441,973 research outputs found
Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining
Service Function Chaining (SFC) allows the forwarding of a traffic flow along
a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT).
Software Defined Networking (SDN) solutions can be used to support SFC reducing
the management complexity and the operational costs. One of the most critical
issues for the service and network providers is the reduction of energy
consumption, which should be achieved without impact to the quality of
services. In this paper, we propose a novel resource (re)allocation
architecture which enables energy-aware SFC for SDN-based networks. To this
end, we model the problems of VNF placement, allocation of VNFs to flows, and
flow routing as optimization problems. Thereafter, heuristic algorithms are
proposed for the different optimization problems, in order find near-optimal
solutions in acceptable times. The performance of the proposed algorithms are
numerically evaluated over a real-world topology and various network traffic
patterns. The results confirm that the proposed heuristic algorithms provide
near optimal solutions while their execution time is applicable for real-life
networks.Comment: Extended version of submitted paper - v7 - July 201
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling
Price movement forecasting aims at predicting the future trends of financial
assets based on the current market conditions and other relevant information.
Recently, machine learning(ML) methods have become increasingly popular and
achieved promising results for price movement forecasting in both academia and
industry. Most existing ML solutions formulate the forecasting problem as a
classification(to predict the direction) or a regression(to predict the return)
problem over the entire set of training data. However, due to the extremely low
signal-to-noise ratio and stochastic nature of financial data, good trading
opportunities are extremely scarce. As a result, without careful selection of
potentially profitable samples, such ML methods are prone to capture the
patterns of noises instead of real signals. To address this issue, we propose a
novel price movement forecasting framework, called Locality-Aware Attention and
Iterative Refinement Labeling(LARA), which consists of two main components:
1)Locality-aware attention automatically extracts the potentially profitable
samples by attending to surrounding class-aware label information. Moreover,
equipped with metric learning techniques, locality-aware attention enjoys
task-specific distance metrics and distributes attention on potentially
profitable samples in a more effective way. 2)Iterative refinement labeling
further iteratively refines the labels of noisy samples and then combines the
learned predictors to be robust to the unseen and noisy samples. In a number of
experiments on three real-world financial markets: ETFs, stocks, and
cryptocurrencies, LARA achieves superior performance compared with the
traditional time-series analysis methods and a set of machine learning based
competitors on the Qlib platform. Extensive ablation studies and experiments
also demonstrate that LARA indeed captures more reliable trading opportunities
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
Sequential tabular data is one of the most commonly used data types in
real-world applications. Different from conventional tabular data, where rows
in a table are independent, sequential tabular data contains rich contextual
and sequential information, where some fields are dynamically changing over
time and others are static. Existing transformer-based approaches analyzing
sequential tabular data overlook the differences between dynamic and static
fields by replicating and filling static fields into each transformer, and
ignore temporal information between rows, which leads to three major
disadvantages: (1) computational overhead, (2) artificially simplified data for
masked language modeling pre-training task that may yield less meaningful
representations, and (3) disregarding the temporal behavioral patterns implied
by time intervals. In this work, we propose FATA-Trans, a model with two field
transformers for modeling sequential tabular data, where each processes static
and dynamic field information separately. FATA-Trans is field- and time-aware
for sequential tabular data. The field-type embedding in the method enables
FATA-Trans to capture differences between static and dynamic fields. The
time-aware position embedding exploits both order and time interval information
between rows, which helps the model detect underlying temporal behavior in a
sequence. Our experiments on three benchmark datasets demonstrate that the
learned representations from FATA-Trans consistently outperform
state-of-the-art solutions in the downstream tasks. We also present
visualization studies to highlight the insights captured by the learned
representations, enhancing our understanding of the underlying data. Our codes
are available at https://github.com/zdy93/FATA-Trans.Comment: This work is accepted by ACM International Conference on Information
and Knowledge Management (CIKM) 202
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Prototyping a Context-Aware Framework for Pervasive Entertainment Applications
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Location-based and contextual mobile learning. A STELLAR Small-Scale Study
This study starts from several inputs that the partners have collected from previous and current running research projects and a workshop organised at the STELLAR Alpine Rendevous 2010. In the study, several steps have been taken, firstly a literature review and analysis of existing systems; secondly, mobile learning experts have been involved in a concept mapping study to identify the main challenges that can be solved via mobile learning; and thirdly, an identification of educational patterns based on these examples has been done.
Out of this study the partners aim to develop an educational framework for contextual learning as a unifying approach in the field. Therefore one of our central research questions is: how can we investigate, theorise, model and support contextual learning
On the Evaluation of RDF Distribution Algorithms Implemented over Apache Spark
Querying very large RDF data sets in an efficient manner requires a
sophisticated distribution strategy. Several innovative solutions have recently
been proposed for optimizing data distribution with predefined query workloads.
This paper presents an in-depth analysis and experimental comparison of five
representative and complementary distribution approaches. For achieving fair
experimental results, we are using Apache Spark as a common parallel computing
framework by rewriting the concerned algorithms using the Spark API. Spark
provides guarantees in terms of fault tolerance, high availability and
scalability which are essential in such systems. Our different implementations
aim to highlight the fundamental implementation-independent characteristics of
each approach in terms of data preparation, load balancing, data replication
and to some extent to query answering cost and performance. The presented
measures are obtained by testing each system on one synthetic and one
real-world data set over query workloads with differing characteristics and
different partitioning constraints.Comment: 16 pages, 3 figure
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Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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