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Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get an explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural calamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon hybrid approach using Levenshtein distance, demetaphone algorithm and dictionary mapping is proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords are further validated from the search engine results at later time for detection of events.

In developing countries like Tanzania, gems and jewellery industry mainly consists of disintegrated and unstable micro and small workshops which operate in a way that misalign value addition processes. This study is aimed to bridge gap by focussing on exploitation of industrial clusters in social normalisation and economic resilience to developing countries. The world economic shocks has been not only individually experienced but also globally shared while disrupted lives across all countries and communities and negatively affected global socio-economic growth.

Furthermore, the explorative design was adopted in this study in order to explore needs of respondents, and with the aim to direct the study towards a descriptive design. The sample frame consists of participants in gems and jewellery activities in Tanzania whereby sample was drawn from Dar es Salaam and Arusha. Semi-structured interview was used to collect quantitative data to establish evidence of Tanzanians’ SSJs linked to global value chains (GVCs).

Results revealed the benefits of exploitation of artisanal industrial clusters to Tanzanians’ SSJs when linked to global value chains (GVCs). Findings of the study demonstrate the importance of artisanal industrial clusters in facilitating Tanzanians’ SSJs to access GVCs. Further, insufficient education, trust and social protection directly affects inclusive GVCs, inferring that the impact of artisanal industrial clusters on inclusive GVCs in social normalisation and economic resilience.

Study findings reveals shortcomings in existing regulatory framework of linking Tanzanians’ SSJs to artisanal industrial clusters, for improvements to better support the inclusiveness in GVCs. Findings of this research invite interventions on institutional capabilities and entrepreneurial competencies to enhance the capabilities of small-scale jewellers (SSJs). Like other studies, this study involved cross-sectional data, limit targeted study population as representative of SSJs in industrial clusters and GVCs in economic crises at limited time.

The study findings makes important practical contributions to the Tanzania’s SSJs by examining mediating role of artisanal industrial clusters hence informing policymakers of mining sector how to improve accessibility on GVCs by focus on offering great institutional capabilities and entrepreneurial competencies. These findings will help SSJs and policy makers to get better understanding of the relationships in exploitation of artisanal industrial clusters when accessing GVCs. Therefore, they can make better decisions on implementing artisanal industrial clusters as well as management accessing GVCs, so that SSJs will attain the best possible performance.

This emphasises the importance of community empowerment in the GVCs process through artisanal industrial clusters. Study findings indicate the influence of industrial relations to social dynamics which are previously inadequately addressed and scantly researched. In actual fact study propose initiatives that ensure local communities benefit socially from the integration of SSJs into GVCs through artisanal industrial clusters. Findings suggest local communities that take into account inter-sectionality of artisanal industrial clusters and inclusive GVCs, by considering how factors like education, trust and social protection status intersect to influence the social inclusiveness of SSJs.

There is limited evidence of linking Tanzanians’ SSJs to GVCs in social normalisation and economic resilience and few researchers have explored this topic. This article leverages exploitation of industrial clusters in normalisation and economic resilience to developing countries such as Tanzania as way of improving shared prosperity, sustainability, inclusive growth, cohesion, value chain upgrading and financial inclusion to SSJs.

We study the contemporaneous effects of US monetary policy normalization on African stock market using panel data from six African countries.

Daily data from May 1, 2013 to December 31, 2018 were used in order to accommodate the announcement effects since the US monetary policy normalization announcement was made in May 2013, while the rate hike was in December 2015. The study used the FE, RE and PMG models.

The results revealed that US 10-year bond yield and Treasury bill rate shocks negatively affect stock prices in Africa. S$P500 shock positively affects African stock prices.The result revealed that the integration of African financial market to the global financial market is a major source of vulnerability. The finding that US Treasury bill rate is a major depressant of the African stock prices reveals the short-termism of foreign polio inflows into African economies.

We provide inexorably insight into the interplay of financial systems globally. It can be useful for the purposes of generalization in developing economies in the shape of African countries. More so, this study could be replicated in another economic bloc or region with the aim of further exposing the far-reaching spillover effects of the US monetary policy normalization.

This paper explores the role that the control system – understood as a set of financial and non-financial mechanisms – introduced by the Ministerial Decree of 15th February 1860 played in promoting the ethical tolerance of prostitution in the Kingdom of Italy.

A qualitative research method was adopted. Specifically, this study draws on literature on accounting and deviant behaviors and on Suchman's theories of legitimation (1995) to interpret empirical evidence collected from archival primary sources as well as secondary sources.

The paper highlights how the accounting mechanisms introduced by the law were molded to limit the serious consequences of prostitution from a public health standpoint and to demonstrate that the State neither profited from prostitution nor used public money to fund it. This should have stimulated ethical tolerance of the law itself and, consequently, of the prostitution that was regulated.

This paper opens a new research avenue in the field of accounting history by exploring the connection between accounting and prostitution. Moreover, unlike the extant literature on accounting and deviant behaviors, this study delves into the role played by accounting mechanisms to promote ethical tolerance rather than to activate normalization processes.

The purpose of this paper is to describe and theorize the type of hate-love relationship to performance measurement systems (PMSs) that individual researchers tend to develop in academia. To this end, the paper draws upon Foucault’s writings on neoliberalism to analyse PMSs as neoliberal technologies holding certain qualities that can be expected to elicit such ambivalent views.

The paper is based on a qualitative interview study of researchers from three Swedish universities, who were asked to reflect upon questions related to three overall themes, namely, what it means to be a researcher in contemporary academia, the existence and use of PMSs at their universities and if/how such PMSs affected them and their work as researchers.

The empirical findings show that the hate-love relationship can be understood in terms of how PMSs are involved in three central moments of governmentality, where each such moment of governmentality tends to elicit feelings of ambivalence among researchers due to how PMSs rely on: a restricted centrifugal mechanism, normalization rather than normation and a view of individual academics as entrepreneurs of themselves.

Existing literature has provided several important insights into how the introduction and use of PMSs in academia tend to result in both negative and positive experiences and reactions. The current paper adds to this literature through theorizing how and why PMSs may be expected to elicit such ambivalent experiences and reactions among individual researchers.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

The purpose of the study is to explore, with anchorage in theories about the normalization of corruption, under what conditions blockchain technology can mitigate corruptive practices of multinational enterprises (MNEs) in emerging markets (EMs).

By synthesizing a technological perspective and theory on corruption, the authors examine the feasibility of blockchain for fighting corruption in MNEs’ business operations in EMs.

Blockchain technology is theorized to have varying mitigating effects on the rationalization, socialization and institutionalization of corruption. The authors provide propositions describing the effects and the limitations of blockchain for mitigating corruption in EMs.

This paper offers a perspective for how to tackle acute business problems and social problems pronounced in international business but also prevailing elsewhere.

The study contributes to literature in international management by systematically exploring how and under what conditions blockchain can mitigate the normalization of corruption.

The purpose of this paper is to develop a new method to extract medical temporal information from online health communities.

The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words.

For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult to identify when it involves a relative TIMEX or a hypothetical concept.

The authors proposed a new method to extract temporal information from the online clinical data and evaluated the usefulness of different level of syntactic features in this task.