trust value
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Appropriate supervision strategies are the backbone of community corrections. The success of community supervision is dependent upon the attitudes of both officers and offenders. Despite this, research on offenders’ attitudes toward community corrections supervision is surprisingly very limited. The current study investigated attitudes of officers and offenders toward and predictors of four different community supervision strategies based on data collected in Hubei, China, in 2103 and 2016. The study found that among demographics, community variables, and value factor, the mutual trust value factor was the most important predictor of community supervision strategies by both officers and offenders. Additional findings and policy implications are discussed.

Educational progress is produced by a strategic and quality process. To produce educational progress, the concept of educational management is needed with the Positioning-Differentiation-Brand strategy. This study aims to formulate a theoretical model: 1) positioning strategy developed by SMK Muhammadiyah 7 Gondanglegi to improve school competitiveness 2) differentiation strategy by SMK Muhammadiyah 7 Gondanglegi so as to strengthen the attractiveness of new students 3) branding strategy at SMK Muhammadiyah 7 Gondanglegi to make it known easier and become the hope of society 4) management strategy and reconstruction of Positioning-Differentiation-Brand (PDB) SMK Muhammadiyah 7 Gondanglegi so as to achieve the success of graduates being accepted by the industry and achieving school progress. This study uses a qualitative phenomenological approach in order to give birth to a phenomenological model formulation. Data collection techniques used: 1) direct observation, 2) documentation study, and 3) in-depth interviews. Data analysis using data reduction, data presentation, and drawing conclusions. The validity and reliability of the research results are measured by four criteria: 1) Credibility, 2) Transfermability, 3) Dependability, and 4) Confirmability.The results of this study indicate: 1) positioning strategy: on the customer (customer); on internal capabilities and strengths (company); over competitors (competitors); on changes (change); be a power of differentiation (clarity); compete for the products owned (consistency); have high credibility (credibility), and have superior products (competitiveness). 2) differentiation strategy: unique and different performance and design (product differentiation); friendly service with speed and convenience (service differentiation); capabilities in distribution channels (channel differentiation); Reliable Human Resources (HR) (people differentiation); courage to act (progressive differentiation); Iduka curriculum is always updated (content), industrial cooperation (context), and technology with other facilities (infrastructure: inabler) 3) branding strategy; innovative (core identity); open (extended identity); public trust (value proposition). 4) Positioning-Differentiation-Brand (PDB) management and reconstruction.Keywords: Education Management, Positioning-Differentiation-Brand (PDB), Educational Progress. The findings of this study are the reconstruction of the education management concept model with the Positioning-Differentiation-Brand (PDB) strategy for the advancement of Islamic education.

Abstract: Due to the characteristics like limited resources and dynamic topology, wireless sensor networks (WSNs) are facing two major problems such as security and energy consumption. To deal with various improper behaviors of nodes the trust-based solutions are possible but still exist a variety of attacks, high energy consumption, and communication congestion between nodes. Therefore, this paper proposes an advanced and efficient trust-based secure and energy-efficient routing protocol (TBSEER) to solve these network problems and to avoid malicious nodes. Efficient Adaptable Ant Colony Optimization Algorithm (EAACO) calculates the comprehensive trust value through adaptive direct trust value, indirect trust value, and energy trust value, which can be resistant to internal network attacks such as sinkhole, black hole, selective forwarding, and hello flood attacks. In addition, to fast identify the malicious nodes in the WSN, the adaptive penalty mechanism and volatilization factor are used. Moreover, the nodes only need to calculate the direct trust value, and the indirect trust value is obtained by the sink, so as to further reduce the energy consumption caused by iterative calculations. To actively avoid network attacks, the cluster heads find the safest multi-hop routes based on the comprehensive trust value. The simulation results show that the proposed EAACO reduces network energy consumption, speeds up the identification of malicious nodes, as well as resists all common attacks. Keywords: Comprehensive trust value, direct trust value, indirect value, EAACO, network attacks, wireless sensor networks

This paper introduces the Trust-Distrust Model and its applications, extending the Bounded Confidence Model, a theory of opinion dynamics, to include the relationship between trust and mistrust. In recent years, there has been an increase in the number of cases in which the prerequisites for conventional communication (e.g., the other person’s gender, appearance, tone of voice, etc.) cannot be established without the exchange of personal information. However, in recent years, there has been an increase in the use of personal information, such as letters and pictograms “as cryptographic asset data” for two-way communication. However, there are advantages and disadvantages to using information assets in the form of personalized data, which are excerpts of personal information as described above. In the future, the discussion of trust value in the above data will accelerate in indicators such as personal credit scoring. In this paper, the Trust-Distrust Model will be discussed with respect to theories that also address charismatic people, the effects of advertising, and social divisions. Furthermore, simulations of the Trust-Distrust Model show that 55% agreement is sufficient to build social consensus. By addressing this theory, we hope to use it to discuss and predict social risk in future credit scoring discussions.

Abstract This paper aims at creating a new Trust Management System (TMS) for a system of nodes. Various systems already exist which only use a simple function to calculate the trust value of a node. In the age of artificial intelligence the need for learning ability in an Internet of Things (IoT) system arises. Malicious nodes are a recurring issue and there still has not been a fully effective way to detect them beforehand. In IoT systems, a malicious node is detected after a transaction has occurred with the node. To this end, this paper explores how Artificial Intelligence (AI), and specifically Linear Regression (LR), could be utilised to predict a malicious node in order to minimise the damage in the IoT ecosystem. Moreover, the paper compares Linear regression over other AI-based TMS, showing the efficiency and efficacy of the method to predict and identify a malicious node.

Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

Abstract In cloud security, detecting attack software is considered an essential task. Among several attack types, a zero-day attack is considered as most problematic because the antivirus cannot able to remove it. The existing attack detection model uses stored data about attack characteristics, which fails to detect zero-attack where an altered attack is implemented for an antivirus system to detect the attack. To detect and prevent zero-day attacks, this paper proposed a model stated as Hidden Markov Model Transductive Deep Learning (HMM_TDL), which generates hyper alerts when an attack is implemented. Also, the HMM_TDL assigns labels to data in the network and periodically updates the database (DB). Initially, the HMM model detects the attacks with hyper alerts in the database. In the next stage, transductive deep learning incorporates k-medoids for clustering attacks and assign labels. Finally, the trust value of the original data is computed and computed in the database based on the value network able to classify attacks and data. The developed HMM_TDL is trained with consideration of two datasets such as NSL-KDD and CIDD. The comparative analysis of HMM_TDL exhibits a higher accuracy value of 95% than existing attack classification techniques.

Our study aims to analyze factors determining the green brand equity (GBE) based on a systematic literature review (SLR) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We posed 3 research questions and searched five databases (Scopus, Web of Sciences, Google Scholar, EBSCO, and Elsevier) for studies containing the term ‘green brand equity’ and the combination of two terms: ‘brand equity’ and ‘green’. Additionally, the backward and forward snowballing methods were applied. In our SLR, we included empirical studies published between 2006 and 2021 as peer-reviewed papers in English. Exclusion criteria included studies with theoretical models, studies describing brand equity not related to GBE, Ph.D. thesis, short reports, workshop papers, practice guidelines, book chapters, reviews, and conference publications. Finally, 33 articles were analyzed as part of the SLR in two fields: general information (authorship, year of publication, type of study, research country or location, sample size, and product categories), and research specifications (factors or variables, number and type of hypotheses, scale or measurement items, type of statistical analysis, and selected indicators of statistical methods). Image, trust, value, satisfaction, and loyalty appeared to be the most studied determinants of GBE. Less frequently analyzed were quality, awareness, attributes, particular promotional activities, and the fact of purchase. The results obtained are important in practical terms, showing what to consider when creating GBE in different categories of products and services.