In this study, we address the task of discerning gender through the textual content of social media, a crucial step in detecting and mitigating counterfeit account activity. Ensuring accurate gender portrayal on digital platforms is essential for creating a secure and inclusive cyberspace. While research exists for languages like English, Russian, and Arabic, Bangla remains underexplored. To address this, we compiled 15,000 Bangla posts from Facebook groups, profiles, pages, blogs, and forums. We trained seven traditional machine learning algorithms (NB, SVM, LR, DT, RF, SGD, KNN) and three deep learning models (MLP, LSTM, GRU), using stylometric features, Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings. Traditional models generally outperformed deep learning models, except with stylometric features. Notably, the Stochastic Gradient Descent (SGD) model with TF-IDF achieved the highest accuracy (78.33%) and F1-Score (87.67%). Additionally, Continuous Bag of Words (CBOW) out-performed Skip-Gram (SG) in training the word2vec model, with top accuracy and F1-Score of 75.13% and 79.92%, respectively. These findings represents a significant stride forward in the field of gender identification from Bangla text.

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

Gender identification from social media content can play a crucial role in detecting and mitigating the risks posed by counterfeit accounts. Authentic gender representation can foster a safer and more diverse online environment. While research has been conducted on gender identification in languages such as English, Russian, and Arabic, there remains a gap in studies targeting Bangla and its related languages. This paper introduces a stylometric feature approach to discern the gender of authors from Bangla texts. Utilizing a dataset of 5,000 posts sourced from various Facebook groups, we trained seven traditional machine learning models. Among these, the Random Forest (RF) model notably excelled, achieving an accuracy of 73.37% and an F1-Score of 79.25%, thus setting a promising benchmark in gender identification from Bangla texts.

Abstract User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP‐net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP‐net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP‐net model that we call the CP‐net with Comfort (CPC‐net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC‐net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC‐net. The exact relation can be obtained by performing dominance testing in the corresponding CP‐net and comparing the numeric comforts.

The Conditional Preference Network (CP-net) represents user's conditional ceteris paribus (all else being equal) preference statements in a graphical manner. In general, an acyclic CP-net induces a strict partial order over the outcomes. The task of comparing two outcomes (dominance testing) is generally PSPACE-complete, which is a limitation for this intuitive model, especially when representing and solving preference-based constrained optimization problems. In order to overcome this limitation in practice, we propose a divide and conquer algorithm that compares two outcomes according to dominance testing. The algorithm divides the original CP-net into sub CP-nets, and recursively calls itself for each of the sub CP-nets until it reaches to a termination criterion. In the termination criterion, the answer of the dominance query is returned. With a theoretical analysis of the time performance, we demonstrate that the proposed algorithm outperforms the existing methods.

In multi-attribute preference-based reasoning, the CP-net is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. This paper outlines three aspects of the CP-net. First, when a CP-net is involved with a set of hard constraints, solving the Constrained CP-net requires dominance testing which is a very expensive operation. We tackle this problem by extending the CP-net model such that dominance testing is not needed. Second, user's choices involve habitual behavior and genuine decision. The former is represented using preferences, while we introduce the notion of comfort to represent the latter. Then, we suggest an extension of the CP-net which can represent both preference and comfort. Third, preferences often come with noise and uncertainty. In this regard, we suggest the probabilistic extension of the Tradeoff-enhanced CP-net (TCP-net) model. The necessary semantics and usefulness of the extensions above are described. Finally, we outline some in-progress and future work.

In preference-based constrained optimization problems, helping users by providing the most preferable feasible outcome is crucial. The Lexicographic Preference Tree (LP- tree) and the Conditional Preference Network (CP-net) are two fundamental graphical models to represent and reason about user's qualitative preferences. In this paper, we extend the LP- tree with a set of hard feasibility constraints, and then we propose a recursive backtrack search algorithm that we call Search-LP to find the most preferable feasible outcome for the Constrained LP-tree. Search-LP instantiates the variables with respect to a hierarchical order defined by the LP-tree. Given that the LP-tree represents a total order over the outcomes, Search-LP simply returns the first feasible outcome. We prove that this returned outcome is also preferable to every other feasible outcome. The main advantage of Search-LP is that it does not require dominance testing (the task of comparing two outcomes using preferences) to find the optimal solution(s), while dominance testing is a very expensive operation in the case of Constrained CP-nets.

The Conditional Preference Network (CP-net) graphically represents user's qualitative and conditional preference statements under the ceteris paribus (all else being equal) interpretation. Given that the CP-net induces a partial order over the outcomes, the optimization for a constrained CP-net can have a set of Pareto optimal solutions. The existing algorithms for solving the constrained CP-net require dominance testing between the outcomes, which is a very expensive operation. In this paper, we propose an extension of the CP-net model by eliciting additional relative importance statements between variables in order to have a total order over the outcomes. As a result, the constrained optimization using the proposed model involves a single optimal solution, assuming the underlying CSP is consistent. In this regard, we provide an efficient algorithm to find this optimal solution without the need for dominance testing.

User's choices involve habitual behavior and genuine decisions. Habitual behavior is often represented using preferences. The Conditional Preference Network (CP-net) graphically represents user's conditional ceteris paribus preference statements. We argue that genuine decisions are environmentally influenced and introduce the notion of "comfort" to represent this type of choices. In this paper, we propose an extension of the CP-net model, that we call the CP-net with Comfort (CPC-net), to represent user's comfort with preferences. A CPC-net has two optimal outcomes, one for preferences and another for comfort. We show that these two outcomes can be obtained in linear time on the number of variables for an acyclic CPC-net. We also evaluate outcome comparison queries with respect to CPC-nets.

The Conditional Preference Network (CP-net) is a graphical tool for representing and reasoning about user's conditional ceteris paribus preference statements. In case the user provides partial preferences, we get a partial CP-net. In this paper, we propose a novel algorithm, that we call Search-Partial-CP, to find the Pareto optimal outcomes with respect to an acyclic partial CP-net and a set of hard constraints. Search-Partial-CP is a backtrack search algorithm that utilizes the topological order of the related Directed Acyclic Graph (DAG) to order the variables, as well as the topological order of the partial preferences to order the values, during the instantiation process. Search-Partial-CP can significantly reduce the search space by pruning every infeasible or dominated outcome. We present and discuss the formal properties of Search-Partial-CP.