Evaluation Matrix Research Articles

Design and Implementation of a New Framework for Post-Synthesis Obfuscation with a Mixture of Multiple Cells with an Integrated Anti-SAT Block

This paper proposes a new framework for post-synthesis obfuscation of digital circuits using a mixture of cells combined with an Anti-SAT block. Furthermore, a novel integrated framework has been established wherein obfuscation, along with Anti-SAT and validation of the benchmarks, progress through MATLAB®, Python, Cadence RTL Encounter® and Cadence LEC® to implement the proposed methodology. Area, delay, leakage power and total power are adopted as elements of the evaluation matrix. These parameters are compared between the original circuit, the circuit after obfuscation, the circuit after integration with Anti-SAT and the circuit after implementation of the proposed method of multiple-cell obfuscation with Anti-SAT. The probability of breaking a circuit is taken as the security criterion. It is mathematically proven that as the number of types of obfuscated cells used increases, then the probability of breaking the circuit decreases. The results obtained accord with the mathematical proof. The framework minimizes the delay by inserting obfuscation cells (OCs) in the non-critical paths, strengthens the security by using several types of OCs and allows the user to select a design based on justified area, leakage power and total power. However, against a Boolean SATisfiability (SAT) attack, obfuscation with multiple cells is not a sufficient defense. An Anti-SAT block performs better than obfuscation but has its own limitations. Thus, use of an Anti-SAT block in combination with multiple-cell obfuscation is proposed and implemented, giving better results against an efficient SAT attack. The number of iterations, as well as runtime to obtain the correct keys, increase significantly for the Anti-SAT block combined with multiple-cell obfuscation compared to the Anti-SAT or obfuscation block alone.

Strategi Peningkatan Pembiayaan Multi Guna (PMG) Pada PT. Bank Sumut Unit Usaha Syariah

Improper financing management will cause many problems and will even cause the collapse of Islamic banks. In financing carried out by Islamic banking, there are bound to be many obstacles experienced, such as default constraints, problematic financing or other constraints, especially during the Covid 19 pandemic. To minimize the various obstacles faced earlier, of course, Islamic banking is required to have an accurate strategy to win market competition while minimizing the occurrence of problem financing. This study aims to analyze the strategy of PT. Bank Sumut Sharia Business Unit in increasing Multipurpose Financing (PMG). This research is a type of qualitative research. Obtained research results SWOT analysis results show that the company's position is in quadrant 1, namely supporting an aggressive strategy. This quadrant position is a very favorable situation, where the company has opportunities and strengths so that it can take advantage of existing opportunities. The strategy that must be applied in this condition is the SO (Strength - Opportunity) Strategy, which is a strategy that utilizes the company's internal strengths to take advantage of external opportunities. The S-O strategy is developed by combining strengths in the internal evaluation matrix and opportunity factors in the external evaluation matrix.

Green Financial Supervision Information System Based on Genetic Algorithm Optimization under Carbon Peaking and Carbon Neutrality Goals

Under the guidance of “carbon peaking and carbon neutrality”, “ecological priority” and “green development” have become the popular consensus, and the financial regulatory level continuously guides financial institutions to increase investment in green and low carbon projects. In the field of green financial supervision in China, due to imperfect systems and poor adaptability, financial risks are often difficult to control within a reasonable range, which has had a significant impact on financial supervision and management. This article aimed to optimize the green financial regulatory information system under the carbon peaking and carbon neutrality goals. Firstly, this article analyzed the concept and background of green finance regulation; then, an investigation was conducted on the construction of the green finance service information system, and a green finance information system supervision plan was established. Finally, data collection and analysis were conducted, and the supervision of the green finance information system was carried out using a standard genetic algorithm based on a fuzzy evaluation matrix. This article used a genetic algorithm to optimize the green financial regulatory information system, and selected 500 people to evaluate the use of the system before and after the optimization. The proportion of very satisfied people increased from 11.2% to 19.2%; the proportion of satisfied people increased from 17.2% to 37.6%; the proportion of people who were very dissatisfied decreased from 14.4% to 3.6%. The experiment in this article showed that the optimized system could operate more stably, and the process was more reasonable. The statistical analysis ability was significantly enhanced, and the functions were more comprehensive. This suggests that the system could better regulate the development of green finance.

Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Jurusan pada Peserta Didik Baru

Majoring students is a process of placing students into certain majors in accordance with their interests and academic abilities in an effort to make it easier for students in the learning process. Madrasah Aliyah Darul Hikmah Menganti is a school equivalent to SMA, which has two majors, namely science and social studies. The difficulty of classifying the majors of new students is an obstacle for the school. Because the criteria assessment process is carried out one by one. From these problems, the K-Nearest Neighbor (K-NN) method was applied to classify majors in order to simplify and minimize errors in the process of determining new student majors. The data initially amounted to 638 records and 31 attributes. After preprocessing, the data used amounted to 635 records with 12 attributes, namely name, gender, major interest, school origin, children to, number of siblings, math scores, English grades, science grades, Indonesian language scores, test scores, and major recommendations. After testing using K-Fold Cross Validation and Confusion Matrix for evaluation and validation of results by calculating the Euclidean Distance distance, the best k value (optimal) k=3 which produces accuracy: 97.11%, precision: 96.82%, recall: 98.33%, and AUC: 0.951.

Strategi Pengembangan Usaha Bandeng Duri Lunak Mandala Presto Berbasis Model Bisnis Kanvas Di Kabupaten Bogor

Mandala Presto is one of the milkfish processing businesses in Bogor Regency. The business started producing soft-boned milkfish without packaging in 2011. Collaborating with 35 street vendors, they sold soft-boned milkfish in traditional markets and through door-to-door sales. In 2018, the owner changed the business model to focus on hygienic vacuum-packaged soft-boned milkfish. The aims of this study were (1) to identify internal and external factors influencing the development of Mandala Presto's soft-boned milkfish business, (2) to develop the implementation of Mandala Presto's soft-boned milkfish business model canvas for its business strategy, and (3) to determine the prioritized strategy choices from the nine elements of the business model canvas to enhance the growth of Mandala Presto's soft-boned milkfish business. The method used was the business model canvas to map the condition of Mandala Presto, followed by descriptive and quantitative analysis including the internal factor evaluation (IFE) matrix, external factor evaluation (EFE) matrix, internal-external (IE) matrix, strengths, weaknesses, opportunities, and threats (SWOT) matrix, and analytical hierarchy process (AHP) for business model improvement. The IFE calculation result is 3.32, and the EFE calculation result is 2.79, which are mapped in the IE matrix in cell IV, indicating that the business is in a growing and developing position. The priority strategy determination with AHP focuses on four elements of the business model canvas: key partners, key activities, channels, and key resources.

Improving the Social Utility Evaluation of Brazilian Community Banks: A Study on Banco dos Cocais

Purpose: The aim of this article is to improve the model for evaluating the social utility of Brazilian Community Development Banks (CDB), based on the perceptions of managers and users-clients-beneficiaries. Theoretical Framework: The CDBs are solidarity finance initiatives, inserted in the social and solidarity economy in Brazil, whose purposes are carried out based on their social utility in terms of the results achieved with their model of action for the human, political, economic and social development of the territories. Method: This is an exploratory and descriptive research using a qualitative approach. Data was collected through a documentary survey, field observation and semi-structured interviews at the CDB Banco dos Cocais dos based on the categories of social utility (socio-environmental, socio-political, socio-cultural, socio-economic and socio-institutional). The data was analyzed using content analysis.. Results and Conclusions: Of the 35 subcategories of social utility analyzed, 29 were covered by CDB Banco dos Cocais. The socio-economic, socio-cultural, socio-institutional and socio-environmental categories showed the greatest impact on the social utility of Banco dos Cocais, while the socio-political category showed the least impact, signaling the need to strengthen the political empowerment of the population in the territory. Research Implications: This research's theoretical contribution includes the reconfiguration of the DECID matrix for social utility evaluation. From the perspective of practical implications, the study aims to contribute to improving the management and evaluation of the CDBs' actions in their territories. Originality/Value: The research seeks to show that the actions of the CDBs have a strong social utility integrated into the development and strengthening of the territories, which is greater than their solely economic results.

Strategi Pengembangan Pada Produk Inovasi Biskuneo (Studi Kasus: Tenant Badan Riset Inovasi Nasional)

Biskuneo is one of the innovative products resulting from research by the National Innovation Research Agency (BRIN) to find the right commercialization strategy based on market demand to support industry needs. The aims of this study were (1) to identify internal and external factors that influence the development strategy of Biskuneo innovation products and (2) to compile the application of the Biskuneo innovation product canvas business model to business development. The canvas business model method is used to map the condition of Biskuneo products, followed by a descriptive and quantitative analysis consisting of an internal factor evaluation matrix (IFE), an external factor evaluation matrix (EFE), an internal-external matrix (IE), a matrix of strengths, weaknesses, opportunities, and threats (SWOT). The IFE calculation result is 2.339 and the EFE calculation result is 2.580 which is mapped in the IE matrix in cell V, indicating that Biskuneo is in a position to grow and develop. Strategy determination in the SWOT matrix is ​​in quadrant I (SO strategy), namely the product has great opportunities and strengths so that it can take advantage of existing opportunities. The recommended strategy is an aggressive/progressive strategy.

Strategi Pengembangan Portofolio Pembiayaan Mikro di Bank Syariah Indonesia (Studi Kasus BSI Cabang Pembantu Bogor Dramaga Perwira)

The role of micro, small and medium business (UMKM) in the structure of the Indonesian economy is important and strategic. Conventional and sharia banking support the increase of UMKM through funding, as well as technical and non-technical assistance. The research was taken at Bank Syariah Indonesia (BSI) Bogor Dramaga Perwira Sub-Branch Office (KCP), under supervision of Regional V Jakarta 2 and Bogor Area, with a focus on microfinance. The research objectives were (1) to identify internal and external factors that influence the microfinance portofolio development, (2) to formulate an appropriate strategy to increase the growth of microfinance portofolio and (3) to recommend the development of UMKM according to the existing BSI microfinance scheme. This research based on data and information from PT BSI in the period of 2018-2022 which affect financing performance. Data analyzed using the Internal Factor Evaluation (IFE) and the External Factor Evaluation (EFE) matrix, followed by Strength-Weaknesses-Opportunities-Threats (SWOT) and Quantitative Strategic Planning (QSP) matrixes. The internal factors that become strengths are the pricing of low-cost financing with BSI Kredit Usaha Rakyat (KUR) products, easy requirements, Service Level Agreement (SLA) fast financing processes and sharia-compliant financing agreements. External factors that become opportunities are the large number of UMKM, the public's interest to Islamic banking and many competing banks. Alternative strategies results of SWOT analysis followed by QSP matrix were building partnerships with the micro business community through micro cluster financing, maximizing online marketing and services, and innovating microfinance products.

Integration strategy of folk art elements in art design based on fuzzy numerical analysis method

Abstract This paper utilizes the fuzzy numerical analysis method to display the customary activities using rich artistic elements and stylistic changes so that the value and quality of the works can be significantly improved. Folk art influences are divided by nature, adjacent structure weights are estimated, the scale of matrix elements is evaluated, and the art design vector is adjusted. Comprehensively evaluate and assign art design factors, establish the fusion judgment set and fuzzy evaluation matrix, and complete the research on the fusion of folk art elements and art design by adopting the principle of maximum affiliation. According to fuzzy numerical analysis, the fusion score is 0.9, the overall art design aesthetics is improved, and the fusion effect is better.

Method to assess the integration of personalization, stimulus and environmental design principles in school classrooms

Studies have shown that a series of architectural factors of the physical learning environment affect the educational experiences of students in schools, particularly learning and well-being. This study proposes a method to holistically assess school classrooms considering three design principles: personalization, stimulus, and environmental. An evaluation matrix has been prepared considering 40 architectural factors, which are categorized into three ranges to generate a score and show their level of compliance. This was applied to a sample size of 30 classrooms from 23 Chilean schools. A classification was generated using the compliance levels, determining deciles and quartiles to set ranges appropriate to local realities along with three classroom classifications: insufficient, sufficient, and satisfactory. This classification proposes a starting point that can evolve as factors in the classroom improve or other cases are incorporated into the database. The method can be replicated in other contexts because it allows identifying which design parameters need to be improved or integrated into school classrooms.

The cultivation path of vocational talents in rural revitalization industry based on multidimensional data analysis method in the context of industry-education integration

Abstract This paper firstly analyzes the demand for industrial technical talents and industrial vocational talents training specifications for rural revitalization and proposes a vocational talents training model for the rural revitalization industry based on the integration of industry and education. Secondly, a multidimensional talent training evaluation index system is constructed, and then a network hierarchy analysis of the evaluation index system is conducted using algorithms such as evaluation matrix, entropy value method and game theory combination assignment. Finally, quantitative data analysis was carried out on the indicator weights and the evaluation of indicators using County A of City B as an example. The results show that the overall score of the talent cultivation index evaluation system in County A is 79.96, among which the evaluation scores of first-level indicators are 16.73, 10.56, 21.42, 7.89, 17.06 and 6.30.

The optimization model of whole process engineering consulting consortium members based on Z-number

At present, there are very few enterprises with whole-process engineering consulting capability and qualification. To carry out consulting work in a consortium is a necessary way to meet the actual situation of the industry and promote whole-process consulting quickly, and the selection of consortium members is the key issue. First of all, this paper summarizes the construction principle of the index system of consortium member selection, and describes the index system. Aiming at the consortium member selection model, on the basis of comprehensive consideration of 9 indicators, the evaluation value of Z-Numbers fuzzy numbers is obtained by using seven-point language terms, the index weight and expert weight are obtained by using the sequential method and entropy weight method, and the data are processed to get the alternative enterprise evaluation matrix. Fuzzy TOPSIS method suitable for Z-Numbers was introduced to solve the positive ideal solution closeness degree of each enterprise, so as to rank the alternative enterprises.

Congenital syphilis: performance of primary care services in São Paulo, 2017.

To evaluate congenital syphilis prevention actions in primary health care services in the state of São Paulo. Cross-sectional evaluative research that used indicators extracted from the Survey of Evaluation and Monitoring of Primary Care Services ( Avaliação e Monitoramento de Serviços da Atenção Básica - QualiAB) in the state of São Paulo in 2017. An evaluative matrix composed of 31 indicators of prevention of congenital syphilis, categorized into four domains of analysis: diagnosis and treatment of acquired syphilis (10); basic infrastructure and resources (7); prevention of congenital syphilis during prenatal care (7); and educational actions and prevention of sexually transmitted infections (7). The frequency of services with positive responses for each indicator and the percentage of service performance were calculated based on the proportion of indicators reported per service and the overall average observed. Subsequently, services were classified into four quality groups, and associations between groups and each indicator, type of organizational arrangement and location were estimated. 2,565 services participated, located in 503 municipalities, with an overall average performance of 74.9%. The domain "diagnosis and treatment of acquired syphilis" had the highest performance (89.8%), followed by "infrastructure and basic resources" (79.5%), "prevention of congenital syphilis in prenatal care" (73.3%) and "educational actions and prevention of sexually transmitted infections" (56.8%). There was a significant difference between quality groups and all indicators and types of organizational arrangements. The evaluated services have limitations in the development of actions to prevent congenital syphilis, mainly related to health education and actions included in prenatal care, such as screening and adequate treatment of pregnant women and their partners. Changes are needed in the work process, with the expansion of educational and surveillance actions, as well as the qualification of the teams to effectively comply with the protocols.

Research on the Countermeasures for the Construction of High-level Practical Training Bases in Higher Vocational Colleges Based on the Integration of Industry and Education

Abstract To quickly establish high-level training bases in higher vocational colleges while integrating industry and education, this article first describes the concepts and layout of the space. Secondly, Using interest-related data, an assessment method of the indicators that demonstrate the efficiency of bringing together industry and schooling is developed, the weights of the indicators are determined, and the evaluation matrix is constructed for a comprehensive evaluation using ANP and gray fuzzy theory, respectively. Three colleges were used as examples to examine the effectiveness of integrating industry and education in creating advanced knowledge bases. The findings show that the greatest weighting of the indicators for how well industry and education are integrated is 0.3135, and the highest governance economic investment in building high-level training supports is 93.26, suggesting that building high-level training bases can enhance how well industry and education are integrated.

Türkiye’de Wattpad Kullanımı Nedenleri

This study was carried out to determine the reasons why users use Wattpad in Turkey. The benefits and harms of Wattpad content and the factors that motivate readers here are important for determining the methods to implement when using this platform. The study was designed as a case study as a qualitative research method. As a data collection instrument, interviews with writers and readers were conducted. Additionally, various documents and a research diary were used. Peer evaluation, member check and role-ordered matrix methods were used to analyze data and create categories. According to the content analysis results, the reasons why users use the Wattpad platform were determined to be the desire to be popular, interaction, ease of use, subjects and language. The reasons for users to use the platform are collected and addressed in the Results section under the headings of sense of belonging and quest for meaning.

Large Language Model-Based Neurosurgical Evaluation Matrix: A Novel Scoring Criteria to Assess the Efficacy of ChatGPT as an Educational Tool for Neurosurgery Board Preparation

Technological advancements are reshaping medical education, with digital tools becoming essential in all levels of training. Amidst this transformation, the study explores the potential of ChatGPT, an artificial intelligence model by OpenAI, in enhancing neurosurgical board education. The focus extends beyond technology adoption to its effective utilization, with ChatGPT's proficiency evaluated against practice questions from the Primary Neurosurgery Written Board Exam. Using the Congress of Neurologic Surgeons (CNS) Self-Assessment Neurosurgery (SANS) Exam Board Review Prep questions, we conducted 3 rounds of analysis with ChatGPT. We developed a novel ChatGPT Neurosurgical Evaluation Matrix (CNEM) to assess the output quality, accuracy, concordance, and clarity of ChatGPT's answers. ChatGPT achieved spot-on accuracy for 66.7% of prompted questions, 59.4% of unprompted questions, and 63.9% of unprompted questions with a leading phrase. Stratified by topic, accuracy ranged from 50.0% (Vascular) to 78.8% (Neuropathology). In comparison to SANS explanations, ChatGPT output was considered better in 19.1% of questions, equal in 51.6%, and worse in 29.3%. Concordance analysis showed that 95.5% of unprompted ChatGPT outputs and 97.4% of unprompted outputs with a leading phrase were aligned. Our study evaluated the performance of ChatGPT in neurosurgical board education by assessing its accuracy, clarity, and concordance. The findings highlight the potential and challenges of integrating AI technologies like ChatGPT into medical and neurosurgical board education. Further research is needed to refine these tools and optimize their performance for enhanced medical education and patient care.

Prioritizing Riverine Bridge Interventions: A Hydrological and Multidimensional Approach

Globally, most bridges fail due to hydrological causes such as scouring or flooding. Therefore, using a hydrological approach, this study proposes a methodology that contributes to prioritizing the intervention of bridges to prevent their collapse. Through an exhaustive literature review, an evaluation matrix subdivided into four dimensions was developed and a total of 18 evaluation parameters were considered, distributed as follows: four environmental, six technical, four social, and four economic. This matrix was applied to eight bridges with a history of hydrological problems in the same river and validated through semi-structured interviews with specialists. Data were collected through field visits, journalistic information, a review of the gauged basin’s historical hydrological flow rates, and consultations with the population. Modeling was then conducted, which considered the influence of gullies that discharge additional flow using HEC-HMS and HEC-RAS, before being calibrated. The application of the matrix, which is an optimal tool for prioritizing bridge interventions, revealed that five bridges have a high vulnerability with scores between 3 and 3.56, and three bridges have a medium vulnerability with scores between 2.75 and 2.94. The hydrological multidimensional approach, which can be adapted for similar studies, contributes to a better decision-making process for important infrastructure interventions such as riverine bridges.

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

Credit Risk Detection in Peer-to-Peer Lending Using CatBoost

P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk to minimize the risk of P2P lending. In this study, a system had been built using the CatBoost method; the dataset used was taken from the Bondora loan dataset. To measure the performance of the CatBoost algorithm, an evaluation matrix was performed using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) was performed. The experiment consists of three scenarios, of which the best result regards Scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value 0.80329 compared to scenario 1 with a data split of 80:20 with the AUC value around 0.789583, and scenario 3 with a data split of 70:30 with the AUC value around 0.781066, respectively.

Informing Children about Privacy: A Review and Assessment of Age-Appropriate Information Designs in Kids-Oriented F2P Video Games

With the rise of free-to-play (F2P) games, the profitability of video-gaming apps critically depends on the ability of developers to acquire, retain, and monetize large numbers of players. In this context, most game designers have no viable alternative than massively collecting players' personal data and monitoring their behavior to target them with personalized advertising and in-game purchases. Given the risks associated with such data practices, players, in particular children, need to be aware that a video game might compromise their privacy. Game designers should therefore ensure that players receive appropriate information about the data practices associated with their games. This might, however, be challenging, especially when the game is directed at children, given the complexity of privacy information and the limited literacy capacities of children and their parents. To answer game designers' need for comprehensive guidance regarding the communication of privacy information to children, we provide a survey of the age-appropriate information design strategies which been recommended by data protection authorities, children protection organizations and the relevant scientific literature. On this occasion, we also refer to illustrative examples of designs which can be considered good practices. Finally, by using an "evaluation matrix", we reviewed and assessed the implementation of those design strategies in nine F2P mobile games committed to following Google Play's Families Policies. Our findings show that, despite being child-oriented, the reviewed games largely fail at communicating privacy information in an age-appropriate way.