An adaptive model for multi-modal biometrics decision fusion

Master'sMASTER OF ENGINEERIN

Efficient inference algorithms for network activities

The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals' activities in the social network. The inference of relationship between users/nodes or groups of users/nodes could be further complicated when activities are interval-censored, that is, when one only observed the number of activities that occurred in certain time windows. The same phenomenon happens in the online advertisement world where the advertisers often offer a set of advertisement impressions and observe a set of conversions (i.e. product/service adoption). In this case, the advertisers desire to know which advertisements best appeal to the customers and most importantly, their rate of conversions. Inspired by these challenges, we investigated inference algorithms that efficiently recover user relationships in both cases: time-stamped data and interval-censored data. In case of time-stamped data, we proposed a novel algorithm called NetCodec, which relies on a Hawkes process that models the intertwine relationship between group participation and between-user influence. Using Bayesian variational principle and optimization techniques, NetCodec could infer both group participation and user influence simultaneously with iteration complexity being O((N+I)G), where N is the number of events, I is the number of users, and G is the number of groups. In case of interval-censored data, we proposed a Monte-Carlo EM inference algorithm where we iteratively impute the time-stamped events using a Poisson process that has intensity function approximates the underlying intensity function. We show that that proposed simulated approach delivers better inference performance than baseline methods. In the advertisement problem, we propose a Click-to-Conversion delay model that uses Hawkes processes to model the advertisement impressions and thinned Poisson processes to model the Click-to-Conversion mechanism. We then derive an efficient Maximum Likelihood Estimator which utilizes the Minorization-Maximization framework. We verify the model against real life online advertisement logs in comparison with recent conversion rate estimation methods. To facilitate reproducible research, we also developed an open-source software package that focuses on various Hawkes processes proposed in the above mentioned works and prior works. We provided efficient parallel (multi-core) implementations of the inference algorithms using the Bayesian variational inference framework. To further speed up these inference algorithms, we also explored distributed optimization techniques for convex optimization under the distributed data situation. We formulate this problem as a consensus-constrained optimization problem and solve it with the alternating direction method for multipliers (ADMM). It turns out that using bipartite graph as communication topology exhibits the fastest convergence.Ph.D

Revisiting LARS for Large Batch Training Generalization of Neural Networks

LARS and LAMB have emerged as prominent techniques in Large Batch Learning (LBL), ensuring the stability of AI training. One of the primary challenges in LBL is convergence stability, where the AI agent usually gets trapped into the sharp minimizer. Addressing this challenge, a relatively recent technique, known as warm-up, has been employed. However, warm-up lacks a strong theoretical foundation, leaving the door open for further exploration of more efficacious algorithms. In light of this situation, we conduct empirical experiments to analyze the behaviors of the two most popular optimizers in the LARS family: LARS and LAMB, with and without a warm-up strategy. Our analyses give us a comprehension of the novel LARS, LAMB, and the necessity of a warm-up technique in LBL. Building upon these insights, we propose a novel algorithm called Time Varying LARS (TVLARS), which facilitates robust training in the initial phase without the need for warm-up. Experimental evaluation demonstrates that TVLARS achieves competitive results with LARS and LAMB when warm-up is utilized while surpassing their performance without the warm-up technique

DEVELOPMENT OF ANTI-WRINKLE CREAM FROM PUERARIA CANDOLLEI VAR. MIRIFICA (AIRY SHAW AND SUVAT.) NIYOMDHAM, KWAO KRUA KAO†FOR MENOPAUSAL WOMEN

Objective: The aim of this study was to incorporate Peraria candollei var. mirifica extract into the cream, to evaluate the physical properties and to conduct the skin tests in participants.Methods: Pueraria candollei var. mirifica was extracted with 95% ethanol to obtain crude pueraria extract (PCM). Crude PCM was developed as an anti-wrinkle PCM cream (B) intended for menopausal women. PCM cream was evaluated for stability of pH and viscosity, primary skin irritation, wrinkle reduction and moisturizing as well as customer satisfaction. Cream base (A) and cream purchased from the market (C) were used for comparison. ANOVA post hoc Turkey was used to analyze the variance (p0.05) of the mean comparisons between groups by cluster analysis. Results: The PCM cream appeared as white color, pH was 6.80, and viscosity was 4.069±0.01 Pa. s, as well as physical characteristic and texture, were acceptable and no irritating reaction. PCM cream exhibited a similar level of moisturizer as cream A and C. The PCM cream revealed an ability to decrease the wrinkle surface and wrinkle volume after applied for 7 and 14 d that shows the activity of this product performed from the PCM extract. Satisfaction of PCM cream showed good acceptance.Conclusion: These results suggest that PCM cream has the ability to reduce skin wrinkles. It is a good product for postmenopausal women and may also be of benefit for the general population for protection skin wrinkle

An Assessment of the Values of French Colonial Townhouses in Hanoi Towards A More Sustainable Conservation

As the capital city of French Indochina, Hanoi was well planned by the French and immensely invested in the construction of public buildings as well as houses. In addition to public buildings and villas designed in French colonial styles that shaped the so-called distinctive architectural heritage in Hanoi throughout the colonial years, a large number of townhouses built during 1920 - 1950 which formed the cityscape of Hanoi in the first half of the 20th century should be noted. After nearly 70 years since the French army withdrew from the city, the number of French townhouses has considerably decreased. The remaining houses have shown that this is a real “treasure” that needs to be conserved because of their important values, not only in terms of urban architecture but also in cultural and historical aspects. However, a fact requiring special attention is that French townhouses in Hanoi - unlike French public buildings and villas - have not yet been recognised as heritage so that they can be kept to avoid the risk of deterioration or demolition under the impact of rapid urbanisation in the market economy. One of the main reasons for this negative urban development is that there has been no concrete or comprehensive rating system to assess the values of those townhouses which will closely correspond to their characteristics and contexts. Therefore, the authors - based on site surveys and by applying some appropriate methods such as expert consultations and case studies - have developed a full set of criteria to help evaluate those remaining townhouses as accurately as possible. This system can be used as a basis for a systematic assessment and classification towards a more effective conservation and even promoting the values of those townhouses with regard to the development of a modern society and in consideration of sustainable heritage conservation as a mainstream in the world.

Label driven Knowledge Distillation for Federated Learning with non-IID Data

In real-world applications, Federated Learning (FL) meets two challenges: (1) scalability, especially when applied to massive IoT networks; and (2) how to be robust against an environment with heterogeneous data. Realizing the first problem, we aim to design a novel FL framework named Full-stack FL (F2L). More specifically, F2L utilizes a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new label-driven knowledge distillation (LKD) technique at the global server to address the second problem. As opposed to current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations, and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.Comment: 28 pages, 5 figures, 10 table

TETRAOXYGENATED XANTHONES FROM THE LATEX OF GARCINIA COWA

Seven tetraoxygenated xanthones, namely fuscaxanthone A,7-O-methylgarcinone E, cowagarcinone A, cowaxanthone, rubraxanthone, cowanin and cowanol, were isolated from the dichloromethane extract of the latex of Garcinia cowa_Roxb. ex Choisy. Their structures were elucidated on the basis of 1D, 2D NMR spectroscopic data and comparison with reported data

Outage and bit error probability analysis in energy harvesting wireless cooperative networks

This study focuses on a wireless powered cooperative communication network (WPCCN), which includes a hybrid access point (HAP), a source and a relay. The considered source and relay are installed without embedded energy supply (EES), thus are dependent on energy harvested from signals from the HAP to power their cooperative information transmission (IT). Taking inspiration from this, the author group investigates into a harvest-then-cooperate (HTC) protocol, whereas the source and the relay first harvest the energy from the AP in a downlink (DL) and then collaboratively work in uplink (UL) for IT of the source. For careful evaluation of the system performance, derivations of the approximate closed-form expression of the outage probability (OP) and an average bit error probability ( ABER) for the HTC protocol over Rayleigh fading channels are done. Lastly, the author group performs Monte-Carlo simulations to reassure the numerical results they obtained.Web of Science255746

F2SD: A dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.Comment: Accepted at ICMV 202

Disinfection performance of an ultraviolet lamp: a CFD investigation

Ultraviolet (UV)-based devices have shown their effectiveness on various germicidal purposes. To serve their design optimisation, the disinfection effectiveness of a vertically cylindrical UV lamp, whose wattage ranges from P = 30 − 100 W, is numerically investigated in this work. The UV radiation is solved by the Finite Volume Method together with the Discrete Ordinates model. Various results for the UV intensity and its bactericidal effects against several popular virus types, i.e., Corona-SARS, Herpes (type 2), and HIV, are reported and analysed in detail. Results show that the UV irradiance is greatly dependent on the lamp power. Additionally, it is indicated that the higher the lamp wattage employed, the larger the bactericidal rate is observed, resulting in the greater effectiveness of the UV disinfection process. Nevertheless, the wattage of P ≤ 100W is determined to be insufficient for an effective disinfection performance in a whole room; higher values of power must hence be considered in case intensive sterilization is required. Furthermore, the germicidal effect gets reduced with the viruses less sensitive to UV rays, e.g, the bactericidal rate against the HIV virus is only ∼8.98% at the surrounding walls