TCP Window Based Congestion Control -Slow-Start Approach

Transmission control protocol (TCP) has undergone several transformations. Several proposals have been put forward to change the mechanisms of TCP congestion control to improve its performance. A line of research tends to reduce speed in the face of congestion thereby penalizing itself. In this group are the window based congestion control algorithms that use the size of congestion window to determine transmission speed. The two main algorithm of window based congestion control are the congestion avoidance and the slow start. The aim of this study is to survey the various modifications of window based congestion control. Much work has been done on congestion avoidance hence specific attention is placed on the slow start in order to motivate a new direction of research in network utility maximization. Mathematical modeling of the internet is discussed and proposals to improve TCP startup were reviewed. There are three lines of research on the improvement of slow start. A group uses the estimation of certain parameters to determine initial speed. The second group uses bandwidth estimation while the last group uses explicit request for network assistance to determine initial startup speed. The problems of each proposal are analyzed and a multiple startup for TCP is proposed. Multiple startups for TCP specify that startup speed is selectable from an n-arry set of algorithms. We then introduced the e-speed start which uses the prevailing network condition to determine a suitable starting speed.

Hate speech detection in Twitter using hybrid embeddings and improved cuckoo search-based neural networks

Purpose-Hate speech is an expression of intense hatred.Twitter has become a popular analytical tool for the prediction and monitoring of abusive behaviors.Hate speech detection with social media data has witnessed special research attention in recent studies,hence,the need to design a generic metadata architecture and efficient feature extraction technique to enhance hate speech detection.Design/methodology/approach-This study proposes a hybrid embeddings enhanced with a topic inference method and an improved cuckoo search neural network for hate speech detection in Twitter data.The proposed method uses a hybrid embeddings technique that includes Term Frequency-Inverse Document Frequency(TF-IDF)for word-level feature extraction and Long Short Term Memory(LSTM)which is a variant of recurrent neural networks architecture for sentence-level feature extraction.The extracted features from the hybrid embeddings then serve as input into the improved cuckoo search neural network for the prediction of a tweet as hate speech,offensive language or neither.Findings-The proposed method showed better results when tested on the collected Twitter datasets compared to other related methods.In order to validate the performances of the proposed method,t-test and post hoc multiple comparisons were used to compare the significance and means of the proposed method with other related methods for hate speech detection.Furthermore,Paired Sample t-Test was also conducted to validate the performances of the proposed method with other related methods.Research limitations/implications-Finally,the evaluation results showed that the proposed method outperforms other related methods with mean F1-score of 91.3.Originality/value-The main novelty of this study is the use of an automatic topic spotting measure based on na€ıve Bayes model to improve features representation.

CA-KSE: a combinatorial algorithm for benchmarking in knowledge sharing environment

Purpose–Over the past decade,the cost of product development has increased drastically,and this is due to the inability of most enterprises to locate suitable and optimal collaborators for knowledge sharing.Nevertheless,knowledge sharing is a mechanism that helps people find the best collaborators with relevant knowledge.Hence,a new approach for locating optimal collaborators with relevant knowledge is needed,which couldhelp enterprisein reducingcost andtime ina knowledge-sharingenvironment.Thepaper aimsto discuss these issues.Design/methodology/approach–One unique challenge in the domain of knowledge sharing is that collaborators do not possess the same number of events resident in the knowledge available for sharing.In this paper,the authors present a new approach for locating optimal collaborators in knowledge-sharing environment using the combinatorial algorithm(CA-KSE).Findings–The proposed pattern-matching approach implemented in Java is considered efficient for solving the issue peculiar to collaboration in knowledge-sharing domain.The authors benchmarked the proposed approach with its semi-global pairwise alignment and global alignment counterparts through scores comparison and the receiver operating characteristic curve.The results obtained from the comparisons showedthat CA-KSEis a perfect test havinganarea undercurveof 0.9659,comparedto the other approaches.Research limitations/implications–The paper has proposed an efficient algorithm,which is considered better than related methods,for matching several collaborators(more than two)in KS environment.The method could be deployed in medical field for gene analysis,software organizations for distributed development and academics for knowledge sharing.Originality/value–One sign of strength of this approach,compared to most sequence alignment approaches that can only match two collaborators at a time,is that it can match several collaborators at a faster rate.