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Research summary: The recent surge of interest in \"ecosystems\" in strategy research and practice has mainly focused on what ecosystems are and how they operate. We complement this literature by considering when and why ecosystems emerge, and what makes them distinct from other governance forms. We argue that modularity enables ecosystem emergence as it allows a set of distinct yet interdependent organizations to coordinate without full hierarchical fiat. We show how ecosystems address multilateral dependences based on various types of complementarities—supermodular or unique, unidirectional or bidirectional—which determine the ecosystem's value-add. We argue that at the core of ecosystems lie nongeneric complementarities, and the creation of sets of roles that face similar rules. We conclude with implications for mainstream strategy and suggestions for future research. Managerial summary: We consider what makes ecosystems different from other business constellations, including markets, alliances, or hierarchically managed supply chains. Ecosystems, we posit, are interacting organizations, enabled by modularity, not hierarchically managed, bound together by the nonredeployability of their collective investment elsewhere. Ecosystems add value as they allow managers to coordinate their multilateral dependence through sets of roles that face similar rules, thus obviating the need to enter into customized contractual agreements with each partner. We explain how different types of complementarities (unique or supermodular, generic or specific, uni- or bi-directional) shape ecosystems and offer a \"theory of ecosystems\" that can explain what they are, when they emerge, and why alignment occurs. Finally, we outline the critical factors affecting ecosystem emergence, evolution, and success—or failure.

We cannot overemphasize the essence of contextual information in most natural language processing (NLP) applications. The extraction of context yields significant improvements in many NLP tasks, including emotion recognition from texts. The paper discusses transformer-based models for NLP tasks. It highlights the pros and cons of the identified models. The models discussed include the Generative Pre-training (GPT) and its variants, Transformer-XL, Cross-lingual Language Models (XLM), and the Bidirectional Encoder Representations from Transformers (BERT). Considering BERT’s strength and popularity in text-based emotion detection, the paper discusses recent works in which researchers proposed various BERT-based models. The survey presents its contributions, results, limitations, and datasets used. We have also provided future research directions to encourage research in text-based emotion detection using these models.

With the rise of online social networking, social relationships are increasingly developed and maintained in a digital domain. Drawing conclusions about the impact of the digital world on loneliness is difficult because there are contradictory findings, and cross-sectional studies dominate the literature, making causation difficult to establish. In this review, we present our theoretical model and propose that there is a bidirectional and dynamic relationship between loneliness and social Internet use. When the Internet is used as a way station on the route to enhancing existing relationships and forging new social connections, it is a useful tool for reducing loneliness. But when social technologies are used to escape the social world and withdraw from the “social pain” of interaction, feelings of loneliness are increased. We propose that loneliness is also a determinant of how people interact with the digital world. Lonely people express a preference for using the Internet for social interaction and are more likely to use the Internet in a way that displaces time spent in offline social activities. This suggests that lonely people may need support with their social Internet use so that they employ it in a way that enhances existing friendships and/or to forge new ones.

Since surface defect detection is significant to ensure the utility, integrality, and security of productions, and it has become a key issue to control the quality of industrial products, which arouses interests of researchers. However, deploying deep convolutional neural networks (DCNNs) on embedded devices is very difficult due to limited storage space and computational resources. In this paper, an efficient lightweight convolutional neural network (CNN) model is designed for surface defect detection of industrial productions in the perspective of image processing via deep learning. By combining the inverse residual architecture with coordinate attention (CA) mechanism, a coordinate attention mobile (CAM) backbone network is constructed for feature extraction. Then, in order to solve the small object detection problem, the multi-scale strategy is developed by introducing the CA into the cross-layer information flow to improve the quality of feature extraction and augment the representation ability on multi-scale features. Hereafter, the multi-scale feature is integrated to design a novel bidirectional weighted feature pyramid network (BWFPN) to improve the model detection accuracy without increasing much computational burden. From the comparative experimental results on open source datasets, the effectiveness of the developed lightweight CNN is evaluated, and the detection accuracy attains on par with the state-of-the-art (SOTA) model with less parameters and calculation.

Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.

Research summary: In this study, we revisit the relationship between corporate social performance (CSP) and corporate financial performance (CFP) by conducting a replication of Waddock and Graves (1997). Using 1990 KLD ratings as the CSP measure, the original study reports a positive bidirectional relationship between CSP and CFP. However, our replication analyses with a larger sample over a longer time period indicate that the findings of the original study may not be generalizable to different samples. We argue that our replication casts doubt on the original study and can serve as a starting point to reconsider the CSP-CFP relationship. Based on the findings of our replication, we discuss the differences between the replication results and the original findings, and then suggest several approaches to revise and extend the original study. Managerial summary: Advocates of corporate social performance (CSP) have long argued that \"doing good leads to doing well.\" However, the evidence to support this argument is not strongly convincing, and managers hence doubt whether better CSP leads to improved corporate financial performance (CFP). In this article, we directly examine the relationship between CSP and CFP. Our article reports that CSP may not have a positive influence on CFP. Instead, our article shows the complexity of the relationship between CSP and CFP. Therefore, we cannot simply argue that doing good will necessarily lead to doing well.

BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, including its structure, utilization in different NLP tasks, and the further development of its design via modifications. The study thoroughly analyses the methodological aspects, conducting a comprehensive analysis of the planning process, the implemented procedures, and the criteria used to decide which data to include or exclude in the evaluation framework. In addition, the study thoroughly examines the influence of BERT on several NLP tasks, such as Sentence Boundary Detection, Tokenization, Grammatical Error Detection and Correction, Dependency Parsing, Named Entity Recognition, Part of Speech Tagging, Question Answering Systems, Machine Translation, Sentiment analysis, fake review detection and Cross-lingual transfer learning. The review study adds to the current literature by integrating ideas from multiple sources, explicitly emphasizing the problems and prospects in BERT-based models. The objective is to comprehensively comprehend BERT and its implementations, targeting both experienced researchers and novices in the domain of NLP. Consequently, the present study is expected to inspire more research endeavors, promote innovative adaptations of BERT, and deepen comprehension of its extensive capabilities in various NLP applications. The results presented in this research are anticipated to influence the advancement of future language models and add to the ongoing discourse on enhancing technology for understanding natural language.

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Machine Reading Comprehension (MRC) of a document is a challenging problem that requires discourse-level understanding. Information extraction from scholarly articles nowadays is a critical use case for researchers to understand the underlying research quickly and move forward, especially in this age of infodemic. MRC on research articles can also provide helpful information to the reviewers and editors. However, the main bottleneck in building such models is the availability of human-annotated data. In this paper, firstly, we introduce a dataset to facilitate question answering (QA) on scientific articles. We prepare the dataset in a semi-automated fashion having more than 100k human-annotated context–question–answer triples. Secondly, we implement one baseline QA model based on Bidirectional Encoder Representations from Transformers (BERT). Additionally, we implement two models: the first one is based on Science BERT (SciBERT), and the second is the combination of SciBERT and Bi-Directional Attention Flow (Bi-DAF). The best model (i.e., SciBERT) obtains an F1 score of 75.46%. Our dataset is novel, and our work opens up a new avenue for scholarly document processing research by providing a benchmark QA dataset and standard baseline. We make our dataset and codes available here at https://github.com/TanikSaikh/Scientific-Question-Answering.

This paper develops a theory of socially determined aspirations, and the interaction of those aspirations with growth and inequality. The interaction is bidirectional: economy-wide outcomes determine individual aspirations, which in turn determine investment incentives and social outcomes. Thus aspirations, income, and the distribution of income evolve jointly. When capital stocks lie in some compact set, steady state distributions must exhibit inequality and are typically clustered around local poles. When sustained growth is possible, initial histories matter. Either there is convergence to an equal distribution (with growth) or there is perennial relative divergence across clusters, with within-cluster convergence. A central feature that drives these results is that aspirations that are moderately above an individual's current standard of living tend to encourage investment, while still higher aspirations may lead to frustration.