The Internet has become one of the indispensable approaches to information exchange and acquisition in the daily lives of people in most of the world. The emerging Internet and vigorous development of information technology in the recent years have brought us a more convenient and reliable means to access information. With the rapid information transmission, it not only changes the traditional social mode of operations, but also enables immediate commercial activities on the Internet. The development of e-negotiation systems benefit the enterprises in the competitive global environments for saving financial and time costs in travels, transportations and accommodations, as well as assists international negotiators to negotiate with counterparts more efficiently, with no time difference and no boundaries in the global village of e-commerce. While Internet and Information technologies breaking the spaces and limitations in terms of geography, the international negotiators have to break the habits of using native language instead and using the world language ¡V English ¡V to negotiate with their counterparts from different backgrounds and countries with various native languages spoken. Languages and communication are the most important elements in the negotiation, and they affect the success of negotiation processes and outcomes. This research is to explore the impact of language familiarity on negotiators and negotiation strategies, an asynchronous e-negotiation system was developed accordingly and experiments conducted in the groups of native language (Mandarin) and non-native language (English). Research results showed that negotiators who negotiated with native language had higher self-efficacy than the group with non-native language. The language self-efficacy showed significant positive influences on communication efficiency, but revealed negative impact on the relation with anxiety. The findings also indicated that communication efficiency showed statistically positive significance in all the three strategies adapted in this research ¡V Contending strategy, Problem-Solving strategy, and Persuasive strategy. The higher the communication efficiency, the more positively it would impact on the strategies of contending, problem-solving and persuasive. As for anxiety, the results indicated that negotiators with higher anxiety would tend to use contending strategy more, but with no obvious impacts on the relations between anxiety vs. problem-solving, and anxiety vs. persuasive.

As the Next-Generation (NextG, 5G and beyond) wireless network supports a wider range of services, optimization of resource allocation plays a crucial role in ensuring efficient use of the (limited) available network resources. Note that resource allocation may require knowledge of network parameters (e.g., channel state information and available power level) for package schedule. However, wireless networks operate in an uncertain environment where, in many practical scenarios, these parameters are unknown before decisions are made. In the absence of network parameters, a network controller, who performs resource allocation, may have to make decisions (aimed at optimizing network performance and satisfying users' QoS requirements) while emph{learning}. To that end, this dissertation studies two novel online learning problems that are motivated by autonomous resource management in NextG. Key contributions of the dissertation are two-fold. First, we study reward maximization under uncertainty with fairness constraints, which is motivated by wireless scheduling with Quality of Service constraints (e.g., minimum delivery ratio requirement) under uncertainty. We formulate a framework of combinatorial bandits with fairness constraints and develop a fair learning algorithm that successfully addresses the tradeoff between reward maximization and fairness constraints. This framework can also be applied to several other real-world applications, such as online advertising and crowdsourcing. Second, we consider global reward maximization under uncertainty with distributed biased feedback, which is motivated by the problem of cellular network configuration for optimizing network-level performance (e.g., average user-perceived Quality of Experience). We study both the linear-parameterized and non-parametric global reward functions, which are modeled as distributed linear bandits and kernelized bandits, respectively. For each model, we propose a learning algorithmic framework that can be integrated with different differential privacy models. We show that the proposed algorithms can achieve a near-optimal regret in a communication-efficient manner while protecting users' data privacy ``for free''. Our findings reveal that our developed algorithms outperform the state-of-the-art solutions in terms of the tradeoff among the regret, communication efficiency, and computation complexity. In addition, our proposed models and online learning algorithms can also be applied to several other real-world applications, e.g., dynamic pricing and public policy making, which may be of independent interest to a broader research community. / Doctor of Philosophy / As the Next-Generation (NextG) wireless network supports a wider range of services, optimization of resource allocation plays a crucial role in ensuring efficient use of the (limited) available network resources. Note that resource allocation may require knowledge of network parameters (e.g., channel state information and available power level) for package schedule. However, wireless networks operate in an uncertain environment where, in many practical scenarios, these parameters are unknown before decisions are made. In the absence of network parameters, a network controller, who performs resource allocation, may have to make decisions (aimed at optimizing network performance and satisfying users' QoS requirements) while emph{learning}. To that end, this dissertation studies two novel online learning problems that are motivated by resource allocation in the presence uncertainty in NextG. Key contributions of the dissertation are two-fold. First, we study reward maximization under uncertainty with fairness constraints, which is motivated by wireless scheduling with Quality of Service constraints (e.g., minimum delivery ratio requirement) under uncertainty. We formulate a framework of combinatorial bandits with fairness constraints and develop a fair learning algorithm that successfully addresses the tradeoff between reward maximization and fairness constraints. This framework can also be applied to several other real-world applications, such as online advertising and crowdsourcing. Second, we consider global reward maximization under uncertainty with distributed biased feedback, which is motivated by the problem of cellular network configuration for optimizing network-level performance (e.g., average user-perceived Quality of Experience). We consider both the linear-parameterized and non-parametric (unknown) global reward functions, which are modeled as distributed linear bandits and kernelized bandits, respectively. For each model, we propose a learning algorithmic framework that integrate different privacy models according to different privacy requirements or different scenarios. We show that the proposed algorithms can learn the unknown functions in a communication-efficient manner while protecting users' data privacy ``for free''. Our findings reveal that our developed algorithms outperform the state-of-the-art solutions in terms of the tradeoff among the regret, communication efficiency, and computation complexity. In addition, our proposed models and online learning algorithms can also be applied to several other real-world applications, e.g., dynamic pricing and public policy making, which may be of independent interest to a broader research community.

Efficient communication is an essential part of cooperative work, and no less so in the case of radio communication during air combat. With time being a limited resource and the consequences of a misunderstanding potentially fatal there is little room for negligence. This work is an exploratory study which combines data mining, machine learning, natural language processing and visual analytics in an effort to investigate the possibilities of using radio traffic data from air combat simulations for human performance evaluation. Both temporal and linguistic properties of the communication were analyzed, with several promising graphical results. Additionally, utterance classification was successfully attempted with mean precision and recall both over 0.9. It is hoped that more complex and to a larger extent automated data based communication analysis can be built upon the results presented in this report. / Effektiv kommunikation är en grundläggande del av god samarbetsförmåga, inte minst när det gäller radiokommunikation under luftstrid. När tid är en begränsad resurs och ett missförstånd kan få fatala följder finns inte mycket utrymme för slarv. Det här arbetet är en utforskande studie som kombinerar data mining, maskininlärning, natural language processing och visuell dataanalys i syfte att undersöka hur radiotrafikdata från luftstridssimulering skulle kunna användas för prestationsutvärdering. Såväl tidsrelaterade som språkliga egenskaper hos kommunikationen har analyserats och flera av visualiseringarna ser lovande ut. Vidare prövades med framgång att klassificera yttranden, med genomsnittlig precision och täckning över 0.9. Förhoppningen är att de resultat som presenteras i rapporten ska kunna användas som grund för vidareutveckling av mer djupgående och i större utsträckning automatiserad databaserad kommunikationsanalys.

Despite the extensive research being done in inter-organizational systems (IOS) adoption in the industry-to-industry field, there seems to be a lack of similar research being done in the IOS adoption for the university-to-industry context. This study takes up this lack of research and focuses on what factors that affect the adoption of IOS in the university-to-industry context instead of the industry-to-industry one. The purpose of this paper is to find how different factors influence IOS adoption decision in the university-to-industry context from the university’s perspective. The study was conducted with a qualitative approach. Five interviews were conducted with coordinators and researchers from different research units at Linnaeus University. The study found seven inter-relationships among the influential factors and how they affect the IOS adoption decision. A model that describes the relations is presented by the end of the study. The study is conducted in the qualitative nature and the sample size is rather limited. Therefore, the findings of the study cannot be generalized.

In the past decades, wireless Machine-to-Machine (M2M) networks have been developed in various industrial and public service areas and envisioned to improve our daily life in next decades, e.g., energy, manufacturing, transportation, healthcare, and safety. With the advantage of low cost, flexible deployment, and wide coverage as compared to wired communications, wireless communications play an essential role in providing information exchange among the distributed devices in wireless M2M networks. However, an intrinsic problem with wireless communications is that the limited radio spectrum resources may result in unsatisfactory performance in the M2M networks. With the number of M2M devices projected to reach 20 to 50 billion by 2020, there is a critical need to solve the problems related to the design and applications in the wireless M2M networks. In this dissertation work, we study the wireless M2M networks design from three closely related aspects, the wireless M2M communication reliability, efficiency, and Demand Response (DR) control in smart grid, an important M2M application taking the advantage of reliable and efficient wireless communications. First, for the communication reliability issue, multiple factors that affect communication reliability are considered, including the shadowing and fading characteristics of wireless channels, and random network topology. A general framework has been proposed to evaluate the reliability for data exchange in both infrastructure-based single-hop networks and multi-hop mesh networks. Second, for the communication efficiency issue, we study two challenging scenarios in wireless M2M networks: one is a network with a large number of end devices, and the other is a network with long, heterogeneous, and/or varying propagation delays. Media Access Control (MAC) protocols are designed and performance analysis are conducted for both scenarios by considering their unique features. Finally, we study the DR control in smart grid. Using Lyapunov optimization as a tool, we design a novel demand response control strategy considering consumer’s comfort requirements and fluctuations in both the renewable energy supply and customers’ load demands. By considering those unique features of M2M networks in data collection and distribution, the analysis, design and optimize techniques proposed in this dissertation can enable the deployment of wireless M2M networks with a large number of end devices and be essential for future proliferation of wireless M2M networks. / Graduate / 0544 / flintlei@gmail.com

Safety has become one of the greatest gauging factors for organisational success, within the mining and construction industry of South Africa. This is due to the fact that organisations and their employees are expected to adhere to safety legislation, or risk permanent shutdown. Notwithstanding this importance, methods of communicating safety information to employees have not yet been widely researched – especially not within the unique context of the combined mining and construction industry of South Africa. In the light of the above, this study focused on researching those internal communication methods most suited for the communication of safety information. The systems theory as meta-theory, and the stakeholder relationship theory, the excellence theory and dual-capacity model were used to frame the study. An extensive literature review was firstly conducted, identifying and discussing internal communication methods, their use within the South African mining and construction industry, and specifically their application to safety communication. The concepts of communication satisfaction and communication effectiveness of internal safety communication, as well as employee relations were investigated. Secondly the application of these internal safety communication methods was then further researched empirically within the Gautrain project – specifically its Precast Yard –which is seated within both the mining, as well as the construction industry, as it has to adhere to safety legislations from both industries. This empirical research was done by means of questionnaires, focus groups, interviews, as well as a discussion of the communication channels employed by the organisation. This accounted for a triangulated approach of using quantitative, as well as qualitative methodologies. From this empirical research it was seen that the Precast Yard of the Gautrain project adheres to the guidelines set in the literature in some circumstances, whilst in others it does not. From these shortcomings, seven recommendations were formulated for the improvement of safety communication in this organisation. These include the proposal that safety communications should be strategically managed by integrating all forms of communication, so that external factors that may impact on this communication are factored in. Furthermore, the communication of safety information should strive to reflect the diverse viewpoints of the employees, as it endeavours to foster a relationship with them. Briefly, this study, therefore, focused on identifying and reporting on those methods and techniques suited for the internal communication of safety information, specifically within the South African mining and construction industry, thereby expanding the field. / Thesis (M.A. (Communication Studies))--North-West University, Potchefstroom Campus, 2010.

Safety has become one of the greatest gauging factors for organisational success, within the mining and construction industry of South Africa. This is due to the fact that organisations and their employees are expected to adhere to safety legislation, or risk permanent shutdown. Notwithstanding this importance, methods of communicating safety information to employees have not yet been widely researched – especially not within the unique context of the combined mining and construction industry of South Africa. In the light of the above, this study focused on researching those internal communication methods most suited for the communication of safety information. The systems theory as meta-theory, and the stakeholder relationship theory, the excellence theory and dual-capacity model were used to frame the study. An extensive literature review was firstly conducted, identifying and discussing internal communication methods, their use within the South African mining and construction industry, and specifically their application to safety communication. The concepts of communication satisfaction and communication effectiveness of internal safety communication, as well as employee relations were investigated. Secondly the application of these internal safety communication methods was then further researched empirically within the Gautrain project – specifically its Precast Yard –which is seated within both the mining, as well as the construction industry, as it has to adhere to safety legislations from both industries. This empirical research was done by means of questionnaires, focus groups, interviews, as well as a discussion of the communication channels employed by the organisation. This accounted for a triangulated approach of using quantitative, as well as qualitative methodologies. From this empirical research it was seen that the Precast Yard of the Gautrain project adheres to the guidelines set in the literature in some circumstances, whilst in others it does not. From these shortcomings, seven recommendations were formulated for the improvement of safety communication in this organisation. These include the proposal that safety communications should be strategically managed by integrating all forms of communication, so that external factors that may impact on this communication are factored in. Furthermore, the communication of safety information should strive to reflect the diverse viewpoints of the employees, as it endeavours to foster a relationship with them. Briefly, this study, therefore, focused on identifying and reporting on those methods and techniques suited for the internal communication of safety information, specifically within the South African mining and construction industry, thereby expanding the field. / Thesis (M.A. (Communication Studies))--North-West University, Potchefstroom Campus, 2010.

With the development of intelligent services and applications enabled by Artificial Intelligence (AI), the Internet of Things (IoT) is infiltrating many aspects of our everyday lives. The usability of phones and tablets is largely increasing as the primary computing device, since the powerful sensors allow these devices to have access to an unprecedented amount of data. However, there are risks and responsibilities to collect the data in a centralized location due to privacy issues. To overcome this challenge, Federated Learning (FL) allows users to collectively taking the benefits of shared models trained from this big data, without the need to centrally store it. In this research, we present and evaluate the implementation of federated learning on Raspberry Pi boards using the FedAvg method. In addition, the compression method such as quantization and sparsification was applied to the baseline implementation to improve communication efficiency. We accomplished the implementation by comparing the baseline implementation and the compressed Federated-Averaging (FedAvg) on Raspberry Pi boards in order to achieve the smallest cost and higher accuracy to fit IoT environment. / Med utvecklingen av intelligenta tjänster och applikationer möjliggjord av AI infiltrerar IoT många aspekter av vår vardag. Användbarheten för telefoner och surfplattor ökar till stor del som den primära datorenheten, eftersom de kraftfulla sensorerna tillåter dessa enheter att få tillgång till en oöverträffad mängd data. Det finns dock risker och ansvar för att lagra data på en central plats på grund av integritetsfrågor. För att övervinna denna utmaning tillåter Federated Learning (FL) användare att kollektivt ta fördelarna av delade modeller utbildade från denna stora data utan att behöva lagra den centralt. I denna forskning presenterar och utvärderar vi implementeringen av federerat lärande på Raspberry Pi-kort med FedAVG-metoden. Dessutom hade komprimeringsmetoden som kvantisering och versifiering tillämpats på basimplementeringen för att förbättra kommunikationseffektiviteten. Vi slutför implementeringen genom att jämföra baslinjeimplementeringen och den komprimerade FedAVG på Raspberry-Pi-kort för att uppnå lägsta kostnad och högre noggrannhet för att passa IoT-miljö