5,201 research outputs found
Continual learning from stationary and non-stationary data
Continual learning aims at developing models that are capable of working on constantly evolving problems over a long-time horizon. In such environments, we can distinguish three essential aspects of training and maintaining machine learning models - incorporating new knowledge, retaining it and reacting to changes. Each of them poses its own challenges, constituting a compound problem with multiple goals.
Remembering previously incorporated concepts is the main property of a model that is required when dealing with stationary distributions. In non-stationary environments, models should be capable of selectively forgetting outdated decision boundaries and adapting to new concepts. Finally, a significant difficulty can be found in combining these two abilities within a single learning algorithm, since, in such scenarios, we have to balance remembering and forgetting instead of focusing only on one aspect.
The presented dissertation addressed these problems in an exploratory way. Its main goal was to grasp the continual learning paradigm as a whole, analyze its different branches and tackle identified issues covering various aspects of learning from sequentially incoming data. By doing so, this work not only filled several gaps in the current continual learning research but also emphasized the complexity and diversity of challenges existing in this domain. Comprehensive experiments conducted for all of the presented contributions have demonstrated their effectiveness and substantiated the validity of the stated claims
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional
segmentation task by incrementally learning newly added classes. Previous work
has introduced generative replay, which involves replaying old class samples
generated from a pre-trained GAN, to address the issues of catastrophic
forgetting and privacy concerns. However, the generated images lack semantic
precision and exhibit out-of-distribution characteristics, resulting in
inaccurate masks that further degrade the segmentation performance. To tackle
these challenges, we propose DiffusePast, a novel framework featuring a
diffusion-based generative replay module that generates semantically accurate
images with more reliable masks guided by different instructions (e.g., text
prompts or edge maps). Specifically, DiffusePast introduces a dual-generator
paradigm, which focuses on generating old class images that align with the
distribution of downstream datasets while preserving the structure and layout
of the original images, enabling more precise masks. To adapt to the novel
visual concepts of newly added classes continuously, we incorporate class-wise
token embedding when updating the dual-generator. Moreover, we assign adequate
pseudo-labels of old classes to the background pixels in the new step images,
further mitigating the forgetting of previously learned knowledge. Through
comprehensive experiments, our method demonstrates competitive performance
across mainstream benchmarks, striking a better balance between the performance
of old and novel classes.Comment: e.g.: 13 pages, 7 figure
Real-time head movement tracking through earables in moving vehicles
Abstract. The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition to see if they are capable of safely handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver it is possible to react before accidents happen. This thesis explores how accelerometer and gyroscope data collected using earables can be used to classify the orientation of the driver’s head in a moving vehicle. It is found that machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach fairly accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how the models could be improved further. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behavior before traffic accidents can happen
From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability
Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent
Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and
consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure,
vehicles or the travelers’ personal devices act as sources of data flows that are eventually
fed into software running on automatic devices, actuators or control systems producing, in turn,
complex information flows among users, traffic managers, data analysts, traffic modeling scientists,
etc. These information flows provide enormous opportunities to improve model development and
decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used
to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes;
in other words, for data-based models to fully become actionable. Grounded in this described data
modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic
to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm
conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying
the majority of ITS applications. Finally, we provide a prospect of current research lines within
Data Science that can bring notable advances to data-based ITS modeling, which will eventually
bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government
Time synchronization in wireless sensor networks
Time synchronization is basic requirements for various applications in wireless sensor network, e.g., event detection, speed estimating, environment monitoring, data aggregation, target tracking, scheduling and sensor nodes cooperation. Time synchronization is also helpful to save energy in WSN because it provides the possibility to set nodes into the sleeping mode. In wireless sensor networks all of above applications need that all sensor nodes have a common time reference. However, most existing time synchronization protocols are likely to deteriorate or even be destroyed when the WSNs attack by malicious intruders. The recently developed maximum and minimum consensus based time synchronization protocol (MMTS) is a promising alternative as it does not depend on any reference node or network topology. But MMTS is vulnerable to message manipulation attacks. In this thesis, we focus on how to defend the MMTS protocol in wireless sensor networks under message manipulation attacks. We investigate the impact of message manipulation attacks over MMTS. Then, a Secured Maximum and Minimum Consensus based Time Synchronization (SMMTS) protocol is proposed to detect and invalidate message manipulation attacks
The effect of government policy on housing delivery in Nigeria: a case study of port harcourt low Income housing programme
Housing is one of the most important needs of individuals next to food and clothing. Housing needs for low income earners has reached an alarming stage in Nigeria. On the supply side, numerous government policies have earlier aimed at disabling the massive shortage through numerous housing reform programmes. Despite these preceding efforts, housing remains an illusion to an average Nigerian. This research assessed the effect of government policy on housing delivery in Nigeria. The objectives were to determine housing needs of the low income group in Nigeria and to determine the impact of government policies on affordable housing provision to the low income group. Survey method was used to collect data from 44 respondents through the administration of questionnaires which was analyzed with statistical tools. The findings from the study shows that insufficient fund is closely related to other finance related factors identified as barriers to the accessibility of public housing by the low income group who are non-public servants. Such factors as high interest rate, low per capita income, lack of security of income, lack of collateral and high cost of public houses. The study suggest the creation of a viable secondary mortgage market, improvement of land registration and allocation, compassionate urban renewal programmes, cost saving house designs amongst others
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