Vol. 43 · No. 5

The invention of new technologies and the upgrading of existing ones to carry out assigned duties for human requirements efficiently and quickly are among the new prospects made possible by the evolution of artificial intelligence and information technology. This study aims to evaluate the performance of the Internet of Things solutions based on fog and edge computing. This article examines fog computing architectures and discusses the primary potential security and trust issues. The topical issue is to analyze and improve the security of data transmission from electronic devices and media in fog clouds using data encryption and encryption algorithms. The study confirms that depending on the amount of information to be processed, each block encryption algorithm can have a different key length. The study investigates the Advanced Encryption Standard (AES) algorithm as a more typical and efficient block encryption algorithm with key lengths of 128 and 256 bits. The paper contains a comparative characteristic of data processing speed with the size of 512 MB on the example of the AES and PRESENT block encryption algorithms, demonstrating an effective software implementation. The main scientific novelty of the study is that the efficiency of such coding algorithms as AES and PRESENT increases with a decrease in their size (from 256 to 128-bit and from 128 to 80-bit, respectively). It is necessary to expand our results for a more complex residential system.

With the growing demand for various IoT applications, fog nodes frequently become overloaded. Fog computing requires effective load balancing to maximize resource utilization. It is essential to determine the load on host to obtain workload consolidation. Various random parameters, including CPU utilization, the number of CPU cores, RAM, memory allocated, memory available, disk I/O, and network I/O are employed to better comprehend host workloads. In the proposed work, the host's load status is detected using an ensemble approach into three categories: under-loaded, balanced and overloaded. Further, the proposed work considers three case studies and varying numbers of virtual machines (VMs) are executed with various parameter combinations. In each case study, a different number of VMs are executed in parallel on two different platforms. In the proposed study, we predicted the load on multiple hosts by employing a variety of advanced machine-learning models. To construct an ensemble model, we selected models with higher accuracy based on retrieved performance evaluation criteria. The ensemble method is applied to deal with the worst-case scenario of the model prediction. For a selected number of case studies, the Random Forest model, Ada Boost Classifier, Gradient Boost and Decision Tree models perform better than other models. These state-of-the-art predictive models are outperformed by our proposed ensemble model and achieves an improved accuracy of nearly 82

The thriving Internet of Things, a paradigm shift from the traditional Internet has brought about great societal improvements such as smart homes, smart cities, smart health, intelligent systems and many more. With these diverse societal improvements come increasing complexities in the areas of system efficiency, privacy, and security. In recent years ample academic and industrial research have delved into resource optimization to the detriment of security, as security features are left to be bolted on at the end of design and developmental processes. This approach leaves the system susceptible to threats and attacks. Consequently, this paper seeks to incorporate security features from the onset, weaving the security feature into the system's design and developmental phase. The proposed model structured in a three-tiered design comprises of concepts of Blockchain, edge computing, clustering techniques and a hybrid algorithm consisting of the static round-robin and the dynamic resource-based algorithms. The composition of the structural layout which considers aspects of the blockchain as a security tight measure for resource optimization in Internet of Things' environment, also incorporates features of edge computing, clustering techniques and the hybrid algorithm as components for resource optimization of the Internet of Things. In addition to the prospective security feature provided by the Hyperledger fabric BC in the proposed model, simulation results illustrate the Hyperledger fabric BC's dexterity in making IoT systems even more efficient, further showing its efficacy over the PSOR2B and the BC-EDSSP.

Deep convolutional networks have been widely applied in image denoising tasks with great success. However, many denoising models extract more feature information by increasing the network depth, which does not fully utilize the shallow features, but also makes it difficult to obtain accurate noise information. In this paper, we introduce a novel modified U-Net structure-based boosted dense attention neural network (BDANet) specifically designed for image denoising. The convolutional block within the encoding layer of BDANet incorporates dense connections and residuals, effectively circumventing the vanishing gradient issue through feature reuse and local residual learning. A boosting strategy is employed in the decoding layer to augment residual information in the noise map. To adeptly process edge details in images, BDANet deploys a polarized self-attentive mechanism to direct the densely connected blocks for depth feature extraction. The network is trained with Gaussian noise at random noise levels in the study to make it flexible to handle images with a wide range of noise levels. In experimental comparisons involving additive Gaussian noise, BDANet outperformed conventional denoising networks and attained competitive results relative to state-of-the-art image denoising networks, with an average improvement of approximately 1.03 dB in terms of PSNR values. Visualization results show that the image after denoising by BDANet network is sharper and richer in texture details than other methods.

Multivariate time series data can be collected and employed in various fields to predict future data. However, owing to significant uncertainty and noise, controlling the prediction accuracy during practical applications remains challenging. Therefore, this study examines the Bayesian information criterion (BIC) as an evaluation metric for prediction models and analyzes its changes by varying the explanatory variables, variable pairs, and learning and validation periods. Descriptive statistics and decision tree-based algorithms, such as classification and regression tree, random forest, and dynamic time warping, were employed in the analysis. The experimental evaluations were conducted using two types of restaurant data: sales, weather, number of customers, number of views on gourmet site, and day of the week. Based on the experimental results, we compared and discussed the learning behavior based on various explanatory variable combinations. We discovered that 1. the explanatory variable, the number of customers, exhibited a significantly different trend from other variables when dynamic time warping was applied, particularly in combination with other variables, and 2. variables with seasonality yielded the best performance when used independently; otherwise, the predictive accuracy decreased according to the decision tree results. This comparative investigation revealed that the proposed BIC analysis method proposed can be used to effectively identify the optimal combination of explanatory variables for multivariate time series data that exhibit characteristics such as seasonality.

Program dependence net (PDNet) is a kind of Petri nets which can represent concurrent systems and software to apply the automata-theoretic approach for software model checking on Linear Temporal Logic (LTL). This paper presents a formal modelling method to construct a PDNet which is consistent with the behavior of multi-threaded C programs (PThread programs) from a source code. For concurrent programs with a function call and POSIX threads, we propose the formal operational semantics by the labeled transition system (LTS). We formalize the statements by the basic PDNet structure based on LTS operational semantics. Then, we propose the formal modelling method to build a basic flow to simulate the execution of PThread programs. Finally, we give a case study to illustrate the formal modelling method for verifying PThread programs on LTL properties.

Automatic Text Summarization (ATS) is a fundamental aspect of Natural Language Processing (NLP) that allows for the conversion of lengthy text documents into concise summaries that retain the essential information based on specific criteria. In this paper, we present a literature review on the topic of ATS, which includes an overview of the various approaches to ATS, categorized by the mechanisms they use to generate a summary. By organizing these approaches based on their underlying mechanisms, we provide a comprehensive understanding of the current state-of-the-art in ATS systems.

Gaining unauthorized remote access to an environment is generally done either by exploiting a vulnerable service, or application that is internet-based; or by tricking a user into executing malicious codes. The former one is typically more simple since there is no need for any user interaction. The latter one, however, requires much more effort on the attackers' side since they must find a way to incite the victim into opening a malicious document and interacting with an HTML page in a web browser. In this paper, we will focus on the latter technique which falls into the social engineering category, as it will involve the use of a phishing attack. The reason for this selection is based on the fact that it is challenging to correct user behavior. Thus, it increases the attackers' chance of performing a successful attack, contrary to the former technique, where a simple patch, upgrade, or update can prevent the adversaries from being successful in their attacks. Since Microsoft Office is a very trusted and used software by many people (both in personal and commercial use), we will make use of its features to build our payloads and eventually to gain a remote code execution to a victim's system. Performing a successful phishing attack involves a lot of barriers that often need to be crossed such as the need for similarity, purchasing domains, the use of encoding, encryption, etc. Nowadays, companies frequently employ very aggressive antivirus software that will delete malicious files as soon as they land on their system. Therefore, bypassing the security protections will need to be taken into account, which will also be addressed in this paper.

Software size – often measured in the source lines of code (SLOC) – fundamentally determines the software development effort. Realistic estimates of software size are, therefore, crucial for project planning. However, even though software size is influenced by both functional requirements (FRs) and non-functional requirements (NFRs), NFRs have been largely neglected in previous SLOC estimation studies. This study conducts an initial investigation of the impact of NFRs on early SLOC estimation by focusing on security-based NFRs related to data entry validations. First, the IFPUG software non-functional assessment process (SNAP) is used to calculate SNAP points for data entry validations (SPDEV). Then, SPDEV is used along with specially adjusted analysis class diagram (ACD) metrics to build and validate an early SLOC estimation model using an industrial dataset. Finally, the proposed model is compared with two existing size estimation models. Results indicate that our proposed model outperforms both models in terms of estimation accuracy.

Speakers tend to engage in adaptive behavior, known as entrainment, when they reuse their partner's linguistic representations, including lexical, acoustic prosodic, semantic, or syntactic structures during a conversation. Studies have explored the relationship between entrainment and social factors such as likeability, task success, and rapport. Still, limited research has investigated the relationship between entrainment and gaze. To address this gap, we conducted a within-subjects user study (N = 33) to test if gaze behavior of a robotic head affects entrainment of subjects toward the robot on four linguistic dimensions: lexical, syntactic, semantic, and acoustic-prosodic. Our results show that participants entrain more on lexical and acoustic-prosodic features when the robot exhibits well-timed gaze aversions similar to the ones observed in human gaze behavior, as compared to when the robot keeps staring at participants constantly. Our results support the predictions of the computers as social actors (CASA) model and suggest that implementing well-timed gaze aversion behavior in a robot can lead to speech entrainment in human-robot interactions.