In 2019 there was an outbreak of coronavirus pandemic also known as COVID-19. Many scientists believe that the pandemic originated from Wuhan, China, before spreading to other parts of the globe. To reduce the spread of the disease, decision makers encouraged measures such as hand washing, face masking, and social distancing. In early 2021, some countries including the United States began administering COVID-19 vaccines. Vaccination brought a relief to the public; it also generated a lot of debates from anti-vaccine and pro-vaccine groups. The controversy and debate surrounding COVID-19 vaccine influenced the decision of several people in either to accept or reject vaccination. Because of data limitations, social media data, collected through live streaming public tweets using an Application Programming Interface (API) search, is considered a viable and reliable resource to study the opinion of the public on Covid-19 vaccine hesitancy. Thus, this study examines 3 sentiment computation methods (Azure Machine Learning, VADER, and TextBlob) to analyze COVID-19 vaccine hesitancy. Five learning algorithms (Random Forest, Logistics Regression, Decision Tree, LinearSVC, and Naïve Bayes) with different combination of three vectorization methods (Doc2Vec, CountVectorizer, and TF-IDF) were deployed. Vocabulary normalization was threefold; potter stemming, lemmatization, and potter stemming with lemmatization. For each vocabulary normalization strategy, we designed, developed, and evaluated 42 models. The study shows that Covid-19 vaccine hesitancy slowly decreases over time; suggesting that the public gradually feels warm and optimistic about COVID-19 vaccination. Moreover, combining potter stemming and lemmatization increased model performances. Finally, the result of our experiment shows that TextBlob + TF-IDF + LinearSVC has the best performance in classifying public sentiment into positive, neutral, or negative with an accuracy, precision, recall and F1 score of 0.96752, 0.96921, 0.92807 and 0.94702 respectively. It means that the best performance was achieved when using TextBlob sentiment score, with TF-IDF vectorization and LinearSVC classification model. We also found out that combining two vectorizations (CountVectorizer and TF-IDF) decreases model accuracy.

We present a distributed algorithm for a multiagent system in a collaborative setting including the cost of actions. We revise the distributed Max-Plus algorithm by presenting a new Cost Distributed Max-Plus (CD Max-Plus) algorithm by including the cost of actions in the interactions of agents, named Factored Value-MCTS-CD Max-Plus algorithm. The proposed Factored Value-MCTS-CD Max-Plus algorithm is online, anytime, distributed, and scalable in number of agents and their interactions. Our contribution is an alternative solution for solving cost Dec-POMDPs competing with the state of art methods and algorithms using MCTS and Max-Plus algorithms to exploit the locality of agent interactions for planning and acting.

We focus on a real-time multiagent decision making problem in a collaborative setting including a cost factor for the planning and execution of actions. We present the centralized coordination of a multiagent system in which the team must make a collaborative decision to maximize the global payoff. We used the framework of Coordination Graphs, which exploit dependencies among agents to decompose the global payoff function value as the sum of local terms. We revise the centralized Max-Plus algorithm by presenting a new Cost Max-Plus algorithm for planning and acting by including the cost in the local interactions of agents. We propose a two-step planning and acting algorithm called Factored Value-MCTS-Cost-Max-Plus algorithm that is online, anytime, and scalable in terms of the number of agents and their local interactions.

We focus on the detection of Low Rate Denial of Service Attacks (LR DoS) based on their spectral properties. We revised the Fisher g-statistic test and Siegel test for detection of LR DoS attacks such as Shrew and New Shrew attacks. Our results reveal that these attacks can be effectively identified by using Fisher g-statistic test for one dominant periodicity as in the case of Shrew attacks. When multiple (compound) dominant periodicities exist in the case of New Shrew attacks, Siegel test can identify the attack better than Fisher g-statistic test. Our contribution consists in developing a new methodology for rejecting the null hypothesis or identifying LR DoS attacks. Numerical examples for a confidence interval at significance level of α = 0.01 and α = 0.05 are given for Shrew and New Shrew attacks.

We characterize the Total Weighted Squared Correlation (TWSC) used in uplink synchronous code division multiple access (S-CDMA) as a payoff function in the context of the game theoretic framework. By using majorization theory we proved that the value of the game for two players is a saddle-point corresponding to the minimum of TWSC. The optimal solution is the Stackelberg equilibrium and is achieved by players choosing the generalized Welch Bound Equality (WBE) sequences as spreading signatures. By deriving the optimum strategy for two players we propose a better method of minimizing TWSC when channel state information is known exactly at both the transmitter and receiver. A comparison of our method in terms of the water filling procedure with the previous methods and the Karystinos-Pados bound is also given.

A procedure for the design of complex sequence/signature sets for a multicell system under synchronous multipath transmission is presented. The optimization criterion is the total weighted square correlation (TWSC) that incorporates transmission power and channel impulse response values. By solving an inverse eigenvalue problem related to block structured matrices, the bandwidth of initial generalized Welch bound equality signals is preserved in the extended signal space associated to multiple cell system. Numerical results for overloaded uplink multicellular direct-sequence code-division multiple-access (DS-CDMA) systems are presented in order to support our analysis.

A composite Gram matrix approach is used in order to minimize the total weighted squared correlation on uplink of overloaded multipath multibase synchronous DS-CDMA systems. A characterization of the weighted Gram matrix in terms of its eigenvalues is given. The optimum eigenvalue vector distribution of this matrix is derived. We developed an algorithm that preserve the bandwidth of generalized WBE sequences from each cell in the multidimensional signal space associated with multicellular system. Numerical examples for the design of spreading sequences with unequal users' power constraint for two cells in the presence of multipath are provided.

An algorithm for designing spreading sequences for an overloaded multicellular synchronous DS-CDMA system, on the uplink, is introduced. The criterion used to measure the optimality of the design is the total weighted square correlation (TWSC), assuming that the channel state information is known perfectly both at transmitter and receiver. By using this algorithm, it is possible to obtain orthogonal generalized WBE (Welch bound equality) sequence sets for any processing gain. The bandwidth of the initial generalized WBE signals of each cell is preserved in the extended signal space associated with the multicellular system. Mathematical formalism is illustrated by selected numerical examples.

A definition of total weighted square correlation (TWSC) for an overloaded multicellular synchronous DS-CDMA system in the presence of multipath on the uplink is introduced. We assume the channel state information is known perfectly both at transmitter and receiver. The optimum solutions for minimizing TWSC are the orthogonal generalized WBE (Welch bound equality) spreading sequences. The design of such sequences in the context of cooperative multibase DS-CDMA systems under the TWSC criterion is presented.

Energy efficiency is a measure of performance in wireless networks. Therefore, controlling the transmitter power at a given node increases not only the operating life of the battery but also the overall system capacity by successfully admitting new nodes between a source and a destination. It is essential to find effective means of power control of point-to-point, broadcasting and multicasting scenarios. In past work A. T. Chronopoulos et al. (2002), we presented a new scheme state space-based control design (SSCD) using both the state space and optimal control methodology in discrete-time for power control in wireless systems. Further, we proved the convergence of the overall network with our algorithm using Lyapunov stability analysis. We made a comparison with a well known distributed power control (DPC) scheme. Here we present simulation results and comparisons for point-to-point communication with random node placement. We also combined the schemes SSCD and DPC with a tree based broadcast algorithm BIP to obtain broadcast tree in ad-hoc networks with power control. We show the effectiveness of the new algorithm through simulations.

We analyze the extended total weighted squared correlation (ETWSC) for a given signature sequence set used in slow flat multipath channels and in the presence of colored noise. extended total square correlation (ETSC) and total squared correlation (TSC) are particular cases of ETWSC and are considered as well. The main results of this paper are: 1) the conditions on the channel such that the received multipath sequences are WBE (Welch bound Equality) sequences and 2) an iterative distributed method for obtaining real sequences that meet the Welch's lower bound on ETWSC. The method is based on majorization theory decreasing the weighted Frobenius norm associated with users' Sylvester channel matrices and noise covariance matrix. Numerical results for overloaded DS-CDMA systems are presented in order to support our analysis.

We define the total generalized square correlation (TGSC) for a given signature sequence set used in code division multiple access (CDMA) systems in the presence of colored noise. Total square correlation (TSC) and total weighted correlation (TWSC) are particular cases of TGSC and are considered as well. We provide an iterative and distributed method for obtaining sequences that minimize TGSC. The method is based on majorization theory decreasing the weighted Frobenius norm associated with users' Gram signature matrix.

We characterize total weighted square correlation (TWSC) for a given signature set used in synchronous code division multiple access (S-CDMA) systems. A new theorem for TWSC and a new algorithm for obtaining real sequences that meet Welch inequality with equality are presented. Using this algorithm optimal sequences, which achieve sum capacity for overload S-CDMA systems are obtained. Numerical examples for unitary power constraint case and for arbitrary (unequal) power sets are given.