253 research outputs found
The Impact of the 2009 Federal Tobacco Excise Tax Increase on Youth Tobacco Use
Based on surveys of eighth-, tenth-, and twelfth-grade students, examines how the tobacco tax increase affected their use of cigarettes and other tobacco products. Considers contributing factors, such as the size of the tax hike, and policy implications
Semi-supervised multiscale dual-encoding method for faulty traffic data detection
Inspired by the recent success of deep learning in multiscale information
encoding, we introduce a variational autoencoder (VAE) based semi-supervised
method for detection of faulty traffic data, which is cast as a classification
problem. Continuous wavelet transform (CWT) is applied to the time series of
traffic volume data to obtain rich features embodied in time-frequency
representation, followed by a twin of VAE models to separately encode normal
data and faulty data. The resulting multiscale dual encodings are concatenated
and fed to an attention-based classifier, consisting of a self-attention module
and a multilayer perceptron. For comparison, the proposed architecture is
evaluated against five different encoding schemes, including (1) VAE with only
normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both
normal and faulty data encodings, but without attention module in the
classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT)
encoding. The first four encoding schemes adopted the same convolutional neural
network (CNN) architecture while the fifth encoding scheme follows the
transformer architecture of CViT. Our experiments show that the proposed
architecture with the dual encoding scheme, coupled with attention module,
outperforms other encoding schemes and results in classification accuracy of
96.4%, precision of 95.5%, and recall of 97.7%.Comment: 16 pages, 8 figure
Open Banking: Credit Market Competition When Borrowers Own the Data
Open banking facilitates data sharing consented to by customers who generate the data, with the regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer transaction data enables better borrower screening. Open banking can make the entire financial industry better off yet leave all borrowers worse off, even if borrowers have the control of whether to share their banking data. We highlight the importance of the equilibrium credit quality inference from borrowers’ endogenous sign-up decisions. We also study extensions with fintech affinities and data sharing on borrower preferences
Open Banking: Credit Market Competition When Borrowers Own the Data
Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers’ endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns
COMS: Customer Oriented Migration Service
Virtual machine live migration has been studied for more than a decade, and this technique has been implemented in various commercial hypervisors. However, currently in the cloud environment, virtual machine migration is initiated by system administrators. Cloud customers have no say on this: They can not initiate a migration, and they do not even know whether or not their virtual machines have been migrated. In this paper, we propose the COMS framework, which is short for Customer Oriented Migration Service . COMS gives more control to cloud customers so that migration becomes a service option and customers are more aware of the migration process. We have implemented a suite of modules in our COMS framework. Our evaluation results show that these modules could either bring performance benefit to cloud customers, or mitigate security threats in the cloud environment
Security and Energy-aware Collaborative Task Offloading in D2D communication
Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment
Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases
Large Language Models (LLMs) have demonstrated remarkable performance in code
completion. However, due to the lack of domain-specific knowledge, they may not
be optimal in completing code that requires intensive domain knowledge for
example completing the library names. Although there are several works that
have confirmed the effectiveness of fine-tuning techniques to adapt language
models for code completion in specific domains. They are limited by the need
for constant fine-tuning of the model when the project is in constant
iteration.
To address this limitation, in this paper, we propose NM-LM, a
retrieval-augmented language model (R-LM), that integrates domain knowledge
into language models without fine-tuning. Different from previous techniques,
our approach is able to automatically adapt to different language models and
domains. Specifically, it utilizes the in-domain code to build the
retrieval-based database decoupled from LM, and then combines it with LM
through Bayesian inference to complete the code. The extensive experiments on
the completion of intra-project and intra-scenario have confirmed that NM-LM
brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A
deep analysis of our tool including the responding speed, storage usage,
specific type code completion, and API invocation completion has confirmed that
NM-LM provides satisfactory performance, which renders it highly appropriate
for domain adaptive code completion. Furthermore, our approach operates without
the requirement for direct access to the language model's parameters. As a
result, it can seamlessly integrate with black-box code completion models,
making it easy to integrate our approach as a plugin to further enhance the
performance of these models.Comment: Accepted by ASE202
- …