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Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a 'heavy' model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature (τ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter (τ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%-however, the stochastic approach reduces the energy consumption and CO2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO2 emission each by a factor of 18.5.

The obligation of ‘iddah for divorced women has been under rule of Islam. Meanwhile, it is also obligation for men to provide his divorced women a material support during ‘iddah period. It aims to facilitate her for living survival after the firstly time divorce. However, the rule possibly changes if the divorce is condemned in terms of thalaq ba’in, a condition in which the men takes three-divorce or state the third divorce to women with its implication to the prohibition for getting remarriage except only if the women has been under married by other men. For this case, the four ulama’s (with four schools of thought) have different decisions, but Imam Ahmad ibn Hambal determined that there is no obligation for providing material support for the divorced woman with thalaq ba’in. The enforcement of this law is based on the hadith as narrated by Asy-Sya’bi from Fathiman binti Qais from Rasulullah on the women with thalaq ba’in.

This study aims at determining the position of Islamic law in the culture of Malay society in Jambi, Sumatera. The obvious facts in the previous studies about Islamic law and culture in Malay Sumatera is heavy emphasis on Minangkabau of West Sumatera with its matrilineal culture and traditionalist-modernist tension. In contrast, Jambi, which is adjacent to Minangkabau region, is starkly different. Jambi culture does not recognize matrilineal culture, except for Minangkabau people migrating to Jambi, nor experience such a tension. This is evidenced from interviews and library sources alike. Interviews were undertaken from several members of traditional leaders including some of the decendents of Jambi Malay Kingdom. As the result, even though there was a was among devotees of each of the laws, Islamic law (Syarak) and customary law in greater Malay Sumatera, the aphorism “Customary laws are based on religious laws; religious laws are based on the Holy Book” unites them, and becomes the living law that rules Muslim society in Jambi from the past until present day.

In this paper we describe a preliminary, work-in-progress Spoken Language Understanding Software (SLUS) with tailored feedback options, which uses interactive spoken language interface to teach Iraqi Arabic and culture to second language learners. The SLUS analyzes input speech by the second language learner and grades for correct pronunciation in terms of supra-segmental and rudimentary segmental errors such as missing consonants. We evaluated this software on training data with the help of two native speakers, and found that the software recorded an accuracy of around 70% in law and order domain. For future work, we plan to develop similar systems for multiple languages.

The emergence of sustainable business practices has garnered interest among stakeholders. However, the question of whether sustainability performance provides companies with a competitive advantage is still being debated in the literature. This paper aims to examine the influence of sustainability performance – namely economic sustainability performance and environmental, social, governance (ESG) – on competitive advantage, with the effectiveness of enterprise risk management (ERM) as the moderating variable. This paper used 202 firm-year observations during 2015–2022 from non-financial sector companies listed on the Indonesia Stock Exchange. To test the hypotheses, panel data regression with a one-year time-lag analysis is conducted. The findings show that economic sustainability performance has no relationship with competitive advantage, while ESG has a positive effect. Furthermore, ERM effectiveness strengthens the effect of economic sustainability and ESG on competitive advantage. Further investigation used a two-year time-lag analysis for a long-term perspective. The analysis shows that economic sustainability performance and ESG have a positive impact on competitive advantage. In contrast, ERM effectiveness has no effect on the relationship between economic sustainability performance and competitive advantage. Moreover, additional analysis incorporates the effect of COVID-19 into the main model and shows that the pandemic did not affect competitive advantage; this is consistent with the main results. The findings encourage companies to improve their risk management and sustainability initiatives. The government may also take it into account when developing rules that promote the implementation of sustainable development. AcknowledgmentThis research was supported by the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia through the Center for Higher Education Fund (BPPT) and Indonesia Endowment Funds for Education (LPDP) for providing the Indonesian Education Scholarship (BPI-Beasiswa Pendidikan Indonesia).

This paper aims to investigate the value relevance of ESG (environmental, social and governance) and earnings quality of companies listed on the Indonesia Stock Exchange during the period from 2012 to 2022. Furthermore, this study also investigates the value relevance of earnings and book value in the presence of ESG and earnings quality. The value relevance was operationalized using Ohlson's price model (1995), and data was extracted from the Refinitiv database. Based on 353 firm-years, the panel data analysis indicates that ESG and earnings quality have value relevance; this proves that information about ESG and earnings quality are used by investors and stakeholders as a basis for decision-making. Moreover, the presence of ESG has a negative impact on the value relevance of earnings but has a positive impact on the value relevance of book value. By contrast, with ESG, the presence of earnings quality has a positive impact on the value relevance of earnings but has a negative impact on the value relevance of book value, implying that when the quality of accounting information increases, users rely more on earnings information rather than book value. A robustness test testing was also carried out by conducting sub-sample tests based on the period during which the COVID-19 pandemic occurred during the research. Results show that before the pandemic, users tended to be more interested in ESG information, while during the pandemic, users tended to focus more on earnings quality information as a basis for decision-making.

This study explores the variables of workload, level of audit fees and audit risk on audit quality by testing the three research hypotheses using SPSS with multiple linear regression methods. The study was conducted at a Public Accounting Firm in East Java, Indonesia, with a population of 48 officially registered auditors. A questionnaire instrument was used to collect research data, containing questions/statements related to research variables tested for validity and reliability. The collected data were analyzed using multiple linear regression methods. The research results show that workload variables, audit fees, and risk levels significantly positively affect audit quality. The main research contribution of this study is aimed at practitioners and authorities or associations that, in the preparation and implementation of audit standards, must consider the auditor's internal and external factors and the client's real condition. The association needs to bridge the mistakes of the auditor or KAP when dealing with different client conditions. In addition, based on the results of this study, it is necessary to consider work overload and working time for auditors because they affect audit quality.

Research aims: The COVID-19 pandemic disrupted multiple industries, including chemistry, pharmaceuticals, telecommunications, and healthcare. This study, thus, explores how these sectors managed their earnings concerning political factors during the pandemic. Since these sectors play a critical role in maintaining business stability during this period, the authors hypothesize that some companies within them may have manipulated their profits. Design/Methodology/Approach: This study used quantitative methods to analyze a sample of companies in the chemical, pharmaceutical, telecommunications, and healthcare sectors listed on the Indonesia Stock Exchange from 2018 to 2022. Out of 60 companies, 20 met the criteria. The authors then tested the political costs hypothesis using Kotari et al.'s (2005) calculation method and a discrimination test for discretionary accrual values. Research findings: The findings reveal that some companies practiced earnings management with varying degrees across sectors. Notably, not all companies employed revenue reduction as a manipulation tactic. Significant differences were observed between the pre-pandemic and pandemic periods. It is worth highlighting that not all COVID-19 beneficiaries resorted to earnings management to access government incentives or facilities. Theoretical contribution/Originality: The findings of this research offer empirical evidence from both a theoretical standpoint and an analysis of the financial status of the involved companies. Practical/Policy Implication: This study aims to investigate if companies manipulated earnings during the COVID-19 pandemic by intentionally reducing their profits when their sectors were thriving due to the pandemic. Research Limitation: This research is limited by its use of quarterly data, incomplete data for some companies, and a reliance on existing data rather than current information collection.