Development Costs Research Articles

A deep learning-based approach to similarity calculation for UML use case models

Unified Modeling Language (UML) models are main artifacts of software applications and valuable assets of software projects. The similarity calculation of UML models is an effective way to model reuse, model validation, model retrieval and pattern detection, which can help developers reduce development cost. Existing studies mainly focus on UML design models such as class diagrams and object diagrams. Yet, there is little research on calculating similarity for use case models despite their importance for software requirements reuse and validation. Thus, this paper proposes a similarity calculation approach for UML use case models. This approach first transforms the use case models into use case graphs and then adopts the deep learning technique — Similarity Graph Neural Network (SimGNN) to calculate the structural similarity between use case graphs. Then it calculates the semantic similarity of use case models with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine similarity. Finally it syntheses the results of semantic similarity and structural similarity of use case graphs. The effectiveness and efficiency of this approach was evaluated by comparing to two other approaches: (1) Cosine similarity+TF-IDF and (2) Cosine similarity+edit distance. The results showed that the our approach has better performance in terms of both effectiveness and efficiency.

Assessing the recycling potential of thermosetting polymer waste in high-density polyethylene composites for safety helmet applications

Abstract The rising demand for thermosetting polymers has resulted in the production of large amounts of industrial waste. Environmental issues due to waste landfills and increased raw material costs for new product development have led to the development of innovative recycling methods. This study focuses on the development of a product (helmet shell) by reinforcing thermosetting polymer waste (TPW) as a filler in a high-density polyethylene (HDPE) matrix. The HDPE and TPW were converted into extrudates using a twin-screw extruder. Then, the extrudate was pelletized to use as raw material for the injection molding machine. The HDPE/TPW composites were fabricated using injection molding. Maleic anhydride-grafted polyethylene was employed as a compatibilizer. In the composite, the TPW volume was reinforced at various weight percentages, ranging from 0 to 35 wt%. The mechanical, thermal, and viscoelastic properties of the composites can be enhanced by uniformly dispersing TPW in the HDPE matrix. However, it is difficult to achieve uniform dispersion at higher TPW volumes owing to the agglomeration effect. According to these findings, the mechanical properties were enhanced by up to 30 wt% addition of TPW. The findings suggest that the proposed composite has sufficient mechanical properties to be suitable for the fabrication of helmet shells.

Analyzing the impact of geological features on reservoir heterogeneity using heterogeneity logs: A case study of Permian reservoirs in the Persian Gulf

Carbonate reservoir heterogeneity has a major impact on fluid flow and hydrocarbon recovery. It is challenging to characterize due to the complex and heterogeneous nature of carbonate reservoirs. It is important to express heterogeneity as a quantitative parameter for comparing various reservoirs. It can also be used for investigating the influence of different factors on the degree of heterogeneity. Heterogeneity log is one of the effective methods for quantifying heterogeneity. The aim of the present study is to investigate the application of heterogeneity log to determine the geological features affecting heterogeneity in Upper Dalan Member (Late Permian) in the central Persian Gulf. The gamma ray, bulk density, neutron porosity, acoustic, and deep resistivity well logs data were used to calculate the heterogeneity logs. A total of 1093 thin sections were studied and primary depositional as well as secondary diagenetic changes were recorded. Porosity and permeability of the same number of samples were measured on the plugs. Heterogeneity logs were calculated by Lorenz coefficient method. The changes in heterogeneity logs were classified into different ranges and compared with frequency of geological features in corresponding intervals. Findings show that heterogeneity logs can help identify areas of the reservoir with distinct reservoir behavior (permeability and/or porosity). These areas can be targeted for increased oil and gas production. This can help reduce exploration and development costs. Dolomite samples are observed in both low and high heterogeneity values. Anhydrite lithology reduces porosity and increases heterogeneity. High heterogeneity values correspond with mudstone texture from supratidal, peritidal, and lagoon subenvironments with low porosity. Grainstone textures from shoal subenvironment and crystalline carbonates are associated with low values of heterogeneity. These microfacies have high porosity and permeability as well as a high rate of marine cementation. Also, interparticle and moldic pores along with stylolites and fractures are observed in low values of heterogeneity. The increase in the ratio of pore space to the solid component of samples has resulted in a reduction in the value of heterogeneity. By creating different subenvironments with different microfacies and average porosities, environmental energy controls the heterogeneity of the studied member. Therefore, the amount of heterogeneity is mainly controlled by two factors namely, porosity and pore type.

Prospects and challenges of digital transformation of the general education system in the Russian Federation

The paper is devoted to the urgent problem of increasing the effectiveness of digital transformation mechanisms in the sphere of general education, aimed at improving the efficiency of functioning of general educational organizations. The analysis of normative legal acts regulating state regulation in the field of information technology application in education, domestic and foreign literature devoted to digital transformation of the economy and society as a whole has been carried out. The main directions of digital transformation of education were analyzed. The lack of elaboration of the issues of digital transformation of general education along with a large number of publications related to higher education institutions is noted. The main technological transformations preceding the current stage of introducing end-to-end technologies in the sphere of general education in the Russian Federation are highlighted. Emphasis is placed on the necessity of digital transformation of general education management. The problems of introducing end-to-end technologies in the sphere of general education management are considered against the background of the acute need to master new technologies and the transition of the economy to a new stage of development. The tasks of digital transformation of education have been studied and the analysis of the reasons hindering the introduction of modern digital technologies has been carried out, which allowed us to identify the need to update not only the methods of conducting the educational process with the use of digital educational content, but also to qualitatively change the management system of a modern educational organization. The study showed that the digital transformation of general education cannot take place without state regulation and research in the field of correlation between the classroom and digital component. Recommendations on the implementation of projects in the system of general education are given, which allow to eliminate the duplication of management functions, reduce the cost of software development and implementation at different levels of education management.

Current Situation of Working Conditions of Female Workers in Export Processing Zones and Industrial Zones

Along with active participation and participation in the struggle of political systems, levels and sectors, especially the role of localities and businesses, up to now the company has developed social housing. has achieved some remarkable results. However, with the current number of completed worker housing of about 2.7 million m<sup>2</sup>, which only accommodates more than 340 workers, the set goal has not been achieved. The reason for not achieving the desired goals is that the mechanisms and policies for developing social housing and housing for workers still have some contents that do not meet practical needs and have not been supplemented or adjusted, timely amendment and completion, such as: participants, beneficiaries, automatic construction investment process, purchase and sale management. The implementation of preferential social housing policies must be done through many steps, so the time to carry out investment procedures is long, the space for innovation and development of social housing is limited as required; The reasonable costs for social housing development have not been calculated correctly or fully. Preferential policies for investors of social housing projects and worker housing are not attractive enough and not realistic enough. On that side, the state budget is still difficult and has not been able to arrange preferential capital for loans for social housing development; There is no mechanism to mobilize reasonable resources to achieve efficiency and system. This article aims to provide a comprehensive and systematic understanding of the current situation of working conditions of female workers in Ho Chi Minh City. The topic of employment conditions of female workers has been studied by many researchers, but certain limitations still exist. In this article, the author pays special attention to the current working conditions of female workers in Ho Chi Minh City. With that goal, it will open up a multi-dimensional view of the problem, thereby contributing to solving the problems that are being posed in practice for the working conditions of female workers in Ho Chi Minh City.

The Influence of Regional Socioeconomic Features On the Distribution of Financial Resources For Higher Education

The article is devoted to solving the problem of “budgetary financial adequacy of higher education” in China through a fairer, uniform, and sufficient distribution of financial costs for higher education among the provinces of this country, considering significant differences in the natural, economic, and social conditions of development in various regions. Currently, in China, the calculation of standard financial costs for the education sector is carried out according to the legal requirements of the Budget Department of the Ministry of Finance, taking into account the number of people studying at the corresponding level of education, the cost norm for this level of education, and the deviation coefficient. Instead, this article proposes a more comprehensive and rational approach that considers regional differences in the cost of living and the financial burden on higher education institutions, increasing the government’s responsibility for supporting a balanced distribution of financial resources for higher education. To take into account regional differences, it is proposed to use a system of indices for the difference in the costs of regional development, which includes three main categories of indicators: natural factors (the complexity of the terrain and the share of the area planted with forests), economic factors (the index of prices for raw materials, GDP per capita, freight traffic, and the total duration of operation of urban public transport), and social factors (population density in municipal areas, number of graduates of general education colleges and universities). To determine the fair size of budgetary expenditures on higher education, it is proposed to make adjustments to the index of differentiation of regional development expenditures, which combines these indicators using the geometric regression average benchmark method (without including a constant term). A higher value of this index means that the region needs more resources to achieve the same level of development as another region, so the standard budget expenditures for higher education should be higher for it. For all provinces in China, the coefficient of financial adequacy of higher education was calculated as the ratio between the standard financial costs of higher education (calculated according to the proposals outlined in this article) and the actual costs incurred. It is proposed that this coefficient be used as a basis for optimizing their distribution. The article emphasises the need to form a strategy for financing higher education in China, which should be based on a dynamic permanent adjustment of the amount of allocated financial resources depending on changes in the regional social and economic conditions of development of each province, an effective institutional mechanism that would allow adapting this strategy to endogenous and endogenous changes in China’s economy, optimisation of horizontal transfer payments and partnership assistance programs, and incentive mechanisms for local governments to invest in higher education.

The interplay between blockchain adoption and green operations for a firm

AbstractThis study considers a firm selling only green products or both ordinary and green ones to consumers with or without blockchain technology. We find that the profitability of adoption of this technology for the firm relies heavily on consumers' environmental awareness. It should be adopted when consumers have relatively high environmental awareness. Here, the selling price, sales, and greenness level of green products are relatively higher compared to those in the case without blockchain. Finally, the firm only sells green products after blockchain adoption when consumers' environmental awareness level is relatively high or the development cost coefficient of green products is relatively small.

Development, challenges and strategies of natural gas industry under carbon neutral target in China

In the mid-21st century, natural gas will enter its golden age, and the era of natural gas is arriving. This paper reviews the development stages of global natural gas industry and the enlightenment of American shale gas revolution, summarizes the development history and achievements of the natural gas industry in China, analyzes the status and challenges of natural gas in the green and low-carbon energy transition, and puts forward the natural gas industry development strategies under carbon neutral target in China. The natural gas industry in China has experienced three periods: start, growth, and leap forward. At present, China has become the fourth largest natural gas producer and third largest natural gas consumer in the world, and has made great achievements in natural gas exploration and development theory and technology, providing important support for the growth of production and reserves. China has set its goal of carbon neutrality to promote green and sustainable development, which brings opportunities and challenges for natural gas industry. Natural gas has significant low-carbon advantages, and gas-electric peak shaving boosts new energy development; the difficulty and cost of development are more prominent. For the national energy security and harmonious development between economy and ecology under the carbon neutral goal, based on the principle of “comprehensive planning, technological innovation, multi-energy complementarity, diversified integration, flexibility and efficiency, optimization and upgrading”, the construction of the production-supply- storage-marketing system has to be improved so as to boost the development of the natural gas industry. First, it is necessary to strengthen efforts in the exploration and development of natural gas, making projects and arrangement in key exploration and development areas, meanwhile, it is urgent to make breakthroughs in key science theories and technologies, so as to increase reserve and production. Second, it should promote green and innovative development of the natural gas by developing new techniques, expanding new fields and integrating with new energy. Third, there is a demand to realize transformation and upgrading of the supply and demand structure of natural gas by strengthening the layout of pipeline gas, liquefied natural gas and the construction of underground gas storage, establishing reserve system for improving abilities of emergency response and adjustment, raising the proportion of natural gas in the primary energy consumption and contributing to the transformation of energy consumption structure, realizing low-carbon resources utilization and clean energy consumption.

Digitalization and the Future of Employment: A Case Study on the Canadian Offshore Oil and Gas Drilling Occupations

This paper presents a novel approach to identifying reskilling requirements, job merging pathways, and a tentative timeline for transforming offshore oil and gas drilling occupations amid the fourth industrial revolution (industry 4.0). The proposed algorithm focuses on potential job merging due to technological adoption. It introduces a scaling factor named digital readiness level to incorporate modulation factors (e.g., cost of development and deployment of new technologies, labour market dynamics, economic benefits, regulatory readiness, and social acceptance) that act as catalysts or hindrances for technology adoption. A feature-based approach is developed to assess the similarities between occupations, while a mathematical model is developed to project automation trajectories for each job under investigation. These facilitate the consideration of potential job merging scenarios and the associated timeline. Since technology adoption depends on the industry, region, occupation, and stakeholder’s ability to manage the transformation, the proposed algorithm is presented as a case study on Canadian offshore oil and gas drilling occupations. However, this algorithm and approach can be applied to other industries or occupation structures. The proposed algorithm projects that the total number of personnel on board (POB) in a typical offshore drilling platform will be reduced to six by 2058. A sensitivity analysis was conducted to assess the robustness of the proposed algorithm against variations in the feature values and weighting factors. It was found that when changing feature values and weighting factors up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm 20\%$</tex-math> </inline-formula> of their original values, only one job that remains after 2058 follows three different job merging pathways, while others remain unchanged. Even the job that followed three different pathways was composed of the same source jobs compared to the corresponding job in the baseline results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research is inspired by the ongoing digital transformation initiatives and their socioeconomic impact. The adoption of digital technologies, such as automation, robotization, digital twins, data-driven decision-making systems, smart devices, and cloud computing technologies, gradually transform existing workplaces into digitally-enabled smart workplaces. Therefore, stakeholders must invest in training programs to reskill existing workforces and to orient prospective employees to work at these smart workplaces. If technology adoption occurs at a rapid or slower pace than workforce reformation, industries cannot gain the optimum benefit from their digital transformation initiatives. Also, human capital investments may not generate much benefit if technology adoption and workforce reformation occur at different rates. Therefore, this work presents a novel framework to predict future employment scenarios, particularly for the workers in offshore oil and gas drilling activities, along with a tentative timeline. Stakeholders can utilize the proposed framework to effectively plan the pace of technological adoption, future workforce transformation and human capital investments.

Large-scale robust regression with truncated loss via majorization-minimization algorithm

The utilization of regression methods employing truncated loss functions is widely praised for its robustness in handling outliers and representing the solution in the sparse form of the samples. However, due to the non-convexity of the truncated loss, the commonly used algorithms such as difference of convex algorithm (DCA) fail to maintain sparsity when dealing with non-convex loss functions, and adapting DCA for efficient optimization also incurs additional development costs. To address these challenges, we propose a novel approach called truncated loss regression via majorization-minimization algorithm (TLRM). TLRM employs a surrogate function to approximate the original truncated loss regression and offers several desirable properties: (i) Eliminating outliers before the training process and encapsulating general convex loss regression within its structure as iterative subproblems, (ii) Solving the convex loss problem iteratively thereby facilitating the use of a well-established toolbox for convex optimization. (iii) Converging to a truncated loss regression and providing a solution with sample sparsity. Extensive experiments demonstrate that TLRM achieves superior sparsity without sacrificing robustness, and it can be several tens of thousands of times faster than traditional DCA on large-scale problems. Moreover, TLRM is also applicable to datasets with millions of samples, making it a practical choice for real-world scenarios. The codebase for methods with truncated loss functions is accessible at https://i-do-lab.github.io/optimal-group.org/Resources/Code/TLRM.html.

Adjusting starting points for initial price offers: the example of ibrutinib.

The Inflation Reduction Act of 2022 (IRA) allows the Medicare program to negotiate drug prices beginning in 2024. Based on the guidance in the statute, CMS has selected specific data items to use to adjust initial price offers for 10 drugs in the decision-making process. Although much of the data are publicly available, some of these data items will need to be collected directly from drug companies. A 2019 US House of Representatives Committee on Oversight and Accountability investigative report collected a wide range of data from manufacturers of 12 high-revenue drugs that show what is available from the drug companies, including development costs, marketing, pricing, competition, and patent status. This article focuses on the data obtained for ibrutinib, an oral medication for treating hematologic malignancies, which is one of the only drugs reviewed by the committee that also has been selected for Medicare price negotiation. We examine data that can be obtained only from the drug manufacturer that the IRA has explicitly identified as being used to determine the price and suggest potential negotiation strategies for CMS in response.

Modification of Simplified Method and Unit Price per FP for Appraisal of Software Development Cost

Modification of Simplified Method and Unit Price per FP for Appraisal of Software Development Cost

Smart Ground Support Equipment—The Design and Demonstration of Robotic Ground Support Equipment for Small Spacecraft Integration and Verification

In order to reduce the costs of integration and verification processes and to optimize the assembly, integration and verification (AIV) flow in the prototype development of small- and medium-sized spacecrafts, an industrial six-axis robot was used as a universal mechanical ground support equipment instead of a tailored prototype specific ground support equipment (GSE). In particular, a robotic platform offers the possibility of embedding verification steps such as mass property determination into the integration process while offering a wider range of ergonomic adaption due to the enhanced number of degrees of freedom compared to a classical static Mechanical GSE (MGSE). This reduces development costs for projects and enhances the flexibility and ergonomics of primarily mechanical AIV operations. In this paper, the robotic MGSE system is described, the operational prospects for in-line verification are elaborated and an example is given showing the possibilities and challenges of its operational use as well as its in-line mass determination capabilities. For this purpose, a method has been developed that allows for the precise measurement of the spacecraft mass using the robot’s existing technology without the need for additional hardware. Subsequent work will extend this to determine the center of gravity and the moments of inertia of the payload on the robotic MGSE.

Experimental data-driven model development for ESP failure diagnosis based on the principal component analysis

The reliable diagnosis of electrical submersible pump (ESP) failure is a vital process for establishing of the optimal production strategies and achieving minimum development costs. Although traditional ammeter charts and nodal analysis are commonly used for ESP failure diagnosis, the techniques have limitations, as it requires manpower and is difficult to diagnose the failure in real-time. Therefore, in this study, ESP failure diagnosis was performed using the principal component analysis (PCA). First, 11 types of 9,955 pieces of data were acquired from a newly constructed ESP experimental system for 300 days. During the experimental period, ESP failure occurred twice with a significant drop in performance: first on day 112 and second on day 271. The PCA model was constructed with the 8,928 pieces of normal status data and tested with the 1,027 pieces of normal and failure status data. Three principal components were extracted from the measured data to identify the patterns of the normal and failure status. Based on the logistic regression method to analyze the efficiency of the PCA model, it was found out that the developed PCA model showed an accuracy of 93.3%. Therefore, the PCA model was found to be reliable and effective for the ESP failure diagnosis and performance analysis.

Ассимиляционный потенциал природы и территорий: нетривиальные следствия управления устойчивым эколого-экономическим развитием

Ecological-economic systems, which are in a state of unstable dynamic equilibrium under the influence of natural and anthropogenic factors, strive for sustainability if human economic activity is able to maintain the assimilation potential at the level of reproduction of natural resources and environmental quality by devoting part of the income to the environmental agenda. Within the framework of the law of balanced environmental management T=1.618·t·a, sustainable environmental and economic development is achieved by allocating funds at the level (t) of the GDP growth rate (T) to the environmental agenda for the regions. The value of the ecological situation coefficient (ESC) in the law of balanced environmental management for the Russian Federation varies around the golden numbers 0.618, 1.618, 2.618, depending on the level of economic development in the biosphere, artificial maintenance of the quality of the environment in it, as well as in the future possibility of terraforming the solar planets systems. An indicator of sustainable, harmonious environmental and economic development is a linear relationship between the assimilation potential (a) and the EES at the level of golden numbers 0.618, 1.618, 2.618. In this case, sustainable environmental and economic development can be achieved in any environmental condition as long as balance is maintained between the rate of economic growth and allocations of funds for the restoration of the assimilation function of nature. But they must be directed to the environmental agenda in strict accordance with the golden proportion relative to the main directions of environmental activities, even in conditions of transformed (converted) nature, and in the near future, in conditions of terraforming. However, then all development costs will go exclusively to maintaining the habitat in multiples of the golden numbers of 1.618 and 2.618 in the coefficient of the law of balanced environmental management.

The Green Development in Saline–Alkali Lands: The Evolutionary Game Framework of Small Farmers, Family Farms, and Seed Industry Enterprises

Amid global climate change and population growth, the prevalence of saline–alkali lands significantly hampers sustainable agricultural development. This study employs theories of asymmetric information and bounded rationality to construct an evolutionary game model, analyzing the interactions among small farmers, family farms, and seed industry enterprises in the context of saline–alkali land management. It investigates the strategic choices and dynamics of these stakeholders under the influence of economic incentives and risk perceptions, with a focus on how government policies can foster green development. Utilizing Delay Differential Equations (DDEs) for simulations, this study highlights the risk of “market failure” without government intervention and underscores the need for government participation to stabilize and improve the efficiency of the green development process. The findings reveal that factors such as initial willingness to participate, the economic viability of salt-tolerant crops, seed pricing, research and development costs, and the design of incentive policies are crucial for sustainable land use. Accordingly, the paper proposes specific policy measures to enhance green development, including strengthening information dissemination and technical training, increasing the economic attractiveness of salt-tolerant crops, alleviating research and development pressures on seed companies, and optimizing economic incentives. This study provides a theoretical and policy framework for the sustainable management of saline–alkali lands, offering insights into the behavioral choices of agricultural stakeholders and supporting government strategies for agricultural and environmental protection.

МОДЕЛИРОВАНИЕ ПРОЦЕССОВ ОПТИМИЗАЦИИ ВЫБОРА ПРОГРАММ ПОДГОТОВКИ И ПЕРЕПОДГОТОВКИ СПЕЦИАЛИСТОВ С ИСПОЛЬЗОВАНИЕМ ГЕНЕТИЧЕСКОГО АЛГОРИТМА

The article considers the problem of modelling the processes of training (retraining) specialists to take into account changes in their professional field in a timely manner. It is considered that specialists sent for training (retraining) may have a different set of initial competencies. As a result, organizations engaged in educational activities may have various training (retraining) programs aimed at a different contingent of students. However, these organizations have limited capacity to carry out the activities of this type. In addition, these programs may vary significantly in terms of their development cost, both due to the cost of resources used by the educational organization and the cost of sending specialists by the dispatching organization. Therefore, organizations forwarding their specialists for training (retraining) need to select the optimal composition of training (retraining) programs for experts, providing each specialist with the greatest number of new competencies within a given constraint on financing this activity. To solve this problem, a mathematical model is developed and the choice of a numerical method based on using a genetic algorithm is substantiated. For this purpose the authors propose descriptions of genes, chromosomes, and individuals, as well as rules for forming a population, an evaluation function for individuals’ fitness, a selection operation, a crossover, and a method of individuals’ mutation. The conducted numerical experiments show the high efficiency of this numerical method.

Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery.

Despite the widespread use of ionizable lipid nanoparticles (LNPs) in clinical applications for messenger RNA (mRNA) delivery, the mRNA drug delivery system faces an efficient challenge in the screening of LNPs. Traditional screening methods often require a substantial amount of experimental time and incur high research and development costs. To accelerate the early development stage of LNPs, we propose TransLNP, a transformer-based transfection prediction model designed to aid in the selection of LNPs for mRNA drug delivery systems. TransLNP uses two types of molecular information to perceive the relationship between structure and transfection efficiency: coarse-grained atomic sequence information and fine-grained atomic spatial relationship information. Due to the scarcity of existing LNPs experimental data, we find that pretraining the molecular model is crucial for better understanding the task of predicting LNPs properties, which is achieved through reconstructing atomic 3D coordinates and masking atom predictions. In addition, the issue of data imbalance is particularly prominent in the real-world exploration of LNPs. We introduce the BalMol block to solve this problem by smoothing the distribution of labels and molecular features. Our approach outperforms state-of-the-art works in transfection property prediction under both random and scaffold data splitting. Additionally, we establish a relationship between molecular structural similarity and transfection differences, selecting 4267 pairs of molecular transfection cliffs, which are pairs of molecules that exhibit high structural similarity but significant differences in transfection efficiency, thereby revealing the primary source of prediction errors. The code, model and data are made publicly available at https://github.com/wklix/TransLNP.

Analisis Kebijakan Program Rumah Gratis Siap Huni pada Koperasi Syariah Benteng Mikro Indonesia dalam Perspektif Ekonomi Islam

This study aims to determine the policy of the free housing program ready for habitation and to determine the free housing program ready for habitation from an Islamic point of view and to determine the relationship between the free housing program ready for habitation in the Benteng Micro Indonesia Sharia cooperative and Islamic economics. The type of research used is qualitative research with a phenomenological approach that is by using primary data in the form of interviews and observations, as well as documentation then secondary data in the form of collection in the form of journals, scientific papers, dictionaries, relevant reading literature. The results of the study show that the ready-to-live-in housing program run by the Benteng Micro Indonesia sharia cooperative is really provided free of charge to its members and the general public without incurring any costs for development from the beneficiaries. In order to get a free, ready-to-live house run by the Benteng Micro Indonesia Sharia cooperative, prospective beneficiaries must follow a policy by fulfilling specific criteria and conditions that have been determined by the director of the Benteng Micro Indonesia Sharia cooperative. Then Islamic economic experts argued that the free housing program is permissible in Islam, because the free housing program is a commendable social activity to help the poor in meeting their basic needs, one of which is housing. As well as the free housing program, which is ready for habitation, is very closely related to the Islamic economy because every economic development, especially for the benefit of Muslims, will relate to and also affect the Islamic economy

Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks. However, deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. This paper provides an overview of model reprogramming to bridge this gap. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing and reusing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning, where the source and target domains can be vastly different. In many applications, model reprogramming outperforms transfer learning and training from scratch. This paper elucidates the methodology of model reprogramming, summarizes existing use cases, provides a theoretical explanation of the success of model reprogramming, and concludes with a discussion on open-ended research questions and opportunities.