京都大学 / 新制・課程博士 / 博士(経済学) / 甲第24867号 / 経博第674号 / 新制||経||304(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 原 良憲, 教授 山田 仁一郎, 准教授 WANG Tao / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

The agile creation of 3D CAD platforms (3D CAD models that can be configured to obtain a family of Products) has become an important factor for increasing competitiveness of organizations that create discrete products. Design Automation (DA) is a powerful tool that can be used for speeding up and optimizing the design process of those 3D CAD platforms. Nonetheless, for effectively applying DA on the development of 3D CAD platforms it is desirable to count on tools that address the three fundamental hurdles that are also obstructing the wide adoption of DA in practice. These hurdles are the lack of identification of DA opportunities, the absence of performance metrics, and the absence of methods for continuous improvement. This dissertation contributes a set of methods and tools to incrementally improve the process for creating 3D CAD platforms towards increased automation. The tools proposed include the development of a Metrics Framework (MF) for assessing the capabilities of an organization for creating 3D CAD platforms; a method for increasing the organizational capabilities for creating 3D CAD platforms, and a method for identifying optimal improvement efforts for creating 3D CAD platforms in a multi-objective scenario

Privacy is an essential concept in the field of healthcare. As healthcare is fast digitalizing and going through platformization, understanding how it is constructed has become important. Swedish digital healthcare platform 1177 is a unique case, that can be used to analyze how the different actors that are present in the platform environment like the platform owner, healthcare organizations, and patients take part in the construction of privacy in a platform environment and how for example national and international legislation affects on the societal norms of the platform. By using the context analysis method and operationalizing platform society – and contextual integrity theory to 1177, it was revealed that while there is a clear hierarchy between the actors, all of them participate in different ways into making sure that the data that flows within the platform is being processed securely and with privacy in mind. While the platforms are their own socio-technical environments, they are also affected by national and international legislative norms. Compliance with legislation like the GDPR and the Swedish national patient data law is important as non-compliance can cause issues for the platform. However, while the platform privacy construction would be strong, the development of data analysis methods and AI can pose a risk for the data transfer from the platform to other purposes, like for example in research.

Tillväxten av sociala medier är väletablerad, idag kommunicerar vi inte bara ansikte mot ansikte, utan även via våra sociala medier. Målet med studien är att öka förståelsen för hur interaktion i kommentarsfält kan skilja sig åt mellan två plattformar kan kunskapen om plattformsdialekter öka. En medvetenhet kring teorin om plattformsdialekten är viktigt för företag som vill nå ut med sin reklam till specifika målgrupper på specifika plattformar. Denna studie tillämpar en tematisk analys för att identifiera olika språkliga uttryck i kommentarsfälten för ett visst inlägg på Facebook samt Tiktok. Resultaten indikerar att det finns skillnader i de språkliga uttrycken mellan plattformarna. Facebook tenderar att vara en plattform där dialekten är mer formell och argumenterande. Dialekten på Tiktok tenderar att vara informell och språket mer koncist, med mer förkortningar. Gemensamt för båda plattformarna är att användare förstärker budskap i kommentarer. Detta görs på Tiktok genom versaler samt längre ord och på Facebook genom stora skiljetecken och emojis. Eftersom studien bygger på en kvalitativ analys och är begränsad i sin omfattning, kommer resultaten såväl somslutsatserna inte vara generaliserande. Däremot kan studien användas som en referens för hur språkliga uttryck på Facebook och Tiktok kan se ut, vilket kan användas för vidare forskning som berör plattformsdialekter. / The growth of social media is well established, today we communicate not only face-toface, but also through our social media platforms. The aim with the study is to increase the knowledge of “platform vernacular” by enhancing the understanding of how interaction in comment fields can differ between two platforms. Awareness of the theory of platform vernacular is important for companies aiming to reach out with their advertising to specific target groups on certain platforms. This study applies a thematic analysis to identify different linguistic expressions in the comment fields of a given post on Facebook and Tiktok. The results indicates that there are differences in linguistic expressions between the platforms. Facebook tends to be a platform where the dialect is more formal and argumentative. The dialect on Tiktok tends to be more informal andthe language more concise, with more abbreviations. The two platforms have in common that users amplify messages in their comments. This is done on Tiktok through capitalization and extended words and on Facebook through large punctuation marks and emojis. As the study is based on a qualitative analysis and limited in its scope, the results as well as the conclusions will not be generalized. However, the study can be used to provide a reference for how linguistic expressions on Facebook and Tiktok may appear which can be used for further research concerning platform vernacular.

The Internet of Things (IoT) impacts nearly all aspects surrounding our daily life, including housing, transportation, healthcare, and manufacturing. IoT devices communicate through a variety of communication protocols, such as Bluetooth Low Energy (BLE), Zigbee, Z-Wave, and LoRa. These protocols serve essential purposes in both commercial industrial and personal domains, encompassing wearables and intelligent buildings. The organic and decentralized development of IoT protocols under the auspices of different organizations has resulted in a fragmented and heterogeneous IoT ecosystem. In many cases, IoT devices do not have an IP address. Furthermore, some protocols, such as LoRa and Z-Wave, are proprietary in nature and incompatible with standard protocols. This heterogeneity and fragmentation of the IoT introduce challenges in assessing the security posture of IoT devices. To address this problem, this thesis proposes a novel methodology that transcends specific protocols and supports network and security monitoring of IoT devices at scale. This methodology leverages the capabilities of software-defined radio (SDR) technology to implement IoT protocols in software. We first investigate the problem of IoT network reconnaissance, that is the discovery and characterization of all the IoT devices in one’s organization. We focus on four popular protocols, namely Zigbee, BLE, Z-Wave, and LoRa. We introduce and analyze new algorithms to improve the performance and speed-up the discovery of IoT devices. These algorithms leverage the ability of SDRs to transmit and receive signals across multiple channels in parallel. We implement these algorithms in the form of an SDR tool, called IoT-Scan, the first universal IoT scanner middleware. We thoroughly evaluate the delay and energy performance of IoT-Scan. Notably, using multi-channel scanning, we demonstrate a reduction of 70% in the discovery times of Bluetooth and Zigbee devices in the 2.4GHz band and of LoRa and Z-Wave devices in the 900MHz band, versus single-channel scanning. Second, we investigate a new type of denial-of-service attacks on IoT cards, called Truncate-after-Preamble (TaP) attacks. We employ SDRs to assess the security posture of off-the-shelf Zigbee and Wi-Fi cards to TaP attacks. We show that all the Zigbee devices are vulnerable to TaP attacks, while the Wi-Fi devices are vulnerable to the attack to a varying degree. Remarkably, TaP attacks demand energy consumption five orders of magnitude lower than what is required by a continuous jamming mechanism. We propose several countermeasures to mitigate the attacks. Third, we devise an innovative approach for the purpose of identifying and creating unique profiles for IoT devices. This approach leverages SDRs to create malformed packets at the physical layer (e.g., truncated or overlapping packets). Experiments demonstrate the ability of this approach to perform fine-grained timing experiments (at the microsecond level), craft multi-packet transmissions/collisions, and derive device-specific reception curves. In summary, the results of this thesis validate the feasibility of our proposed SDR-based methodology in addressing fundamental security challenges caused by the heterogeneity of the IoT. This methodology is future-proof and can accommodate new protocols and protocol upgrades.

This dissertation comprises three essays on theoretical analysis of middlemen in intermediated markets. Chapter 1 gives a brief survey on the market intermediation literature and also briefly describes the subsequent chapters. In Chapter 2 I study the role of horizontally differentiated middlemen in a bilateral search market in which heterogeneous agents of each group possess private information concerning the value of joint production. I focus on the effect of the middlemen on agents' search efforts and on pricing decisions by middlemen. In particular, I show that the middlemen intensify agents' search activities. I also provide an explanation for why middlemen often use asymmetric pricing for two groups in a market. In Chapter 3 I study a model of platform competition when both indirect network effect and the desirability concerns of the agents are present. The desirability concerns are defined as the perceived quality of platforms. A platform with a higher proportion of high-type agents is regarded as a platform with a better quality. Under these circumstances, I derive conditions for the existence of equilibrium. In a dominant platform equilibrium, I show that some agents may not be served by the dominant platform. I also show that two platforms with different perceived quality may coexist in equilibrium. It suggests that endogenous market segmentation may arise in two-sided markets. In chapter 4 I study the effort-maximizing contest rule when there is a positive externality between aggregate efforts and the contest audience: the audience is more willing to pay for watching a contest if each participating contestant expends more effort. The Tullock rent-seeking contest with endogenous entry is extended by incorporating the contest audience into the model. In order to fund the contest, the organizer with no budget has to collect fees from one or both of two groups. It is shown that the effort-maximizing contest rule under a positive externality attracts only two entrants and, in the unique subgame perfect equilibrium, the entrants are always subsidized regardless of the size of entry costs, and the audience pay a positive fee. / Ph. D.

With the large amount of personal data being shared on social media platforms, there is an increased security risk involved. Individuals are reliant on companies keeping their promises of securely handling personal data. Despite this, previous incidents such as the Cambridge Analytica incident have unveiled issues with the model of trusting a single entity to handle personal data safely. Instead of relying on a single entity keeping their promise, a different type of social media platform has started to emerge that decentralizes control over data. This type of social media platform that removes trust in a central entity, is called a decentralized social media platform. There are a plethora of decentralized social media platforms each relying on different heuristics for creating the network. Depending on the network structure which is the backbone of the platform, each network can have a different degree of centrality. If a decentralized social media platform’s network becomes too centralized, some entities in the network can gain larger control of the network, defeating its intended purpose. To prevent this, studying the network that comprises the platform can be fruitful. The science of network analysis can aid in finding the optimal network structure that best fits a decentralized social media platform. This study has examined five different network generation algorithms with a number of permutations in search of the network generation algorithm that best fits a decentralized social media platform. Each algorithm has generated 1,000 networks which were then used in one-way ANOVA tests to observe differences between the measurements. Four network centralization measures and a network efficiency measure have been used to determine the algorithm that minimizes centralization, while still being functional. The results indicate that, out of the five algorithms, the k-degree algorithm best fits a decentralized social media platform. / Med den stora andelen personlig data som delas på sociala medieplattformar, medför det en ökad säkerhetsrisk. Individer är beroende av att företag håller sina löften kring säkerhetshantering av personlig data. Trots detta har tidigare incidenter som Cambridge Analytica-incidenten avslöjat problem med modellen där individer behöver ha fullt förtroende att en enskild aktör ska hantera personlig data på ett säkert sätt. Istället för att ha förtroende att en enskild aktör ska hålla sitt löfte, har en ny typ av social medieplattform dykt upp som decentraliserar kontrollen över data. Denna typ av social medieplattform som avlägsnar förtroendet i centrala aktörer kallas för en decentraliserad social medieplattform. Det finns en stor andel decentraliserade sociala medieplattformar som beror av olika heuristiker för att bygga nätverket. Baserat på nätverksstrukturen, som är grunden till plattformen, så kan varje nätverk ha olika grad av centralisering. Omen decentraliserad social medieplattforms nätverk blir för centraliserad, kan ett par aktörer i nätverket få större kontroll över nätverket, vilket motverkar dess avsedda syfte. För att förhindra detta kan det vara givande att studera nätverket som är underlag för plattformen. Vetenskapen om nätverksanalys kan bidra att hitta den optimala nätverksstrukturen som på bästa sätt passar en decentraliserad social medieplattform. Denna forskning har undersökt fem olika nätverksgenerande algoritmer med ett antal permutationer för att söka den nätverksgenerande algoritm som bäst passar en decentraliserad social medieplattform. Varje algoritm har generat 1,000 nätverk som sedan använts i ett antal one-way ANOVA test för att observera skillnader mellan mätningarna. Fyra centraliseringmått och ett effektivitetmått har använts för att bestämma den algoritm som minimerar centraliseringen men som fortfarande är funktionell. Resultaten indikerar att, av de fem algoritmerna, passar k-gradsalgoritmen en decentraliserad social medieplattform bäst.

Although its applications have spread beyond computer science field, the process of machine learning still has some challenges for both expert and novice users. Machine learning platform aims to automate and accelerate the delivery cycle of using machine learning techniques. The objective of this degree project is to generate a user-centred design for an input interface of a machine-learning platform. To answer the research question, there are three methods conducted sequentially: 1) interviews; 2) prototyping; and 3) design evaluation. From the initial interview, we concluded users’ problems and expectations into 11 initial design requirements that should be incorporated into our future platform. The prototype testing focused on checking and improving the functionalities, rather than the visual appearance of the product. Finally, in the design evaluation method, the research delivered design recommendations consisting of five implications: 1) start with a clear definition of the specific machine learning goal; 2) present states of machine learning with a straight-forward flow that promotes learning-opportunity; 3) enable two-way transitions between all states; 4) accommodate different users’ goals with multiple scenarios; and 5) provide expert users with more control to customize the models. / Trots att dess tillämpningar har spridit sig utöver datavetenskapliga fält, behöver utvecklingen av framgångsrik användning av maskininlärning fortfarande anspråkiga komplexa metoder. Maskininlärningsplattform syftar till att automatisera och påskynda leveranscykeln för att använda maskininlärningstekniker. Syftet med detta examensarbete är att generera en användarcentrerad design för ett ingångsgränssnitt för en maskininlärningsplattform. För att besvara forskningsfrågan finns det tre metoder som genomförs i följd: 1) intervjuer; 2) prototypning; och 3) designutvärdering. Från den första intervjun avslutade vi användarnas problem och förväntningar i 11 ursprungliga designkrav som bör integreras av vår framtida plattform. Prototyptesten fokuserade på att kontrollera och förbättra funktionaliteterna snarare än det visuella utseendet på produkten. Avslutningsvis, i designbedömningsmetoden, levererade forskningen designrekommendationer bestående av fem implikationer: 1) börja med en tydlig definition av maskininlärningsmålet; 2) nuvarande stater med ett rakt framåtflöde som främjar inlärningsmöjligheter; 3) möjliggöra tvåvägsövergångar mellan tillstånd; 4) Rymma olika användares mål med flera scenarier; och 5) ge experter användare mer kontroll.

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