京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21770号 / 工博第4587号 / 新制||工||1715(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 中村 裕一, 教授 小山田 耕二, 教授 喜多 一 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

No / This paper proposes a novel call admission control (CAC) algorithm and develops a two-dimensional markov chain processes (MCP) analytical model to evaluate its performance for heterogeneous wireless network. Within the context of this paper, a hybrid UMTS-WLAN network is investigated. The designed threshold-based CAC algorithm is launched basing on the user’s classification and channel allocation policy. In this approach, channels are assigned dynamically in accordance with user class differentiation. The two-dimensional MCP mathematical analytic method reflects the system performance by appraising the dropping likelihood of handover traffics. The results show that the new CAC algorithm increases the admission probability of handover traffics, while guarantees the system quality of service (QoS) requirement.

We develop and analyze a class of CAC algorithms for resource management in wireless networks with the goal not only to satisfy QoS constraints, but also to maximize a value or reward objective function specified by the system. We demonstrate through analytical modeling and simulation validation that the CAC algorithms developed in this research for resource management can greatly improve the system reward obtainable with QoS guarantees, when compared with existing CAC algorithms designed for QoS satisfaction only. We design hybrid partitioning-threshold, spillover and elastic CAC algorithms based on the design techniques of partitioning, setting thresholds and probabilistic call acceptance to use channel resources for servicing distinct QoS classes. For each CAC algorithm developed, we identify optimal resource management policies in terms of partitioning or threshold settings to use channel resources. By comparing these CAC algorithms head-to-head under identical conditions, we determine the best algorithm to be used at runtime to maximize system reward with QoS guarantees for servicing multiple service classes in wireless networks. We study solution correctness, solution optimality and solution efficiency of the class of CAC algorithms developed. We ensure solution optimality by comparing optimal solutions achieved with those obtained by ideal CAC algorithms via exhaustive search. We study solution efficiency properties by performing complexity analyses and ensure solution correctness by simulation validation based on real human mobility data. Further, we analyze the tradeoff between solution optimality vs. solution efficiency and suggest the best CAC algorithm used to best tradeoff solution optimality for solution efficiency, or vice versa, to satisfy the system's solution requirements. Moreover, we develop design principles that remain applicable despite rapidly evolving wireless network technologies since they can be generalized to deal with management of 'resources' (e.g., wireless channel bandwidth), 'cells' (e.g., cellular networks), "connections" (e.g., service calls with QoS constraints), and "reward optimization" (e.g., revenue optimization in optimal pricing determination) for future wireless service networks. To apply the CAC algorithms developed, we propose an application framework consisting of three stages: workload characterization, call admission control, and application deployment. We demonstrate the applicability with the optimal pricing determination application and the intelligent switch routing application. / Ph. D.

Ransomware is an ever-growing issue that has been affecting individuals and corporations since its inception, leading to losses of the order of billions each year. This research builds upon the existing body of research pertaining to ransomware detection for Windows-based platforms through behavioral analysis using sandboxing techniques and classification using machine learning (ML), considering the various predefined function calls, known as API (Application Programming Interface) calls, made by ransomware and benign samples as classifying features. The primary aim of this research is to study the effect of the frequency of API calls made by ransomware samples spanning across a large number of ransomware families exhibiting varied behavior, and benign samples on the classification accuracy of various ML algorithms. Conducting an experiment based on this, a quantitative analysis of the ML classification algorithms was performed, for the frequency of API calls based input and binary input based on the existence of an API call, resulting in the conclusion that considering the frequency of API calls marginally improves the ransomware recall rate. The secondary research question posed by this research aims to justify the ML classification of ransomware by conducting behavioral analysis of ransomware and goodware in the context of the API calls that had a major effect on the classification of ransomware. This research was able to provide meaningful insights into the runtime behavior of ransomware and goodware, and how such behavior including API calls and their frequencies were in line with the MLbased classification of ransomware. / Master of Science / Ransomware is an ever-growing issue that has been affecting individuals and corporations since its inception, leading to losses of the order of billions each year. It infects a user machine, encrypts user files or locks the user out of their machine, or both, demanding ransom in exchange for decrypting or unlocking user data. Analyzing ransomware either statically or behaviorally is a prerequisite for building detection and countering mechanisms. Behavioral analysis of ransomware is the basis for this research, wherein ransomware is analyzed by executing it on a safe sandboxed environment such as a virtual machine to avoid infecting a real-user machine, and its runtime characteristics are extracted for analysis. Among these characteristics, the various predefined function calls, known as API (Application Programming Interface) calls, made to the system by ransomware will serve as the basis for the classification of ransomware and benign software. After analyzing ransomware samples across various families, and benign samples in a sandboxed environment, and considering API calls as features, the curated dataset was fed to a set of ML algorithms that have the capability to extract useful information from the dataset to take classification decisions without human intervention. The research will consider the importance of the frequency of API calls on the classification accuracy and also state the most important APIs for classification along with their potential use in the context of ransomware and goodware to justify ML classification. Zero-Day detection, which refers to testing the accuracy of trained ML models on unknown ransomware samples and families was also performed.

Background: Telephone counseling with a registered nurse at the front has become an integral part of health care both in Sweden and internationally. Telephone nurses' in call centers and primary care handle calls with patients of all ages regarding with both physical and psychological symptoms and for that they need competence with specialist knowledge at an advanced level. In the absence of specialist education to telephone nurse, and still be able to meet the patients` needs, registered nurses' need knowledge of what it is like to work with telephone advice. Aim: The aim of the study was to describe nurses' experiences of working with telephone counseling in call center and primary care. Method: The method chosen for this study was a literature-based study based on the analysis of qualitative research. A total of 12 scientific articles were analyzed.  Collected data was analyzed based on Friberg's five-step model (Friberg, 2017).  Results: In the analysis three themes emerged with nine subthemes. The themes were; challenges in the telephone conversation, need for support and stress factors in the work environment.  Conclusion: Working as a telephone nurse requires advanced skills in verbal communication, cooperation with colleagues, patients and third part, but also skills in working in several computerized systems simultaneously. The results indicate that telephone nurses' values patient safety highly and they take a great responsibility for optimizing the conditions for safe patient assessment. Sometimes an involuntary responsibility that can be seen to negatively affect both telephone nurse, the patient and society. / Syftet med denna studie var att beskriva sjuksköterskors erfarenheter av att arbeta med telefonrådgivning inom callcenter och primärvård. Studien baseras på analys av 12 artiklar där kvalitativ metod använts. Analys av artiklarna gjordes enligt Fribergs femstegsmodell. Ur analysen av datamaterialet framträdde tre teman: utmaningar i telefonsamtalet, behov av stöd och stressande faktorer i arbetsmiljön. Resultatet visade att när sjuksköterskorna inte såg patienterna såsom vid ett fysiskt möte, så låg vikten på att lyssna till patientens berättelse och genom den verbala kommunikationen bygga framgångsrik interaktion, samt ställa uppföljande frågor till stöd för bedömningen av patientens behov. Samtal med tredje part försvårade bedömningen och gav en risk för beslut som inte var patientsäkra. Sjuksköterskorna angav att stöd från kollegor och andra professioner var värdefullt. Som stöd i sina beslut arbetade sjuksköterskorna med ett datoriserat beslutstöd vilket upplevdes positivt, men det framkom även att beslutstöden hade en del brister i sin utformning. Sjuksköterskorna angav att den av arbetsgivaren bestämda samtalstiden var en stressfaktor. Svåra samtal, främst de som handlade om psykisk ohälsa tog lång tid och då växte telefonkön, vilket även den gav en känsla av stress att inte hinna med sitt arbete.

Yes / Fifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing tra c requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system e ciency. In this paper, a novel Fuzzy-Logic based CAC scheme with pre-emption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are pre-empted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption and up to 95% of return on revenue.

Digital tools and technologies have become a natural part of everyday life, whether at home, in workplaces, or school. This study explores Swedish middle school teachers' perceptions of Computer Assisted Language Learning (CALL), compared to more traditional and analog tools, with the focus on their students’ English proficiency and attitudes towards CALL. The methods used in this study include an online survey and semi-structured interviews. The findings reveal that the opinions on CALL differ, with some teachers expressing reservations while others see potential benefits, particularly in the possibilities of individualized teaching. While the teachers also perceive CALL to increase student engagement, concerns about digital distractions are also expressed. Though there is previous research done on the subject, there are not that many studies from a Swedish middle school context; hence, this study might fill that gap.

Conflict among legislators has been an ever-present component of the legislative process in the U.S. Congress. However, most political scientists have treated all dissension within the legislature as the result of partisan disagreement over various policy options. I propose in this thesis that a second dimension of conflict exists within Congress, one caused by personal rivalries unrelated to the discussion of issues. This category, which I have termed "personality conflict," or "incivility," can take the form of actions between legislators such as name-calling and fist-fights. In my research, I have created a measure of these incivilities and studied the movement in the levels of personality conflict within Congress from 1851 through 2004. In addition, I compare these trends to a conventional measure of party polarization or partisan conflict. The analysis suggests that the two types of conflict are distinct, but also that levels of one type of congressional conflict can have important effects on the absolute level of the other.

No / Purpose – The purpose of this paper is to identify the impact of organizational stressors on organizational citizenship behaviour (OCB) and how perceived organizational support (POS) will moderate in the relationship between stressors and citizenship behavior. Design/methodology/approach – The sample for this research involves operators from call center organizations located around the national capital of India. A questionnaire survey was carried out involving 402 operator level employees from five different organizations. Findings – The results highlight a significant negative relationship between organizational stressors and OCB, a significant positive relationship between POS and OCB, and confirmation that POS moderates in the relationship between organizational stressors and OCB. Research limitations/implications – This research has been carried out in an emerging economy and in a sector which is seen as an attractive area of work. However, as this study is limited to the BPO sector in India, these results may not be generalized to other areas such as the public and manufacturing sectors and in other national contexts. Future research in this area should also consider using different data collection approaches to maximize participation and enrich findings. Practical implications – The analysis suggests that change management initiatives in organizations may not be implemented as effectively as they can under high stressor conditions because employee extra‐role work behavior and commitment may not be at full capacity. Originality/value – There is limited research examining the relationship between organizational stressors and OCB in the presence of POS, especially within high demand environments such as the Indian BPO sector.

The purpose of the research was to determine whether personality and measures of ability would significantly predict job performance of call centre operators in a South African communications company. The Customer Contact Styles Questionnaire (CCSQ7.2), the Basic Checking (CP7.1) ability test and the Audio Checking (CP8.1) ability test were completed by operators as the predictors. Supervisors completed the Customer Contact Competency Inventory (CCCI) for 140 operators as a measure of job performance. Performance statistics were obtained for the sample as additional criterion data. Correlations and multiple regression analysis revealed statistically significant small to moderate correlations between the criteria and the predictors. The research was conducted from a concurrent validity perspective. Further research from a predictive validity perspective is suggested in order to substantiate the findings and to improve the generalisability thereof. / Industrial and Organisational Psychology / M.A. (Industrial Psychology)