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 × 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 × 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