University of Southeastern Philippines

Injection attacks persist as dominant threats in modern web systems due to obfuscation, polymorphism, and multi-vector exploitation across SQLi, XSS, LDAP Injection, and Command Injection. Existing defenses often rely on static signatures or single-vector models, which limit generalization under adversarial payload mutation. This study addressed that limitation by designing and evaluating a unified multi-vector detection framework that integrated Natural Language Processing (NLP) and Deep Q-Network (DQN) Reinforcement Learning (RL) within a structured Design-Development-Research methodology. The study consolidated heterogeneous open-source datasets comprising 346,954 benign and 653,046 malicious XSS samples, 107,328 benign and 136,746 malicious SQLi samples, 1,591 benign and 515 malicious Command Injection samples, and 1,100 benign and 900 malicious LDAP Injection samples. The pipeline operationalized canonicalized payloads as inputs, hybrid lexical-semantic feature extraction and supervised classification as processes, and probabilistic attack decisions with calibrated thresholds as outputs. The NLP pipeline fused TF-IDF character n-grams with transformer embeddings to preserve structural and contextual signatures. Logistic Regression and One-vs-Rest Linear SVM achieved strong discrimination under group-aware splits, while the DQN agent optimized decision thresholds using reward-based calibration without modifying classifier parameters. Results demonstrated stable ROC and Precision-Recall performance, coherent embedding separation, and convergence of reinforcement learning rewards and loss. The deployed system was evaluated using ISO/IEC 25010 functional suitability criteria, including functional completeness, correctness, and appropriateness, to verify that the detection pipeline executed all required operations and produced reliable decision outputs and explainable, confidence-supported decisions. The framework strengthened secure digital infrastructure, contributing to resilient innovation ecosystems aligned with Sustainable Development Goals 9 and 16.

Let η be a fixed positive integer. Let S be a subset of ℤ, ⋆:S× S→ℤ be a binary function, and ζ_η:{ξ∈ℤ:(ξ,η)=1}→{0,1} be a function. For a simple connected graph G of order n, a bijective function f:V(G)→ S (where |S|=n) is called an arithmetic cordial labeling modulo η under ⟨ S,ζ_η,⋆⟩ if the induced function f_η^*:E(G)→{0,1}, defined by f_η^*(uv)=0 whenever ζ_η(f(a)⋆ f(b))=0 or (f(a)⋆ f(b),η)≠ 1, and f_η^*(uv)=1 whenever ζ_η(f(a)⋆ f(b))=1, satisfies the condition |e_f_η^*(0)-e_f_η^*(1)|≤ 1, where e_f_η^*(i) is the number of edges with label i (i=0,1). In this paper, we explore the arithmetic cordial labeling of some graphs under conditions imposed on the function ζ_η. The graphs included are star graphs, ladder graphs, alternate cycle snake graphs, join graphs, corona graphs, and tensor product graphs.

Vegetable price volatility poses a serious challenge to food security and agricultural sustainability, particularly in developing countries such as the Philippines. Fluctuations in prices disrupt market efficiency, reduce producer and consumer welfare, and hinder long-term planning in the agricultural sector. This study investigates the drivers of price volatility in two key condiment vegetables—onion and garlic—which are highly traded and politically sensitive commodities in the Philippines. Using quarterly data from 2010 to 2024, this study decomposes farm, wholesale, and retail price variances into demand, supply, and import components. A variance decomposition framework was employed, complemented by Monte Carlo simulations to test the robustness of elasticity estimates under data-scarce and uncertain conditions. The findings reveal that demand volatility is the dominant driver of price fluctuations, explaining 50–90

Let p be an odd prime and let F_i be the ith (a,b)-Fibonacci number with initial values F_0=a and F_1=b. For a simple connected graph G=(V,E), define a bijective function f:V(G)→{0,1,…,|V|-1}. If the induced function f_p^*:E(G)→{0,1}, defined by f_p^*(uv)=1+([F_f(u)+F_f(v)]/p)/2 whenever F_f(u)+F_f(v)≢0p and f_p^*(uv)=0 whenever F_f(u)+F_f(v)≡ 0p, satisfies the condition |e_f_p^*(0)-e_f_p^*(1)|≤ 1 where e_f_p^*(i) is the number of edges labeled i (i=0,1), then f is called (a,b)-Fibonacci-Legendre cordial labeling modulo p. In this paper, the (a,b)-Fibonacci-Legendre cordial labeling of path graphs, star graphs, wheel graphs, and graphs under the operations join, corona, lexicographic product, cartesian product, tensor product, and strong product is explored in relation to k-Pisano-Legendre primes relative to (a,b). We also present some properties of k-Pisano-Legendre primes relative to (a,b) and numerical observations on its distribution, leading to several conjectures concerning their density and growth behavior.

This chapter examines gender-neutral language as a sociocultural, linguistic, and political phenomenon that shapes how identities, power relations, and social hierarchies are constructed and contested. It traces the historical development of gendered language, analyzes how linguistic structures perpetuate inequality, and presents theoretical frameworks from feminist linguistics, critical sociolinguistics, and queer studies. The discussion highlights cross-cultural variations, global activism, and the influence of technology, media, and education on language reform. By synthesizing research on stereotypes, policy shifts, and emerging inclusive practices, the chapter underscores the transformative potential of gender-neutral language in promoting equity, affirming diverse identities, and reshaping public discourse.