The \u3cem\u3eX\u3c/em\u3e-Alter Algorithm: A Parameter-Free Method of Unsupervised Clustering

Using quantization techniques, Laloë (2010) defined a new clustering algorithm called Alter. This L1-based algorithm is shown to be convergent but suffers two major flaws. The number of clusters, K, must be supplied by the user and the computational cost is high. This article adapts the X-means algorithm (Pelleg & Moore, 2000) to solve both problems

Computationally Efficient Optimization of a Five-Phase Flux-Switching PM Machine Under Different Operating Conditions

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis (CE-FEA) method, the cyclic representative operating points extraction technique, as well as the response surface methodology (in order to minimize the number of experiments when establishing the inverse machine model), are presented to reduce the computational effort and cost. From the results and discussion, it will be found that the optimization results against different operating conditions exhibit distinct characteristics in terms of geometry, efficiency, and energy loss distributions. For the traditional optimization performed at the rated operating point, the optimal design tends to reduce copper losses but suffer from high core losses; for UDDS, the optimal design tends to minimize both copper losses and PM eddy-current losses in the low-speed region; for HWFET, the optimal design tends to minimize core losses in the high-speed region; for the combined UDDS/HWFET, the optimal design tends to balance/compromise the loss components in both the low-speed and high-speed regions. Furthermore, the advantages of the adopted optimization methodologies versus the traditional procedure are highlighted

Oppfattet risiko og beredskap : Misforhold mellom kompetanse og antatt kunnskap om potensielle risikoer?

Forfatterens aksepterte manuskript (postprint).Dette er et akseptert manuskript av en artikkel som ble publisert av Universitetsforlaget i Praktisk økonomi & finans 30/03/2023.Tilgjengelig online: https://www.idunn.no/doi/10.18261/pof.39.1.3Vellykket kriseforebygging er avhengig av at ansatte i selskapene kan forutse og vurdere potensielle risikoer. Det skal være en sammenheng mellom hvor god beredskap de ansatte oppfatter at bedriften har for å håndtere fremtidige risikoer, og hvor sofistikert risikostyringssystemet er. Regnskapsbransjen er særlig utsatt for effektene av digitalisering (se f.eks. Frey og Osborne 2013). Vi har derfor analysert små og mellomstore norske regnskapsbyråer for å finne ut hvilken sammenheng det er mellom de ansattes opplevelse av selskapets risikoberedskap og hvor sofistikert risikostyringen er. Resultatene fra en spørreundersøkelse med 81 respondenter viser at respondentene kan grupperes i fire klynger. Empirien viser at det for noen selskaper ser ut til at jo mer sofistikerte risikostyringssystemene er, jo mindre forberedt er selskapene på å håndtere disse risikoene (og motsatt). Vi diskuterer dette ut fra den såkalte Dunning-Kruger-effekten, dvs. at mennesker som mangler kompetanse på et område er uvitende om sin manglende kompetanse. Våre funn indikerer at respondentenes oppfatning av sitt eget selskaps risikosystem og beredskap ikke alltid stemmer overens med virkeligheten. I flere tilfeller viste det seg at beredskapen og risikosystemet var langt svakere, noe som kan lede til finansielle tap. Dette kan i seg selv medføre en risiko. For å redusere denne risikoen kan bedrifter ta i bruk scenariostyring som et supplement til tradisjonell virksomhetsstyring for å forbedre sin beredskap.acceptedVersio

Application of privacy-preserving clustering methods using homomorphic encryption algorithms

The need of protection and processing of the sensitive data in large scale data systems (for example data derived from nancial systems, militaristic systems or social media platforms) is a common problem. Usage of traditional cryptographic methods for data protection mainly needs at least two of the ciphering, deciphering and data processing works to be done on the same side. Because of this, with increase of the data size there will be a need for higher processing power to work on the data. Using traditional encryption algorithms for protection of the sensitive data on large scale systems, also brings the need of exchanging the needed keys for protection and processing the data. Homomorphic encryption schemes have enough exibility that, they should be used on data systems that contains data from multiple parts, because of its feature of allowing to process the encrypted data like its non-encrypted form. With the usage of homomorphic encryption schemes and proper data learning systems on encrypted data, distribution of sensitive data to dierent parties can be done without violatingitsprivacy. Inthisthesis, weproposeamethodtorunmathematicalcomputations which needs high processing power on a common platform which oers high processing power of data but not on parties that the sensitive data will be distributed. As a result the partners of this systems will not need to have high processing power to function on the data because the high processing demanding tasks would be done on the common platform. In this research Paillier Cryptographic system was used to protect data privacy. Paillier Cryptographic algorithm's most prominent features are its asymmetrical and partially homomorphic behavior. We proposed a system that uses privacy preserving distance matrix calculation as input for several clustering algorithms which are commonly used in machine learning systems. Our system is evaluated considering dierent data lengths and dierent key lengths. Four dierent data clustering methods have been tested. By applying clustering algorithms on both encrypted and plain forms of the same data for dierent key and data lengths, we obtained performance results by using six dierent metrics.CONTENTS: Declaration of Authorship ii Abstract iv Öz v Acknowledgments vii List of Figures x List of Tables xiii 1 Introduction 1 1.1 Current Situation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Related Work 4 2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 Preliminaries 8 3.1 Data Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.1 K-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.3 Spectral Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.4 Birch Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.5 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.5.1 Homogeneity . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.5.2 Completeness . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.5.3 V-Measure . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.5.4 Adjusted Rand Index . . . . . . . . . . . . . . . . . . . . 15 3.1.5.5 Adjusted Mutual Information . . . . . . . . . . . . . . . . 15 3.1.5.6 Silhouette Coecient . . . . . . . . . . . . . . . . . . . . 16 3.2 Homomorphic Encryption . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Paillier Cryptosystem . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.2 Floating Point Numbers . . . . . . . . . . . . . . . . . . . . . . . . 17 4 System Model 18 4.1 Development Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 Sequence Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.1 Client Computaion . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.2 Data Authority Computation . . . . . . . . . . . . . . . . . . . . . 20 4.2.3 Model Building at Client . . . . . . . . . . . . . . . . . . . . . . . 21 5 Experiments and Results 24 5.1 Plaintext Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.1.1 K-Means Algorithm Results . . . . . . . . . . . . . . . . . . . . . . 25 5.1.2 Hierarchical Algorithm Results . . . . . . . . . . . . . . . . . . . . 28 5.1.3 Spectral Algorithm Results . . . . . . . . . . . . . . . . . . . . . . 31 5.1.4 Birch Algorithm Results . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2 Encrypted Domain Results . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.2.1 K-Means Algorithm Results . . . . . . . . . . . . . . . . . . . . . . 38 5.2.2 Hierarchical Algorithm Results . . . . . . . . . . . . . . . . . . . . 45 5.2.3 Spectral Algorithm Results . . . . . . . . . . . . . . . . . . . . . . 52 5.2.4 Birch Algorithm Results . . . . . . . . . . . . . . . . . . . . . . . . 59 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6 Conclusions and Future Work 67 Bibliography 6