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The literature on the influence of audit committees (AC) and cosmetic accounting (CA) is scarce. AC plays a unique and vital role in boosting earnings reliability in countries with weaker application of accounting standards or weaker legal protection for investors. AC, therefore, are considered to be one of the essential tools available to directors in supervising management decisions regarding financial reporting. This paper aims to examine the influence of AC characteristics (ACC) on CA and how this relationship is moderated by the audit fee.

This study used probit regression to analyze 1,218 firm-year observations of listed companies in Tehran Stock Exchange from 2014 to 2020.

The results show that AC financial accounting expertise, AC independence, female AC membership and AC tenure were negatively related to CA. The negative relationship is highly pronounced when a firm incurs higher audit fees, and audit fees moderate the relationship between ACC and CA. Results for the robustness checks show that only AC independence was significant, and the results of other characteristics were not significant.

This research was conducted in an Iranian setting where the formation of ACs is on the verge of regulation; therefore, the data used for the study only contains the seven-year period of ACs’ statutory activity. In addition, a lack of consensus on the precise measures of an AC’s effectiveness could be considered as a restrictive factor.

The findings provide an initial insight into the effect AC on CA and moderating effect of audit fee on the relationship between ACC and CA.

This largely conceptual study aims to draw from the author’s experience of conversations with Svalbard’s educators, lessons for international higher education institutions’ engagement with climate change education and thinking for non-specialists.

In situ discussions with Svalbard’s educators informed the theoretical work of the author towards the development of conceptual conclusions. The theoretical frame used – “Red Biocentrism” – draws on both radical left and green thought to posit an emplaced, materialist understanding of author’s, participants’ and place’s intra-related contributions.

That, insofar as universities represent nodes in an ethical ecology, they have a capacity to realise that which is obvious in Svalbard – their role as embassies for their learning places, generative of spokespeople or ambassadors.

There is sparse published research into the work of Svalbard’s climate educators, as a pedagogical project undertaken under such extreme and rapidly changing environmental conditions. This study represents the first to reflect on what can be learnt from the educators of Svalbard by Universities elsewhere.

The purpose of this article is to provide information about interactions between pink-pigmented facultative methylotroph (PPFM) organisms and plants, their molecular mechanisms of methylotrophic metabolism, application of PPFMs in agriculture, biotechnology and bioremediation and also to explore lacuna in PPFMs research and direction for future research.

Research findings on PPFM organisms as potent plant growth promoting organisms are discussed in the light of reports published by various workers. Unexplored field of PPFM research are detected and their application as a new group of biofertilizer that also help host plants to overcome draught stress in poorly irrigated crop field is suggested.

PPFMs are used as plant growth promoters for improved crop yield, seed germination capacity, resistance against pathogens and tolerance against drought stress. Anti-oxidant and UV resistant properties of PPFM pigments protect the host plants from strong sunshine. PPFMs have excellent draught ameliorating capacity.

To meet the ever increasing world population, more and more barren, less irrigated land has to be utilized for agriculture and horticulture purpose and use of PPFM group of organisms due to their draught ameliorating properties in addition to their plant growth promoting characters will be extremely useful. PPFMs are also promising candidates for the production of various industrially and medicinally important enzymes and other value-added products. Wider application of this ecofriendly group of bacteria will reduce crop production cost thus improving economy of the farmers and will be a greener alternative of hazardous chemical fertilizers and fungicides.

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Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

The purpose of this study is to investigate the association between three corporate governance (CG) idiosyncrasies, namely audit committee characteristics, external audit quality (AQ), board diversity and firm performance (FP) in the South Asian Association for Regional Cooperation (SAARC) nations.

The study used a sample of 200 listed nonfinancial firms in the SAARC nations from 2012 to 2021. The System Generalized Method of Moment model was applied to the data consisting of 2000 firm-year observations. The Generalized Estimating Equation population-averaged model was also employed for added robustness. The study employed Tobin's Q as the measure of FP.

The findings revealed that amongst the CG variables tested, external AQ exhibited a significantly positive relationship with Tobin's Q. Significant negative influences on FP have been demonstrated by the variables of audit committee meeting and board's independence. Furthermore, gender diversity, CEO duality, audit committee strength and independence failed to record any significant association.

This study is one of the first to investigate the association between CG idiosyncrasies and FP in the SAARC nations. The study findings have important implications for policymakers and regulators in the region.

This paper aims to conceptualize a framework drawn upon the self-regulation theory to study the effect of system quality features on the continuous usage intention of social network users. The study explores the relationship among the selected variables and identifies the determinants of continuous usage intention of social networks.

A survey method was used to collect data from 301 male and 311 female social network users to test the research model with the help of the structural equation modelling technique.

The results show a difference between male and female social media users for the continuous usage intention of social networks. A substantial difference in the relationship was seen between the attitude and continuous usage intention, where female social network users had a more robust and significant relationship than their male counterparts.

This study uses the self-regulation theory to understand the continuous usage intention of social networks in the Indian context. This research study contributes to the existing literature on social networks/social media and the adoption intention.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The authors analyze the association between the functional background of the compensation committee chair and CEO compensation. The analysis is motivated by the continuing debate about the reasonableness of executive pay patterns and the growing emphasis on the role of compensation committees.

The authors define three expert categories—accounting, finance, and generalist—and collect data on the compensation committee (CC) chairs of the S&P 500 firms from 2008 to 2018. The authors run an ordinary least square model and regress CEO total and cash compensation on the three expert categories.

The authors find that firms in which the CC chair has expertise in accounting, finance, and general business favor performance measures that are more aligned with accounting, finance, and general business, respectively. There is little evidence that CC chairs who are CEOs of other firms endorse more generous pay for the host CEO; the authors find some evidence that CC chairs tenure relative to the host CEO's is negatively associated with the level of the CEO's pay.

This study suggests that firms and regulators should consider the background of the compensation committee chair to understand the variations in top executive.

Companies desiring to link executive compensation to particular areas of strategy must also consider matching the functional background of the compensation committee chair with the target strategy areas. From regulatory standpoint, requiring compensation committees to operate independent of inside directors can reduce attempts by inside directors to skim the process, but a failure to also consider the impact of compensation committees' discretion over the pay-setting process can distort the executives' pay-performance relation.

This is the first study to examine the effects of the functional background of the compensation committee chair on CEO compensation.

In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance.

SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments.

We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively.

In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.

This study aims to examine how audit committee (AC) attributes influence quality and quantity disclosure of energy consumed by the listed nonfinancial firms for the period of five years (2016–2020). The study aims at providing empirical evidence on how board of director’s independence influences the relationship between AC attributes and firms’ energy in achieving sustainable development goals (SDGs) on world climate policy.

The study obtained data from a sample of 83 listed nonfinancial firms, content analysis technique was used to compute energy disclosure indexes using global reporting initiative standards, while regression analysis was conducted to test the relationship among research variables.

The study revealed that AC independence, diversity and meetings were significantly related with energy disclosure. Also, the study found that other variables were insignificantly related with energy disclosure.

The study is constrained for not considering all listed firms in the country. Furthermore, the study considered selected attributes, other important audit-committee size attributes such as audit-committee size, audit-committee size tenure could be study in by the future study.

The study’s findings would have practical implications for corporations and other business organizations seeking to actively involve the energy-related SDGs 7 and 13 in their business models and successfully communicate these efforts to stakeholders.

To the best of author’s knowledge, this is the first study that provides empirical evidence on the effect of AC attributes on the energy disclosure using effect of board independence as moderator in Nigeria.