1,471 research outputs found
Some stochastic Property of topological action semigroups
Under suitable conditions, with respect to some property on a random iterated
function system(RIFS), it is shown how the system satisfies in this property
almost surely.Comment: 9 page
Vitis: A Gossip-based Hybrid Overlay for Internet-scale Publish/Subscribe
Peer-to-peer overlay networks are attractive solutions for building Internet-scale publish/subscribe systems. However, scalability comes with a cost: a message published on a certain topic often needs to traverse a large number of uninterested (unsubscribed) nodes before reaching all its
subscribers. This might sharply increase resource consumption for such relay nodes (in terms of bandwidth transmission cost, CPU, etc) and could ultimately lead to rapid deterioration of the system’s performance once the relay nodes start dropping the messages or choose to permanently abandon the system. In this paper, we introduce Vitis, a gossip-based publish/subscribe system that significantly decreases the number of relay messages, and scales to an unbounded number of nodes and topics. This is achieved by the novel approach of enabling rendezvous routing on unstructured overlays. We construct a hybrid system by injecting structure into an otherwise unstructured network. The resulting structure resembles a navigable small-world network, which spans along clusters of nodes that have similar subscriptions. The properties of such an overlay make it an ideal platform for efficient data dissemination in large-scale systems. We perform extensive simulations and evaluate Vitis by comparing its performance against two base-line publish/subscribe systems: one that is oblivious to node subscriptions, and another that exploits the subscription similarities. Our measurements show that Vitis significantly outperforms the base-line solutions on various subscription and churn scenarios, from both synthetic models and real-world traces
Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models
Pre-trained Programming Language Models (PPLMs) achieved many recent states
of the art results for many code-related software engineering tasks. Though
some studies use data flow or propose tree-based models that utilize Abstract
Syntax Tree (AST), most PPLMs do not fully utilize the rich syntactical
information in source code. Still, the input is considered a sequence of
tokens. There are two issues; the first is computational inefficiency due to
the quadratic relationship between input length and attention complexity.
Second, any syntactical information, when needed as an extra input to the
current PPLMs, requires the model to be pre-trained from scratch, wasting all
the computational resources already used for pre-training the current models.
In this work, we propose Named Entity Recognition (NER) adapters, lightweight
modules that can be inserted into Transformer blocks to learn type information
extracted from the AST. These adapters can be used with current PPLMs such as
CodeBERT, GraphCodeBERT, and CodeT5. We train the NER adapters using a novel
Token Type Classification objective function (TTC). We insert our proposed work
in CodeBERT, building CodeBERTER, and evaluate the performance on two tasks of
code refinement and code summarization. CodeBERTER improves the accuracy of
code refinement from 16.4 to 17.8 while using 20% of training parameter budget
compared to the fully fine-tuning approach, and the BLEU score of code
summarization from 14.75 to 15.90 while reducing 77% of training parameters
compared to the fully fine-tuning approach.Comment: 11 pages, 5 Figures, Has been accepted on ICSE 202
ReviewViz: Assisting Developers Perform Empirical Study on Energy Consumption Related Reviews for Mobile Applications
Improving the energy efficiency of mobile applications is a topic that has
gained a lot of attention recently. It has been addressed in a number of ways
such as identifying energy bugs and developing a catalog of energy patterns.
Previous work shows that users discuss the battery-related issues (energy
inefficiency or energy consumption) of the apps in their reviews. However,
there is no work that addresses the automatic extraction of battery-related
issues from users' feedback. In this paper, we report on a visualization tool
that is developed to empirically study machine learning algorithms and text
features to automatically identify the energy consumption specific reviews with
the highest accuracy. Other than the common machine learning algorithms, we
utilize deep learning models with different word embeddings to compare the
results. Furthermore, to help the developers extract the main topics that are
discussed in the reviews, two states of the art topic modeling algorithms are
applied. The visualizations of the topics represent the keywords that are
extracted for each topic along with a comparison with the results of string
matching. The developed web-browser based interactive visualization tool is a
novel framework developed with the intention of giving the app developers
insights about running time and accuracy of machine learning and deep learning
models as well as extracted topics. The tool makes it easier for the developers
to traverse through the extensive result set generated by the text
classification and topic modeling algorithms. The dynamic-data structure used
for the tool stores the baseline-results of the discussed approaches and is
updated when applied on new datasets. The tool is open-sourced to replicate the
research results.Comment: 4 pages, 5 figure
Eigenstates Transition without Undergoing an Adiabatic Process
We introduce a class of non-Hermitian Hamiltonians that offers a dynamical approach to a shortcut to adiabaticity (DASA). In particular, in our proposed 2 Ă— 2 Hamiltonians, one eigenvalue is absolutely real and the other one is complex. This specific form of eigenvalues helps us to exponentially decay the population in an undesired eigenfunction or amplify the population in the desired state while keeping the probability amplitude in the other eigenfunction conserved. This provides us with a powerful method to have a diabatic process with the same outcome as its corresponding adiabatic process. In contrast to standard shortcuts to adiabaticity, our Hamiltonians have a much simpler form with a lower thermodynamic cost. Furthermore, we show that DASA can be extended to higher dimensions using the parameters associated with our 2 Ă— 2 Hamiltonians. Our proposed Hamiltonians not only have application in DASA but also can be used for tunable mode selection and filtering in acoustics, electronics, and optics
To Comply Software and IT System Development with Related Laws
. Accretion procedure of crimes and security breaches against the privacy of individual’s
information and their maintenance information systems has cost huge amount of financial and other
resources loose. Consequently governments take serious actions toward approving protective
legislation against cyber crimes and it will be duty of software developers to adopt policies and
measures to ensure that their designed systems are compatible with existing laws and their
amendments. Since information technology and legislation are two quite distinct sciences, existence
of a mechanism to do this adjustment and satisfy security and legal requirements of a designing
software system is very essential. This paper is representing a framework that will help IT
professionals to extract security requirements from relevant rules and use them in design of a system
which is in accordance with those rules. It is giving brief discussion of the framework’s
methodology and design of a simulating computer-aided system of this framework. It also reports
the research progress and new discovered conclusions
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