over finite field arithmetics, such as addition, subtraction, multiplication, and multiplicative inversion defined over GF($2^m$) . Hence, to implement these cryptosystems efficiently, it is important to carry out these operations defined over GF($2^m$) fast. Among these operations, since multiplicative inversion is much more time-consuming than other operations, it has become the object of lots of investigation. Recently, many methods for computing multiplicative inverses at hi호 speed has been proposed. These methods are based on format's theorem, and reduce the number of required multiplication using normal bases over GF($2^m$) . The method proposed by Itoh and Tsujii[2] among these methods reduced the required number of times of multiplication to O( log m) Also, some methods which improved the Itoh and Tsujii's method were proposed, but these methods have some problems such as complicated decomposition processes. In practical applications, m is frequently selected as a power of 2. In this parer, we propose a fast method for computing multiplicative inverses in GF($2^m$) , where m = ($2^n$) . Our method requires fewer ultiplications than the Itoh and Tsujii's method, and the decomposition process is simpler than other proposed methods.

Conventional split computing approaches for AI models that generate large outputs suffer from long transmission and inference times. Due to the limited resources of the edge server and selfish MDs, some MDs cannot offload their tasks and sacrifice their performance. To address these issues, we formulate an optimization problem to determine one or two split points that minimize inference latency while ensuring fair offloading among MDs. Additionally, we devise a low-complexity heuristic algorithm called fast and fair split computing (F2SC). Evaluation results demonstrate that F2SC reduces inference time by 3.8%~20.1% compared to the conventional approaches while maintaining fairness.