The purpose of this study was to explore how a potential model of a tourism industry cluster could be developed in KwaZulu-Natal (KZN). To undertake this task an extensive literature review of cluster-based economic strategies was done. This was followed by a synopsis of the tourism industry from a global and South African perspective in order to determine issues of competitiveness and their impact on provincial dynamics. The study employed a qualitative research design and focused on the tourism industry in KwaZulu-Natal as a case study. Major stakeholders in the industry were interviewed. The main findings of the study are that KZN has the necessary preconditions for existence of a potential tourism cluster, and these conditions make it ripe for the cluster to be activated and developed. Activating and developing a cluster would bring home major benefits. The study highlighted various tools and mechanisms which could be used to analyse the province’s cluster map, and a model cluster map is also suggested based on contributions from different respondents. Using Porter’s Diamond Model, the competitiveness of the KZN tourism industry was assessed. Overall, it has been found that the province’s tourism industry possesses a mixture of resources and capabilities, which could be capitalised upon to developing the industry in future. However, glaring weaknesses are also exposed, which need to be dealt with urgently. In particular, crime and grime, together with the lack of tourism infrastructure to attract the high-end of the market, are seen as huge liability for the industry. A major contribution of this study is in identifying strategic management challenges that cluster studies have not addressed previously. The study also highlighted important critical success factors for cluster development and the drivers for change. The presence of some of these factors contributes to making the future prospect of the tourism industry in KZN to look bright. The study concludes by recommending that a tourism cluster be activated and developed in KZN and this process be led by an Independent Cluster Facilitator, who must be appointed by the Member of the Executive Committee responsible for Finance and Economic Development in the province in consultation with industry leaders.

The purpose of this study was to explore how a potential model of a tourism industry cluster could be developed in KwaZulu-Natal (KZN). To undertake this task an extensive literature review of cluster-based economic strategies was done. This was followed by a synopsis of the tourism industry from a global and South African perspective in order to determine issues of competitiveness and their impact on provincial dynamics. The study employed a qualitative research design and focused on the tourism industry in KwaZulu-Natal as a case study. Major stakeholders in the industry were interviewed. The main findings of the study are that KZN has the necessary preconditions for existence of a potential tourism cluster, and these conditions make it ripe for the cluster to be activated and developed. Activating and developing a cluster would bring home major benefits. The study highlighted various tools and mechanisms which could be used to analyse the province’s cluster map, and a model cluster map is also suggested based on contributions from different respondents. Using Porter’s Diamond Model, the competitiveness of the KZN tourism industry was assessed. Overall, it has been found that the province’s tourism industry possesses a mixture of resources and capabilities, which could be capitalised upon to developing the industry in future. However, glaring weaknesses are also exposed, which need to be dealt with urgently. In particular, crime and grime, together with the lack of tourism infrastructure to attract the high-end of the market, are seen as huge liability for the industry. A major contribution of this study is in identifying strategic management challenges that cluster studies have not addressed previously. The study also highlighted important critical success factors for cluster development and the drivers for change. The presence of some of these factors contributes to making the future prospect of the tourism industry in KZN to look bright. The study concludes by recommending that a tourism cluster be activated and developed in KZN and this process be led by an Independent Cluster Facilitator, who must be appointed by the Member of the Executive Committee responsible for Finance and Economic Development in the province in consultation with industry leaders.

In massive open online courses (MOOCs), a pressing need for an efficient automated approach of identifying keyphrases from MOOC video lectures has emerged. Because of the linear structure of MOOCs and the linear way in navigating the content of MOOCs, learners have difficulty to know the main knowledge addressed in MOOC video lectures and spend too much time navigating among to find the right content matching their learning goals. A feasible solution is automatic provision of keyphrases associated with MOOC video lectures that can help learners quickly identify a suitable knowledge and efficiently navigate to desired parts of MOOC video lectures without spending too much time to expedite their learning process. Keyphrases in MOOCs demonstrate three unique features: (1) low-frequency occurrence, (2) advanced scientific or technical concepts, and (3) late occurrence. Existing approaches to automatic keyphrases extraction (either supervised or unsupervised) do not consider these unique features, causing them to produce unsatisfactory performance when utilized to extract keyphrases from MOOC video lectures. In this dissertation, we propose $SemKeyphrase$, an unsupervised cluster-based approach for keyphrase extraction from MOOC video lectures. $SemKeyphrase$ incorporates a new semantic relatedness method and ranking algorithm, called $PhraseRank$. The proposed semantic relatedness method incorporates a novel metric that combines two scores ($WSem$ and $CSem$) to efficiently compute the semantic relatedness between candidate keyphrases in MOOCs. The $PhraseRank$ algorithm involves two phases when ranking candidate keyphrases: ranking clusters and reranking top candidate keyphrases. The first phase of $PhraseRank$ leverages the semantic relatedness of candidate keyphrases with regard to the subtopics of a MOOC video lecture to measure the importance of candidate keyphrases, which are further used to rank clusters of candidate keyphrases. Top candidate keyphrases from top-ranked clusters are then determined by a proposed selection strategy. The second phase of $PhraseRank$ reranks the top candidate keyphrases using a new ranking criterion and generates ranked top-K keyphrases as the final output. Experiment results on a real-world dataset of MOOC video lectures show that $SemKeyphrase$ outperforms other state-of-the-art methods.

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2007. / Submitted by Luis Felipe Souza (luis_felas@globo.com) on 2008-12-10T18:56:04Z No. of bitstreams: 1 Dissertacao_2007_EmersonMachado.pdf: 480909 bytes, checksum: 33454d8cde13ccd0274df91f48a4125d (MD5) / Approved for entry into archive by Georgia Fernandes(georgia@bce.unb.br) on 2009-03-04T12:18:48Z (GMT) No. of bitstreams: 1 Dissertacao_2007_EmersonMachado.pdf: 480909 bytes, checksum: 33454d8cde13ccd0274df91f48a4125d (MD5) / Made available in DSpace on 2009-03-04T12:18:48Z (GMT). No. of bitstreams: 1 Dissertacao_2007_EmersonMachado.pdf: 480909 bytes, checksum: 33454d8cde13ccd0274df91f48a4125d (MD5) / O objetivo geral desta pesquisa é analisar técnicas para aumentar a acurácia de classificadores construídos a partir de bases de dados desbalanceadas. Uma base de dados é desbalanceada quando possui muito mais casos de uma classe do que das outras, portanto possui classes raras. O desbalanceamento também pode ser em uma mesma classe se a distribuição dos valores dos atributos for muito assimétrica, levando à ocorrência de casos raros. Algoritmos classificadores são muito sensíveis a estes tipos de desbalanceamentos e tendem a valorizar as classes (ou casos) predominantes e a ignorar as classes (ou casos) de menor freqüência. Modelos gerados para bases de dados com classes raras apresentam baixa acurácia para estas classes, o que é problemático quando elas são classes de interesse (ou quando uma delas é a classe de interesse). Já os casos raros podem ser ignorados pelos algoritmos classificadores, o que é problemático quando tais casos pertencem à classe (ou às classes) de interesse. Uma nova proposição de algoritmo é o Cluster-based Smote, que se baseia na combinação dos métodos de Cluster-based Oversampling (oversampling por replicação de casos guiada por clusters) e no SMOTE (oversampling por geração de casos sintéticos). O método Cluster-based Oversampling visa melhorar a aprendizagem de pequenos disjuntos, geralmente relacionados a casos raros, mas causa overfitting do modelo ao conjunto de treinamento. O método SMOTE gera novos casos sintéticos ao invés de replicar casos existentes, mas não enfatiza casos raros. A combinação desses algoritmos, chamada de Clusterbased Smote, apresentou resultados melhores do que a aplicação deles em separado em oito das nove bases de dados utilizadas proposta nesta pesquisa. A outra abordagem proposta nesta pesquisa visa a diminuir a sobreposição de classes possivelmente provocada pela aplicação do método SMOTE. Intuitivamente, esta abordagem consiste em guiar a aplicação do SMOTE com a aprendizagem não supervisionada proporcionada pela clusterização. O método implementado sob esta abordagem, denominado de C-clear, resultou em melhora significativa em relação ao SMOTE em três das nove bases testadas e empatou nas demais. Foi também proposta uma nova abordagem para limpeza de dados baseada na aprendizagem não supervisionada, a qual foi incorporada ao C-clear. Esta limpeza somente surtiu melhora em uma base de dados, sendo este baixo desempenho oriundo possivelmente da escolha não adequada de seus parâmetros de limpeza. A aprendizagem destes parâmetros a partir dos dados ficou como trabalho futuro. ___________________________________________________________________________________________ ABSTRACT / It is intended in this work to research methods that improve the accuracy of classifiers applied to data set with class imbalance (high skew in class distribution causing rare classes) and within-class imbalance (high skew in data within-class distribution causing care cases). Standard classifier algorithms are strongly affected by these characteristics and their generated model are biased to the majority classes (or cases), in detriment of classes (or cases) underrepresented. Generally, models generated with imbalanced data set suffer from low accuracy for the minority classes, which is a problem when the target class is one of them. Eventually, rare cases are likely of being ignored by inductors, which is a problem when they belong to the interesting class (or classes). A new method is proposed in this work, Cluster-based Smote, which combines the methods Cluster-based Oversampling (oversampling by replication of positive cases guided by clusters) and SMOTE (Synthetic Minority Oversampling Technique). Cluster-based Oversampling addresses small disjuncts, but overfits the model to the training set. The method SMOTE addresses the overfit problem of random oversampling, but does not treat rare cases. The combination of them proposed in this research, named Cluster-based Smote, presented better results in eight out of nine datasets, compared to the applying of them all alone. Another approach proposed in this research aims at reducing the class overlap problem possibly caused by applying SMOTE. The main idea is to guide the SMOTE process by non-supervised learning (with clustering techniques). The method implemented under this approach, named Cclear, resulted in significant improvement over SMOTE in three out of nine datasets. A cleaning method based in the non-supervised learning was also proposed and has been incorporated in the C-clear method. The cleaning method improved the results in only one dataset, probably because of the not so well values chosen as cleaning parameters. The learning of these parameters from the data is left as a future work.

In this paper, we propose a trust-based message propagation and evaluation framework to support the effective evaluation of information sent by peers and the immediate control of false information in a VANET. More specifically, our trust-based message propagation collects peers’ trust opinions about a message sent by a peer (message sender) during the propagation of the message. We improve on an existing cluster-based data routing mechanism by employing a secure and efficient identity-based aggregation scheme for the aggregation and propagation of the sender’s message and the trust opinions. These trust opinions weighted by the trustworthiness of the peers modeled using a combination of role-based and experience-based trust metrics are used by cluster leaders to compute a ma jority opinion about the sender’s message, in order to proactively detect false information. Malicious messages are dropped and controlled to a local minimum without further affecting other peers. Our trust-based message evaluation allows each peer to evaluate the trustworthiness of the message by also taking into account other peers’ trust opinions about the message and the peer-to-peer trust of these peers. The result of the evaluation derives an effective action decision for the peer. We evaluate our framework in simulations of real life traffic scenarios by employing real maps with vehicle entities following traffic rules and road limits. Some entities involved in the simulations are possibly malicious and may send false information to mislead others or spread spam messages to jam the network. Experimental results demonstrate that our framework significantly improves network scalability by reducing the utilization of wireless bandwidth caused by a large number of malicious messages. Our system is also demonstrated to be effective in mitigating against malicious messages and protecting peers from being affected. Thus, our framework is particularly valuable in the deployment of VANETs by achieving a high level of scalability and effectiveness.

In this paper, we propose a trust-based message propagation and evaluation framework to support the effective evaluation of information sent by peers and the immediate control of false information in a VANET. More specifically, our trust-based message propagation collects peers’ trust opinions about a message sent by a peer (message sender) during the propagation of the message. We improve on an existing cluster-based data routing mechanism by employing a secure and efficient identity-based aggregation scheme for the aggregation and propagation of the sender’s message and the trust opinions. These trust opinions weighted by the trustworthiness of the peers modeled using a combination of role-based and experience-based trust metrics are used by cluster leaders to compute a ma jority opinion about the sender’s message, in order to proactively detect false information. Malicious messages are dropped and controlled to a local minimum without further affecting other peers. Our trust-based message evaluation allows each peer to evaluate the trustworthiness of the message by also taking into account other peers’ trust opinions about the message and the peer-to-peer trust of these peers. The result of the evaluation derives an effective action decision for the peer. We evaluate our framework in simulations of real life traffic scenarios by employing real maps with vehicle entities following traffic rules and road limits. Some entities involved in the simulations are possibly malicious and may send false information to mislead others or spread spam messages to jam the network. Experimental results demonstrate that our framework significantly improves network scalability by reducing the utilization of wireless bandwidth caused by a large number of malicious messages. Our system is also demonstrated to be effective in mitigating against malicious messages and protecting peers from being affected. Thus, our framework is particularly valuable in the deployment of VANETs by achieving a high level of scalability and effectiveness.

In this Thesis, we propose a Cluster-based TDMA (CBT) scheme for Vehicular Ad-hoc Networks (VANET). In the CBT, the collision problems can be solved when packets are transmitted at the same time. In the Intra-cluster communications, the VANET Coordinator (VC) is determined by randomly choosing a number of zero or one. Other VANET Nodes (VNs) then randomly select different time slots to transmit their Bandwidth Requests (BRs). If more than two VNs choose the same slots for BRs, collision will occur. The failed VNs will continue to issue BRs in the next TDMA frames. After the time slots are scheduled by VC, all VNs can use the designated time slots to send data. In the Inter-cluster communications, when two clusters are approaching to each other, two VCs must exchange Slot Allocation MAP (SAM) using the random zero-or-one scheme. The VCs successfully receive SAM must reschedule the time slots. For the purpose of performance evaluation, we calculate the average time slots of selecting VC and the average time slots required for successful BRs. We also compute the average time slots required for successfully transmitting SAM and the average time slots required for broadcasting SAM to all VNs. Finally, we calculate the average time slots required for waiting before data transmission. To validate the mathematical results, we perform a simulation written in C++. When comparing the mathematical results to the simulation results, we observe that in the average time slots required for BR, the former is larger than the latter. This is because in the mathematical equations it is difficult to specify which time slots are used by VNs to transmit BRs. However, the rest of performance comparisons, the two results are very close.

Wireless Sensor Network (WSN) is composed of sink and sensors. Sensors transmit data to sink through wireless network after collecting data. Because multi-hop routing and forwarding may be required on WSN, sensors closer to sink will consume more energy than other nodes due to hop-by-hop forwarding. In this Thesis, we propose pipelined forwarding for cluster-based WSN to solve these problems. First, we divide a WSN into several clusters such that the distance between sensors and sink is reduced and packet transmission delay can be decreased. However, since reducing the distance can increase the number of clusters significantly, multiple mobile sinks are embedded in the system to increase overall throughput. Second, we change the direction of pipelined forwarding to avoid from running out of energy of some sensors. We derive mathematical equations to analyze and validate the proposed scheme. From the analytical results, we prove that the proposed scheme can decrease packet transmission delay. The results also show that system throughput can be improved by increasing the length of pipeline and the number of mobile sinks. Finally, we demonstrate that the proposed scheme can increase energy throughput more efficiently than conventional non-pipelined forwarding scheme.

Intelligent Transport System (ITS) has become a hot research topic over the past decades. ITS is a system that applies the following technologies to the whole transportation management system efficiently, including information technique, wireless communication, sensor networks, control technique, and computer engineering. ITS provides an accurate, real time and synthetically efficient transportation management system. Obviously, Vehicular Ad Hoc NETworks (VANETs) attract growing attention from both the research community and industry all over the world. This is because a large amount of applications are enabled by VANETs, such as safety related applications, traffic management, commercial applications and general applications. When connecting to the internet or communicating with different networks in order to access a variety of services using VANETs, drivers and passengers in different cars need to be able to exchange messages with gateways from their vehicles. A secure gateway discovery process is therefore critical, because vehicles should not be subject to security attacks while they are communicating; however, currently there is no existing protocol focusing on secure gateway discovery. In this thesis, we first analyze and compare current existing secure service discovery protocols and then we propose a Secure Gateway Localization and Communication System for Vehicular Ad Hoc Networks (SEGAL), which concentrates on the security issue in gateway discovery. We focus on the authentication aspect by proposing secure cluster based VANETs, that can ensure the gateway discovery messages exchanged through secure clusters. We present the principle and specific process of our SEGAL protocol and analyze its performance to guarantee its outstanding practical applicability.

Intelligent Transport System (ITS) has become a hot research topic over the past decades. ITS is a system that applies the following technologies to the whole transportation management system efficiently, including information technique, wireless communication, sensor networks, control technique, and computer engineering. ITS provides an accurate, real time and synthetically efficient transportation management system. Obviously, Vehicular Ad Hoc NETworks (VANETs) attract growing attention from both the research community and industry all over the world. This is because a large amount of applications are enabled by VANETs, such as safety related applications, traffic management, commercial applications and general applications. When connecting to the internet or communicating with different networks in order to access a variety of services using VANETs, drivers and passengers in different cars need to be able to exchange messages with gateways from their vehicles. A secure gateway discovery process is therefore critical, because vehicles should not be subject to security attacks while they are communicating; however, currently there is no existing protocol focusing on secure gateway discovery. In this thesis, we first analyze and compare current existing secure service discovery protocols and then we propose a Secure Gateway Localization and Communication System for Vehicular Ad Hoc Networks (SEGAL), which concentrates on the security issue in gateway discovery. We focus on the authentication aspect by proposing secure cluster based VANETs, that can ensure the gateway discovery messages exchanged through secure clusters. We present the principle and specific process of our SEGAL protocol and analyze its performance to guarantee its outstanding practical applicability.