208 research outputs found
Variational Approaches For Learning Finite Scaled Dirichlet Mixture Models
With a massive amount of data created on a daily basis, the ubiquitous demand for data analysis is undisputed. Recent development of technology has made machine learning techniques applicable to various problems. Particularly, we emphasize on cluster analysis, an important aspect of data analysis. Recent works with excellent results on the aforementioned task using finite mixture models have motivated us to further explore their extents with different applications. In other words, the main idea of mixture model is that the observations are generated from a mixture of components, in each of which the probability distribution should provide strong flexibility in order to fit numerous types of data. Indeed, the Dirichlet family of distributions has been known to achieve better clustering performances than those of Gaussian when the data are clearly non-Gaussian, especially proportional data.
Thus, we introduce several variational approaches for finite Scaled Dirichlet mixture models. The proposed algorithms guarantee reaching convergence while avoiding the computational complexity of conventional Bayesian inference. In summary, our contributions are threefold. First, we propose a variational Bayesian learning framework for finite Scaled Dirichlet mixture models, in which the parameters and complexity of the models are naturally estimated through the process of minimizing the Kullback-Leibler (KL) divergence between the approximated posterior distribution and the true one. Secondly, we integrate component splitting into the first model, a local model selection scheme, which gradually splits the components based on their mixing weights to obtain the optimal number of components. Finally, an online variational inference framework for finite Scaled Dirichlet mixture models is developed by employing a stochastic approximation method in order to improve the scalability of finite mixture models for handling large scale data in real time. The effectiveness of our models is validated with real-life challenging problems including object, texture, and scene categorization, text-based and image-based spam email detection
On the Design of Secure Full-Duplex Multiuser Systems under User Grouping Method
Consider a full-duplex (FD) multiuser system where an FD base station (BS) is
designed to simultaneously serve both downlink users and uplink users in the
presence of half-duplex eavesdroppers (Eves). Our problem is to maximize the
minimum secrecy rate (SR) among all legitimate users by proposing a novel user
grouping method, where information signals at the FD-BS are accompanied with
artificial noise to degrade the Eves' channel. The SR problem has a highly
nonconcave and nonsmooth objective, subject to nonconvex constraints due to
coupling between the optimization variables. Nevertheless, we develop a
path-following low-complexity algorithm, which invokes only a simple convex
program of moderate dimensions at each iteration. We show that our
path-following algorithm guarantees convergence at least to a local optima. The
numerical results demonstrate the merit of our proposed approach compared to
existing well-known ones, i.e., conventional FD and nonorthogonal multiple
access.Comment: 6 pages, 3 figure
Some factors affecting the effectiveness of social work activities in supporting drug addicts concentrated in No. II drug addiction treatment facility in Hoa Binh province, Vietnam
The article deals with the current situation of factors affecting the effectiveness of social work activities in supporting drug addicts at the No. II Hoa Binh drug rehabilitation facility. To achieve this goal, we conducted a random survey of 110 students undergoing detoxification at the center. Factors such as the characteristics of drug addicts; responsiveness of drug addiction treatment establishments; performance quality of social workers; Care and support of drug addicts' families. From there, propose measures to improve the effectiveness of social work activities in supporting drug addicts concentrated at detoxification establishments
VFFINDER: A Graph-based Approach for Automated Silent Vulnerability-Fix Identification
The increasing reliance of software projects on third-party libraries has
raised concerns about the security of these libraries due to hidden
vulnerabilities. Managing these vulnerabilities is challenging due to the time
gap between fixes and public disclosures. Moreover, a significant portion of
open-source projects silently fix vulnerabilities without disclosure, impacting
vulnerability management. Existing tools like OWASP heavily rely on public
disclosures, hindering their effectiveness in detecting unknown
vulnerabilities. To tackle this problem, automated identification of
vulnerability-fixing commits has emerged. However, identifying silent
vulnerability fixes remains challenging. This paper presents VFFINDER, a novel
graph-based approach for automated silent vulnerability fix identification.
VFFINDER captures structural changes using Abstract Syntax Trees (ASTs) and
represents them in annotated ASTs. VFFINDER distinguishes vulnerability-fixing
commits from non-fixing ones using attention-based graph neural network models
to extract structural features. We conducted experiments to evaluate VFFINDER
on a dataset of 36K+ fixing and non-fixing commits in 507 real-world C/C++
projects. Our results show that VFFINDER significantly improves the
state-of-the-art methods by 39-83% in Precision, 19-148% in Recall, and 30-109%
in F1. Especially, VFFINDER speeds up the silent fix identification process by
up to 47% with the same review effort of 5% compared to the existing
approaches.Comment: Accepted by IEEE KSE 202
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Toward user-driven Metaverse applications with fast wireless connectivity and
tremendous computing demand through future 6G infrastructures, we propose a
Brain-Computer Interface (BCI) enabled framework that paves the way for the
creation of intelligent human-like avatars. Our approach takes a first step
toward the Metaverse systems in which the digital avatars are envisioned to be
more intelligent by collecting and analyzing brain signals through cellular
networks. In our proposed system, Metaverse users experience Metaverse
applications while sending their brain signals via uplink wireless channels in
order to create intelligent human-like avatars at the base station. As such,
the digital avatars can not only give useful recommendations for the users but
also enable the system to create user-driven applications. Our proposed
framework involves a mixed decision-making and classification problem in which
the base station has to allocate its computing and radio resources to the users
and classify the brain signals of users in an efficient manner. To this end, we
propose a hybrid training algorithm that utilizes recent advances in deep
reinforcement learning to address the problem. Specifically, our hybrid
training algorithm contains three deep neural networks cooperating with each
other to enable better realization of the mixed decision-making and
classification problem. Simulation results show that our proposed framework can
jointly address resource allocation for the system and classify brain signals
of the users with highly accurate predictions
When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach
Rate Splitting Multiple Access (RSMA) has emerged as an effective
interference management scheme for applications that require high data rates.
Although RSMA has shown advantages in rate enhancement and spectral efficiency,
it has yet not to be ready for latency-sensitive applications such as virtual
reality streaming, which is an essential building block of future 6G networks.
Unlike conventional High-Definition streaming applications, streaming virtual
reality applications requires not only stringent latency requirements but also
the computation capability of the transmitter to quickly respond to dynamic
users' demands. Thus, conventional RSMA approaches usually fail to address the
challenges caused by computational demands at the transmitter, let alone the
dynamic nature of the virtual reality streaming applications. To overcome the
aforementioned challenges, we first formulate the virtual reality streaming
problem assisted by RSMA as a joint communication and computation optimization
problem. A novel multicast approach is then proposed to cluster users into
different groups based on a Field-of-View metric and transmit multicast streams
in a hierarchical manner. After that, we propose a deep reinforcement learning
approach to obtain the solution for the optimization problem. Extensive
simulations show that our framework can achieve the millisecond-latency
requirement, which is much lower than other baseline schemes
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