Facial Paralysis Grading Based on Dynamic and Static Features

Peripheral facial nerve palsy, also known as facial paralysis (FP), is a common clinical disease, which requires subjective judgment and scoring based on the FP scale. There exists some automatic facial paralysis grading methods, but the current methods mostly only consider either static or dynamic features, resulting in a low accuracy rate of FP grading. This thesis proposes an automatic facial paralysis assessment method including both static and dynamic characteristics. The first step of the method performs preprocessing on the collected facial expression videos of the subjects, including rough video interception, video stabilization, keyframe extraction, image geometric normalization and gray-scale normalization. Next, the method selects as keyframes no facial expression state and maximum facial expression state in the image data to build the the research data set. Data preprocessing reduces errors, noise, redundancy and even errors in the original data. The basis for extracting static and dynamic features of an image is to use Ensemble of Regression Trees algorithm to determine 68 facial landmarks. Based on landmark points, image regions of image are formed. According to the Horn-Schunck optical flow method, the optical flow information of parts of the face are extracted, and the dynamic characteristics of the optical flow difference between the left and right parts are calculated. Finally, the results of dynamic and static feature classification are weighted and analyzed to obtain FP ratings of subjects. A 32-dimensional static feature is fed into the support vector machine for classification. A 60-dimensional feature vector of dynamical aspects is fed into a long and short-term memory network for classification. Videos of 30 subjects are used to extract 1419 keyframes to test the algorithm. The accuracy, precision, recall and f1 of the best classifier reach 93.33%, 94.29%, 91.33% and 91.87%, respectively.Perifeerinen kasvojen hermohalvaus, joka tunnetaan myös nimellä kasvojen halvaus (FP), on yleinen kliininen sairaus, joka vaatii subjektiivista arviointia ja FP -asteikon pisteytystä. Joitakin automaattisia kasvohalvauksen luokittelumenetelmiä on olemassa, mutta yleensä ottaen ne punnitsevat vain joko staattisia tai dynaamisia piirteitä. Tässä tutkielmassa ehdotetaan automaattista kasvojen halvaantumisen arviointimenetelmää, joka kattaa sekä staattiset että dynaamiset ominaisuudet. Menetelmän ensimmäinen vaihe suorittaa ensin esikäsittelyn kohteiden kerätyille kasvojen ilmevideoille, mukaan lukien karkea videon sieppaus, videon vakautus, avainruudun poiminta, kuvan geometrinen normalisointi ja harmaasävyjen normalisointi. Seuraavaksi menetelmä valitsee avainruuduiksi ilmeettömän tilan ja kasvojen ilmeiden maksimitilan kuvadatasta kerryttäen tutkimuksen data-aineiston. Tietojen esikäsittely vähentää virheitä, kohinaa, redundanssia ja jopa virheitä alkuperäisestä datasta. Kuvan staattisten ja dynaamisten piirteiden poimimisen perusta on käyttää Ensemble of Regression Trees -algoritmia 68 kasvojen merkkipisteiden määrittämiseen. Merkkipisteiden perusteella määritellään kuvan kiinnostavat alueet. Horn-Schunckin optisen virtausmenetelmän mukaisesti poimitaan optisen virtauksen tiedot joistakin kasvojen osista, ja dynaaminen luonnehdinta lasketaan vasempien ja oikeiden osien välille. Lopuksi dynaamisen ja staattisen piirteiden luokittelun tulokset painotetaan ja analysoidaan kattavasti koehenkilöiden FP-luokitusten saamiseksi. 32- ulotteinen staattisten piirteiden vektori syötetään tukivektorikoneeseen luokittelua varten. 60-ulotteinen dynaamisten piirteiden ominaisuusvektori syötetään pitkän ja lyhyen aikavälin muistiverkkoon luokittelua varten. Parhaan luokittelijan tarkkuus, täsmällisyys, palautustaso ja f1 saavuttavat arvot 93,33%, 94,29%, 91,33% ja 91,87%

Message Framing in P2P Lending Relationships

This paper investigates whether language and associated message framing (low-cost signal) can provide a solution to the risks generated by asymmetric information in P2P lending, drawing on the signalling and message-framing theories. First, it examines the extent to which message framing is associated with funding outcomes in the context of P2P lending; second, it investigates whether positive message framing reinforces the positive impact of credit ratings (high-cost signal) on funding outcomes. Our analysis is conducted on a dataset of 33028 listings of potential borrowers from a Chinese P2P lending platform using the Heckman selection models. We find that the use of positively framed messages is positively associated with positive funding outcomes and enhances the positive impact of the credit ratings on funding outcomes. Our results contribute to the literature on the effectiveness of low-cost signals in of Internet-based interactions while highlighting complementarities between different types of signals in P2P lending

Essays on Peer to Peer Lending

The Peer-to-Peer (P2P) lending model has become increasingly popular in China in recent years. In 2012, there are only 298 P2P platforms operating in China and loan volume is 22.9 billion RMB while in the first half of 2018, there are 1881 P2P platforms and trading volume has reached 7.33 trillion RMB. Although both number of platforms and transaction volume have increased significantly, severe asymmetric information still discourages participants. This doctoral thesis uses three empirical chapters to investigate the P2P lending market in China. Drawing on Message framing and signaling theory, we first examines the extent to which message framing is associated with funding outcomes receive in the context of P2P lending and whether positive message framing reinforces the positive impact of credit ratings on funding outcomes. Using a Heckman two stage model, we find that the use of positively framed messages is positively associated with positive funding outcomes. Besides, positive message framing enhances the positive impact of the credit ratings (an example of costly signals) on funding outcomes. The results contribute to the literature on the effectiveness of cheap signals in the context of Internet-based interactions while highlighting complementarities between different types of signals in P2P lending. We then investigate the role of psychological distancing and language intensity in P2P funding performance. We bridge the P2P lending literature and psycholinguistics literature and set out to explain how psychological distancing manifested by linguistic styles can influence lenders’ decision on P2P funding campaign. We argue find that linguistic styles related to psychological distancing have a negative impact onare negatively related to P2P funding success. Moreover, the language intensity tends to strengthen the negative relationship between psychological distancing and funding success. Our empirical results provide general support for the argument. This finding is consistent with psycholinguistics literature which suggests that psychological distancing is associated with negative interpersonal outcome (Simmons et al, 2005; Revenstorf et al, 1984). Specifically, the number of “you” and the number of negations used in borrowers’ description are negatively related to the willingness of the lender to support the funding campaign. The intensive language negatively strengths the relationship between the funding performance and number of “you” but does not apply to number of negations. Lastly, we investigate the funding performance of the financial excluded borrower in a large P2P lending platform. The association of financial technology (fintech) and financial exclusion has attracted attention since rapid growth of fintech innovation. Using loan-level data from a lending Chinese P2P company, we find there is a negative indirect effect of financial exclusion on funding success through credit score. In a moderated mediation analysis, we also find new business model such as offline authentication and education qualification positively moderates the linkage between the financial excluded and credit score and therefore negative indirect effect of financial exclusion on funding success is overturned when the excluded borrower has conducted offline authentication and obtained higher education qualification. In the end, we examine the determinants of offline authentication decision. We find the borrowers in a city with better financial infrastructure are more willing to conduct authentication. However, the financial excluded borrowers are less likely to conduct offline authentication