QoS based Web Service Selection and Multi-Criteria Decision Making Methods

With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 knapsack problem of multiple dimensions and multiple choices, known as an NP-hard problem. Multi-Criteria Decision Making (MCDM) method is one of the ways which suits this problem and helps the users to select the best service based on his/her preferences. In this regard, this paper assists the researchers in two conducts: Firstly, to witness the performance of different MCDM methods for large number of alternatives and attributes. Secondly, to perceive the possible deviation in the ranking obtained from these methods. For carrying out the experimental evaluation, in this paper, five different well-known MCDM methods have been implemented and compared over two different scenarios of 50 as well as 100 web services, where their ranking is defined on an account of several Quality of Service (QoS) parameters. Additionally, a Spearman’s Rank Correlation Coefficient has been calculated for different pairs of MCDM methods in order to provide a clear depiction of MCDM methods showing the least deviation in their ranking. The experimental results comfort web service users in conquering an appropriate decision on the selection of suitable service

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML) methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS) security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security

A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning-based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings

N-grams Based Supervised Machine Learning Model for Mobile Agent Platform Protection against Unknown Malicious Mobile Agents

From many past years, the detection of unknown malicious mobile agents before they invade the Mobile Agent Platform has been the subject of much challenging activity. The ever-growing threat of malicious agents calls for techniques for automated malicious agent detection. In this context, the machine learning (ML) methods are acknowledged more effective than the Signature-based and Behavior-based detection methods. Therefore, in this paper, the prime contribution has been made to detect the unknown malicious mobile agents based on n-gram features and supervised ML approach, which has not been done so far in the sphere of the Mobile Agents System (MAS) security. To carry out the study, the n-grams ranging from 3 to 9 are extracted from a dataset containing 40 malicious and 40 non-malicious mobile agents. Subsequently, the classification is performed using different classifiers. A nested 5-fold cross validation scheme is employed in order to avoid the biasing in the selection of optimal parameters of classifier. The observations of extensive experiments demonstrate that the work done in this paper is suitable for the task of unknown malicious mobile agent detection in a Mobile Agent Environment, and also adds the ML in the interest list of researchers dealing with MAS security

Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation

We study how to exploit the notion of strategy templates to learn strategies for multi-issue bilateral negotiation. Each strategy template consists of a set of interpretable parameterized tactics that are used to decide an optimal action at any time. We use deep reinforcement learning throughout an actor-critic architecture to estimate the tactic parameter values for a threshold utility, when to accept an offer and how to generate a new bid. This contrasts with existing work that only estimates the threshold utility for those tactics. We pre-train the strategy by supervision from the dataset collected using "teacher strategies", thereby decreasing the exploration time required for learning during negotiation. As a result, we build automated agents for multi-issue negotiations that can adapt to different negotiation domains without the need to be pre-programmed. We empirically show that our work outperforms the state-of-the-art in terms of the individual as well as social efficiency.Comment: arXiv admin note: text overlap with arXiv:2009.0830

A randomised trial to compare 200 mg micronised progesterone effervescent vaginal tablet daily with 250 mg intramuscular 17 alpha hydroxy progesterone caproate weekly for prevention of recurrent preterm birth

For prevention of a recurrent preterm birth (PTB), intramuscular 17-α-hydroxy progesterone caproate (IM 17 OHPC) weekly is recommended. Vaginal progesterone is preferred for women at risk for PTB due to a short cervical length, but may be useful in women with a prior PTB. However, there is no consensus about the optimal vaginal formulation or its efficacy as compared to 17 OHPC to prevent recurrent PTB. We randomised 100 women with a singleton pregnancy between 16 and 24 weeks of gestation and ≥ one prior spontaneous PTB, of a singleton (>16 to <37 weeks of gestation) to receive the 200 mg vaginal progesterone effervescent tablet daily (Group A) or IM 17-OHPC, 250 mg weekly (Group B) till 37 weeks of gestation or delivery. The spontaneous PTB rate of <37 weeks was similar (20% in Group A and 20.8% in Group B, p =  .918). The PTB rate of <34 weeks or <28 weeks were also comparable. The mean birth weight and other neonatal outcomes were similar in the two groups. Two neonates in Group A and four neonates in Group B required NICU admission, one of whom (Group B) died due to prematurity. Twenty percent of women in Group A and 29.2% in Group B reported adverse effects from their respective study medications (p =  .408, NS). Thus, there did not appear to be a difference between vaginal progesterone and 17-OHPC when used for the prevention of a recurrent PTB.Impact statement What is already known on this subject? Progesterone administration is useful for prevention of a recurrent preterm birth (PTB) and these women are prescribed the intramuscular 17-α-hydroxy progesterone caproate (IM 17 OHPC), 250 mg, weekly. Some studies found that vaginal progesterone (once daily) is also beneficial in these women, but there is no consensus regarding its efficacy when compared to 17 OHPC, or its optimal formulation and dose. What do the results of this study add? In the present study, 100 women with a singleton pregnancy between 16 and 24 weeks of gestation and ≥ one prior spontaneous singleton PTB or mid-trimester abortion were randomised to receive 200 mg of vaginal progesterone effervescent tablet daily (Group A) or 250 mg IM 17-OHPC weekly (Group B) till 37 weeks of gestation or delivery. The spontaneous PTB rate <37 weeks was similar in the two groups (20% in Group A and 20.8% in Group B, p = .918). The PTB rate <34 weeks or <28 weeks were also comparable. The mean birth weight and other neonatal outcomes were similar. Twenty percent of women in Group A and 29.2% of women in Group B reported adverse effects from their respective study medications (p = .408, NS). Thus, there did not appear to be a difference between the vaginal progesterone effervescent tablet and 17-OHPC when used for the prevention of a recurrent PTB. What are the implications of these findings for clinical practice and/or further research? The vaginal progesterone effervescent tablet may be a suitable alternative to IM 17 OHPC to prevent recurrent PTB. Future studies should identify the most appropriate route (IM or vaginal) and vaginal progesterone formulation for PTB prevention in women at risk for a recurrent PTB and in women with a short cervical length