Branding Strategies of Born Global A Case Study of a Finnish Firm

Born globals are special kind of firms that expands into the global markets quite rapidly after their inception. Branding strategies of born globals have not been explored up to sufficient extent in the literature. For this reason the main purpose of this study is to explore how born global firm utilizes branding strategies during their growth stages. Three objectives have been discussed in this regard. The first objective is to define born global concept and different growth stages of born-global firm. Second objective is to identify the brand decision process and to study different types of branding strategies. Last objective is to empirically explore branding strategies of a Finnish born global during its growth stages. Born global concept, their growth stages and discussions related to branding strategies are done theoretically. This study follows deductive research approach and qualitative research method. The data is collected from a single company through face to face interview technique. In addition to interview, the company website, annual reports, company global financial data and company partner websites have also been consulted for the purpose of data collection. This study focuses on a successful Finnish born global firm which has been rapidly internationalizing in the information technology sector of global markets. Finding reveals that selected born global firm chooses product brand strategy to make its brand successful during its introduction and growth stage. The company has not yet reached to maturity and still considered to be in the growth stage. However it is not always possible to use a single brand strategy in all situations.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Anomaly Detection In Blockchain

Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers. However, with the advancements in the financial domain such as blockchain and artificial intelligence, it is more challenging to deceive financial systems. Despite these technological advancements many fraudulent cases have still emerged. Many artificial intelligence techniques have been proposed to deal with the anomaly detection problem; some results appear to be considerably assuring, but there is no explicit superior solution. This thesis leaps to bridge the gap between artificial intelligence and blockchain by pursuing various anomaly detection techniques on transactional network data of a public financial blockchain named 'Bitcoin'. This thesis also presents an overview of the blockchain technology and its application in the financial sector in light of anomaly detection. Furthermore, it extracts the transactional data of bitcoin blockchain and analyses for malicious transactions using unsupervised machine learning techniques. A range of algorithms such as isolation forest, histogram based outlier detection (HBOS), cluster based local outlier factor (CBLOF), principal component analysis (PCA), K-means, deep autoencoder networks and ensemble method are evaluated and compared

Blockchain based auditable access control for distributed business processes

The use of blockchain technology has been proposed to provide auditable access control for individual resources. However, when all resources are owned by a single organization, such expensive solutions may not be needed. In this work we focus on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains can provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may be overlapping in terms of the component conditions/rules, and simply using existing solutions would result in repeated evaluation of user’s authorization separately for each resource, leading to significant overhead in terms of cost and computation time over the blockchain. To address this challenge, we propose an approach that formulates a constraint optimization problem to generate an optimal composite access control policy. This policy is in compliance with all the local access control policies and minimizes the policy evaluation cost over the blockchain. The developed smart contract(s) can then be deployed to the blockchain, and used for access control enforcement. We also discuss how the access control enforcement can be audited using a game-theoretic approach to minimize cost. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and experimentally validated the effectiveness and efficiency of our approach

Blockchain based auditable access control for business processes with event driven policies

The use of blockchain technology has been proposed to provide auditable access control for individual resources. Unlike the case where all resources are owned by a single organization, this work focuses on distributed applications such as business processes and distributed workflows. These applications are often composed of multiple resources/services that are subject to the security and access control policies of different organizational domains. Here, blockchains provide an attractive decentralized solution to provide auditability. However, the underlying access control policies may have event-driven constraints and can be overlapping in terms of the component conditions/rules as well as events. Existing work cannot handle event-driven constraints and does not sufficiently account for overlaps leading to significant overhead in terms of cost and computation time for evaluating authorizations over the blockchain. In this work, we propose an automata-theoretic approach for generating a cost-efficient composite access control policy. We reduce this composite policy generation problem to the standard weighted set cover problem. We show that the composite policy correctly captures all the local access control policies and reduces the policy evaluation cost over the blockchain. We have implemented the initial prototype of our approach using Ethereum as the underlying blockchain and empirically validated the effectiveness and efficiency of our approach. Ablation studies were conducted to determine the impact of changes in individual service policies on the overall cost

Anomaly Detection In Blockchain

Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers. However, with the advancements in the financial domain such as blockchain and artificial intelligence, it is more challenging to deceive financial systems. Despite these technological advancements many fraudulent cases have still emerged. Many artificial intelligence techniques have been proposed to deal with the anomaly detection problem; some results appear to be considerably assuring, but there is no explicit superior solution. This thesis leaps to bridge the gap between artificial intelligence and blockchain by pursuing various anomaly detection techniques on transactional network data of a public financial blockchain named 'Bitcoin'. This thesis also presents an overview of the blockchain technology and its application in the financial sector in light of anomaly detection. Furthermore, it extracts the transactional data of bitcoin blockchain and analyses for malicious transactions using unsupervised machine learning techniques. A range of algorithms such as isolation forest, histogram based outlier detection (HBOS), cluster based local outlier factor (CBLOF), principal component analysis (PCA), K-means, deep autoencoder networks and ensemble method are evaluated and compared

Recent advances in water falling film reactor designs for the removal of organic pollutants by advanced oxidation processes: A review

Chemical wastewater from industrial and urban activities is a major environmental concern. Advanced oxidation processes (AOPs) have emerged as efficient techniques for the removal of persistent organic pollutants from wastewater. AOPs generate highly reactive hydroxyl radicals (•OH) that effectively degrade and mineralize a wide range of organic contaminants in aqueous solutions. Research is ongoing to find simple and efficient reactor designs for AOPs. Water falling film (WFF) reactor designs have been effectively utilized for the removal of various organic pollutants from wastewater. This review provides an overview of the development and application of WFF reactor designs for organic pollutants degradation by various AOPs. This work summarizes recent studies on treating organic pollutants, highlights current challenges in applying WFF reactors for water treatment using AOPs, and proposes future research directions. The review aims to guide researchers and stimulate further investigations into practical applications of WFF reactors in wastewater treatment

Oxidative Desulfurization of Real High-Sulfur Diesel Using Dicarboxylic Acid/H2O2 System

From the perspective of pollution, economics, and product quality, it is very important to find an efficient way to minimize the sulfur content of petroleum products such as gasoline and diesel. In this work, an effective, inexpensive, and simple oxidative desulfurization system based on hydrogen peroxide activation by three dicarboxylic acids which have different carbon numbers (i.e., malonic acid, succinic acid, and glutaric acid) was utilized for the desulfurization of a real diesel sample with high organic sulfur-containing compounds. The desulfurization process was based on the oxidation of sulfur compounds in diesel fuel to the corresponding sulfones followed by acetonitrile extraction of the sulfones. To select the optimal experimental conditions, the effects of several parameters, including temperature, catalyst H2O2 dosages, and treatment time, were investigated. The results showed that the developed system was effective in desulfurizing real diesel fuel with high sulfur content. With an initial total sulfur content of about 8104 mg/L, the desulfurization rate from the diesel sample reached more than 90.9, 88.9, and 93%, using malonic acid, succinic acid, and glutaric acid, respectively. The optimum parameters such as reaction temperature, reaction time, H2O2 (50 w/w%), and carboxylic acid dosage for oxidative desulfurization were determined to be 95 °C, 6 h, 10 mL, and 0.6 g, respectively. The conversion of refractory sulfur compounds into extractable sulfone forms was verified using gas chromatography. Moreover, the kinetic study confirmed that the designed reaction system follows the pseudo-first-order kinetic model

An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods

Orchestration and management of adaptive IoT-centric distributed applications

Current Internet of Things (IoT) devices provide a diverse range of functionalities, ranging from measurement and dissemination of sensory data observation, to computation services for real-time data stream processing. In extreme situations such as emergencies, a significant benefit of IoT devices is that they can help gain a more complete situational understanding of the environment. However, this requires the ability to utilize IoT resources while taking into account location, battery life, and other constraints of the underlying edge and IoT devices. A dynamic approach is proposed for orchestration and management of distributed workflow applications using services available in cloud data centers, deployed on servers, or IoT devices at the network edge. Our proposed approach is specifically designed for knowledge-driven business process workflows that are adaptive, interactive, evolvable and emergent. A comprehensive empirical evaluation shows that the proposed approach is effective and resilient to situational changes

Volumetric Calculation of Generated Hydrocarbon from Sargelu Formation in Kurdistan Region, Iraq

In attempt to determine the amount of hydrocarbon generated from Sargelu Formation, the outcrop and bore hole samples were used. The areal distribution, density, and weight of Sargelu Formation were determined by using traditional methods and using geographic information system. The amount of hydrocarbon generated was determined to be 3.4199 x 1012 kg