TIME AND ENERGY OPTIMAL LIST RANKING ALGORITHMS ON THE k-CHANNEL BROADCAST COMMUNICATION MODEL WITH NO COLLISION DETECTION

2004 ◽  
Vol 15 (01) ◽  
pp. 73-88
Author(s):  
KOJI NAKANO
Keyword(s):  
Collision Detection ◽  
Data Packet ◽  
K Channel ◽  
Communication Model ◽  
Ranking Algorithm ◽  
List Ranking ◽  
Broadcast Communication ◽  
Linked List ◽  
Ranking Algorithms ◽  
Time And Energy

A Broadcast Communication Model (BCM, for short) is a distributed system with no central arbiter populated by n processing units referred to as stations. The stations can communicate by broadcasting/receiving a data packet to one of k distinct communication channels. We assume that the stations run on batteries and expend power while broadcasting/receiving a data packet. Thus, the most important measure to evaluate algorithms on the BCM is the number of awake time slots, in which a station is broadcasting/receiving a data packet. The main contribution of this paper is to present time and energy optimal list ranking algorithms on the BCM. We first show that the rank of every node in an n-node linked list can be determined in O(n) time slots with no station being awake for more than O(1) time slots on the single-channel n-station BCM with no collision detection. We then extend this algorithm to run on the k-channel BCM. For any small fixed ∊>0, our list ranking algorithm runs in [Formula: see text] time slots with no station being awake for more than O(1) time slots, provided that k≤n1-∊. Clearly, [Formula: see text] time is necessary to solve the list ranking problem for an n-node linked list on the k-channel BCM. Therefore, our list ranking algorithm on the k-channel BCM is time and energy optimal.

scholarly journals Parallel communicating grammar systems with context-free components are Turing complete for any communication model

2016 ◽  
Vol 8 (2) ◽  
pp. 113-170
Author(s):  
Mary Sarah Ruth Wilkin ◽  
Stefan D. Bruda
Keyword(s):  
Concurrent Systems ◽  
Communication Model ◽  
Broadcast Communication ◽  
Turing Completeness ◽  
One Step ◽  
Grammar Systems ◽  
Turing Complete ◽  
Context Free ◽  
Original Construction ◽  
Parallel Communicating Grammar Systems

Abstract Parallel Communicating Grammar Systems (PCGS) were introduced as a language-theoretic treatment of concurrent systems. A PCGS extends the concept of a grammar to a structure that consists of several grammars working in parallel, communicating with each other, and so contributing to the generation of strings. PCGS are usually more powerful than a single grammar of the same type; PCGS with context-free components (CF-PCGS) in particular were shown to be Turing complete. However, this result only holds when a specific type of communication (which we call broadcast communication, as opposed to one-step communication) is used. We expand the original construction that showed Turing completeness so that broadcast communication is eliminated at the expense of introducing a significant number of additional, helper component grammars. We thus show that CF-PCGS with one-step communication are also Turing complete. We introduce in the process several techniques that may be usable in other constructions and may be capable of removing broadcast communication in general.

scholarly journals SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Cong Wan ◽  
Yanhui Fang ◽  
Cong Wang ◽  
Yanxia Lv ◽  
Zejie Tian ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Emotion ◽  
Ranking Algorithm ◽  
Random Walking ◽  
Signed Networks ◽  
Ranking Algorithms ◽  
Negative Score ◽  
Positive Score ◽  
Signed Network

Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.

scholarly journals Text Summarization using Extractive and Abstractive Methods

2021 ◽  
Vol 40 ◽  
pp. 03023
Author(s):  
Saurabh Varade ◽  
Ejaaz Sayyed ◽  
Vaibhavi Nagtode ◽  
Shilpa Shinde
Keyword(s):  
Input Sequence ◽  
Link Analysis ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Ranking Algorithm ◽  
Text File ◽  
Extractive Summarization ◽  
Ranking Algorithms ◽  
Original Meaning ◽  
Abstractive Summarization

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.

RANKuser

2018 ◽  
Vol 52 (3) ◽  
pp. 329-350 ◽  
Author(s):  
Abhishek Kumar Singh ◽  
Naresh Kumar Nagwani ◽  
Sudhakar Pandey
Keyword(s):  
Question Answering ◽  
User Profile ◽  
High Volume ◽  
Ranking Algorithm ◽  
Content Type ◽  
Community Question Answering ◽  
Ranking Algorithms ◽  
Expert User ◽  
Third Stage

Purpose Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites. Design/methodology/approach In this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score. Findings In this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms. Originality/value This paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.

Sign in / Sign up

Export Citation Format

Share Document