arXiv subjects

Asef Nazari

Publications and source records attributed to Asef Nazari.

17 recordsLinked to original sources

arXiv2026

Overcoming Tight Constraints in Soft Happy Colouring

The Soft Happy Colouring (SHC) problem, a mathematical framework for identifying homophilic network structures, seeks to maximise the number of $\rho$-happy vertices, i.e., vertices with at least a proportion $\rho$ of neighbours that share the same colour. Because this NP-hard problem makes finding exact solutions intractable for large networks, probabilistic metaheuristics such as the Cross-Entropy (CE) method are suitable candidates. However, pure CE frequently suffers from stagnation of the probability distributions and non-convergence in high-dimensional spaces. To address this, we introduce {\sf CE+LS}, synergising CE's adaptive learning with a fast, structure-aware local search ({\sf LS}). By restricting the search exclusively to local optima, {\sf CE+LS} learns from high-quality structural characteristics rather than raw random samples. We mathematically and empirically demonstrate that this search space reduction resolves CE's stagnation, yielding a convergent algorithm. Evaluating {\sf CE+LS} across 28,000 Stochastic Block Model graphs, validated by non-parametric statistical testing, demonstrates that it consistently outperforms existing heuristic and memetic algorithms. Furthermore, benchmarking against the commercial exact solver, CPLEX, on real-world networks confirms that {\sf CE+LS} identifies near-optimal configurations in a fraction of the required computational time for CPLEX. Crucially, {\sf CE+LS} remains highly efficient even in the tight constraint regime, where comparative algorithms usually fail.

cs.SI↗

arXiv2026

Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty

Residential users in demand response programs must balance electricity costs and user dissatisfaction under real-time pricing. This study proposes a multiobjective model predictive control approach for home energy management systems with battery storage, aiming to minimize both objectives while mitigating uncertainties. Laguerre functions parameterize control signals, transforming the optimization problem into one with linear inequalities for efficient exploration. A constrained multiobjective evolutionary algorithm, incorporating convex sampler-based crossover and mutation, is developed to ensure feasible solutions. Simulations show that the proposed method outperforms existing approaches, limiting cost increases to 0.52\% under uncertainties, compared to at least 2.3\% with other methods.

eess.SY↗

arXiv2025

Audio Physical Dynamics Inspired Deepfake Detection for Voice Authentication Systems

Voice authentication systems deployed at the network edge face dual threats: a) sophisticated deepfake synthesis attacks and b) control-plane poisoning in distributed federated learning protocols. We present a framework coupling audio physical dynamics deepfake detection with uncertainty-aware in edge learning. The framework fuses interpretable physics features modeling vocal tract dynamics with representations coming from a self-supervised learning module. The representations are then processed via a streamlined Multi-Layer Perceptron backbone, followed by a Bayesian ensemble providing uncertainty estimates. Incorporating audio physical characteristics evaluations and uncertainty estimates of audio samples allows our proposed framework to remain robust to advanced deepfake attacks, while our trust-based aggregation protocol secures the control plane against poisoning in network edge voice authentication systems.

cs.SD↗

arXiv2025

Finding happiness by evolutionary algorithms

A recent line of research concerns the problem of soft happy colouring (SHC), which requires that a partially coloured graph be extended to a complete colouring to maximise local agreements, so that as many vertices as possible end up surrounded by enough same-coloured neighbours. It is already known that SHC is NP-hard, and its solutions have a direct relationship with the community structure of networks; thus, it has immense applications in security and resilience. Past studies have shown that local search approaches can be fast and effective to an extent on the SHC; however, they often get stuck in local optima. Regarding the related problem of maximising happy vertices, evolutionary approaches have been proven effective; hence, this study develops a customised memetic algorithm that is a hybrid of genetic algorithms and local search. The experimental evaluation on a range of graphs in the stochastic block model shows that the memetic algorithm can achieve excellent results in search for an optimised solution to SHC compared to the local search approaches and standard genetic algorithms. Moreover, learning and evolution in the memetic algorithm allow diversification of solutions generated by fast, effective local search approaches, which prove superior for the challenging problem of community detection.

cs.DM↗

arXiv2025

A Bi-Objective Mathematical Model for the Multi-Skilled Resource-Constrained Project Scheduling Problem Considering Reliability: An AUGMECON2VIKOR Hybrid Method

In recent years, resources with multiple skills have received attention as an extension of the resource-constrained project scheduling problem known as MSRCPSP. Although the disruption rate is well-estimated in today's manufacturing projects, its impact on project makespan and cost need further investigation. Hence, this study presents a novel mathematical model for the MSRCPSP considering reliability, namely MSRCPSPR. The model proposes both objectives of minimizing project makespan and project cost. The MSRCPSP is an NP-hard problem, and including reliability constraints, as proposed in this paper, makes solving the problem more intractable. To cope with the computational challenges of solving the problem, a combination of an enhanced version of the epsilon-constraint method as well as an augmented version of the VIKOR algorithm, namely AUGMECON2VIKOR, is employed to solve benchmark instances j10 and j20 from the PSPLIB. A comparative analysis demonstrates the performance of the proposed method, and the sensitivity analysis represents the effects of positive reliable constraints on the objective functions. Employing the proposed method, the project makespan and cost are reduced by nearly 2.55% and 2.80% in j10 on average. CPU time is also decreased by about 543 seconds in comparison to the epsilon-constraint method.

math.OC↗

arXiv2025

Local Search Improvements for Soft Happy Colouring

For $0\leq \rho\leq 1$ and a coloured graph $G$, a vertex $v$ is $\rho$-happy if at least $\rho \mathrm{deg}(v)$ of its neighbours have the same colour as $v$. Soft happy colouring of a partially coloured graph $G$ is the problem of finding a vertex colouring $\sigma$ that preserves the precolouring and has the maximum number of $\rho$-happy vertices. It is already known that this problem is NP-hard and directly relates to the community structure of the graphs; under a certain condition on the proportion of happiness $\rho$ and for graphs with community structures, the induced colouring by communities can make all the vertices $\rho$-happy. We show that when $0\leq \rho_1<\rho_2\leq 1$, a complete $\rho_2$-happy colouring has a higher accuracy of community detection than a complete $\rho_1$-happy colouring. Moreover, when $\rho$ is greater than a threshold, it is unlikely for an algorithm to find a complete $\rho$-happy colouring with colour classes of almost equal sizes. Three local search algorithms for soft happy colouring are proposed, and their performances are compared with one another and other known algorithms. Among them, the linear-time local search is shown to be not only very fast, but also a reliable algorithm that can dramatically improve the number of $\rho$-happy vertices.

cs.DM↗

arXiv2025

Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can exploit both attacks simultaneously. We propose a unified defense framework that effectively addresses both BTA and TDPA. Our framework integrates a frequency focused detection mechanism that flags covert pitch boosting and sound masking backdoor attacks in near real time, followed by a convolutional neural network that addresses TDPA. This dual layered defense approach utilizes multidimensional acoustic features to isolate anomalous signals without requiring costly model retraining. In particular, our PBSM detection mechanism can seamlessly integrate into existing voice authentication pipelines and scale effectively for large scale deployments. Experimental results on benchmark datasets and their compression with the state of the art algorithm demonstrate that our PBSM detection mechanism outperforms the state of the art. Our framework reduces attack success rates to as low as five to fifteen percent while maintaining a recall rate of up to ninety five percent in recognizing TDPA.

cs.CR↗

arXiv2024

Securing Voice Authentication Applications Against Targeted Data Poisoning

Deep neural network-based voice authentication systems are promising biometric verification techniques that uniquely identify biological characteristics to verify a user. However, they are particularly susceptible to targeted data poisoning attacks, where attackers replace legitimate users' utterances with their own. We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction (5% of the dataset) is poisoned.

cs.CR↗

arXiv2024

Soft happy colourings and community structure of networks

For $0<\rho\leq 1$, a $\rho$-happy vertex $v$ in a coloured graph $G$ has at least $\rho\cdot \mathrm{deg}(v)$ same-colour neighbours, and a $\rho$-happy colouring (aka soft happy colouring) of $G$ is a vertex colouring that makes all the vertices $\rho$-happy. A community is a subgraph whose vertices are more adjacent to themselves than the rest of the vertices. Graphs with community structures can be modelled by random graph models such as the stochastic block model (SBM). In this paper, we present several theorems showing that both of these notions are related, with numerous real-world applications. We show that, with high probability, communities of graphs in the stochastic block model induce $\rho$-happy colouring on all vertices if certain conditions on the model parameters are satisfied. Moreover, a probabilistic threshold on $\rho$ is derived so that communities of a graph in the SBM induce a $\rho$-happy colouring. Furthermore, the asymptotic behaviour of $\rho$-happy colouring induced by the graph's communities is discussed when $\rho$ is less than a threshold. We develop heuristic polynomial-time algorithms for soft happy colouring that often correlate with the graphs' community structure. Finally, we present an experimental evaluation to compare the performance of the proposed algorithms thereby demonstrating the validity of the theoretical results.

cs.DM↗

arXiv2024

Risk-Constrained Community Battery Utilisation Optimisation for Electric Vehicle Charging with Photovoltaic Resources

High penetration of renewable generation in the electricity grid presents power system operators with challenges including voltage instability mainly due to fluctuating power generation. To cope with intermittent generation, community batteries introduce an elegant solution for storing excess generation of renewable resources and reverting to the grid in peak demand periods. The question of the right battery type and size coupled with the right investment is challenging. Furthermore, the growth in adapting EVs imposes additional demand challenges on the power system compared to traditional industrial and household demand. This paper introduces long-term planning for community batteries to capture the surplus generation of PV resources for a given area and redirect these resources to charge EVs, without direct injection to the grid. For long-term investment planning on batteries, we consider 15 years' worth of historical data associated with solar irradiance, temperature, EV demands, and household demands. A novel stochastic mathematical model is proposed for decision-making on battery specifications (the type and capacity per year) based on the four standard battery types provided by the CSIRO in Australia. Uncertainties related to the EVs and RESs are captured by a non-parametric robust technique, named information gap decision theory, from optimistic and pessimistic perspectives. The investment decision-making part is formulated as mixed-integer linear programming taking advantage of the powerful commercial solver -- GUROBI -- which leads to finding feasible global solutions with low computational burden. The outcomes of this investigation not only detect optimal battery installation strategies to improve the stability profile of the grid by capturing the excess generation of PV resources but also facilitate EV integration in the community toward reaching net-zero emissions targets.

math.OC↗

arXiv2023

SALSA: Sequential Approximate Leverage-Score Algorithm with Application in Analyzing Big Time Series Data

We develop a new efficient sequential approximate leverage score algorithm, SALSA, using methods from randomized numerical linear algebra (RandNLA) for large matrices. We demonstrate that, with high probability, the accuracy of SALSA's approximations is within $(1 + O({\varepsilon}))$ of the true leverage scores. In addition, we show that the theoretical computational complexity and numerical accuracy of SALSA surpass existing approximations. These theoretical results are subsequently utilized to develop an efficient algorithm, named LSARMA, for fitting an appropriate ARMA model to large-scale time series data. Our proposed algorithm is, with high probability, guaranteed to find the maximum likelihood estimates of the parameters for the true underlying ARMA model. Furthermore, it has a worst-case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale data strongly support these theoretical results and underscore the efficacy of our new approach.

stat.ML↗

arXiv2023

A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading

Despite advances in artificial intelligence-enhanced trading methods, developing a profitable automated trading system remains challenging in the rapidly evolving cryptocurrency market. This research focuses on developing a reinforcement learning (RL) framework to tackle the complexities of trading five prominent altcoins: Binance Coin, Ethereum, Litecoin, Ripple, and Tether. To this end, we present the CausalReinforceNet~(CRN) framework, which integrates both Bayesian and dynamic Bayesian network techniques to empower the RL agent in trade decision-making. We develop two agents using the framework based on distinct RL algorithms to analyse performance compared to the Buy-and-Hold benchmark strategy and a baseline RL model. The results indicate that our framework surpasses both models in profitability, highlighting CRN's consistent superiority, although the level of effectiveness varies across different cryptocurrencies.

cs.AI↗

arXiv2023

Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships

Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. Despite their growing popularity, cryptocurrencies can be a high-risk investment due to their price volatility. The inherent volatility in cryptocurrency prices, coupled with the effects of external global economic factors, makes predicting their price movements challenging. To address this challenge, we propose a dynamic Bayesian network (DBN)-based approach to uncover potential causal relationships among various features including social media data, traditional financial market factors, and technical indicators. Six popular cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether are studied in this work. The proposed model's performance is compared to five baseline models of auto-regressive integrated moving average, support vector regression, long short-term memory, random forests, and support vector machines. The results show that while DBN performance varies across cryptocurrencies, with some cryptocurrencies exhibiting higher predictive accuracy than others, the DBN significantly outperforms the baseline models.

cs.LG↗

arXiv2023

Causal Modelling of Cryptocurrency Price Movements Using Discretisation-Aware Bayesian Networks

This study identifies the key factors influencing the price movements of major cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether, using Bayesian networks (BNs). This study addresses two key challenges: modelling price movements in highly volatile cryptocurrency markets and enhancing predictive performance through discretisation-aware Bayesian Networks. It analyses both macro-financial indicators (gold, oil, MSCI, S and P 500, USDX) and social media signals (tweet volume) as potential price drivers. Moreover, since discretisation is a critical step in the effectiveness of BNs, we implement a structured procedure to build 54 BNs models by combining three discretisation methods (equal interval, equal quantile, and k-means) with several bin counts. These models are evaluated using four metrics, including balanced accuracy, F1 score, area under the ROC curve and a composite score. Results show that equal interval with two bins consistently yields the best predictive performance. We also provide deeper insights into each network's structure through inference, sensitivity, and influence strength analyses. These analyses reveal distinct price-driving patterns for each cryptocurrency, underscore the importance of coin-specific analysis, and demonstrate the value of BNs for interpretable causal modelling in volatile cryptocurrency markets.

q-fin.ST↗

arXiv2022

Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training

In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point approximation to the secant equation which quasi-Newton methods are based upon. Moreover, the adaptive learning rate method presented here is quite general in nature and can be applied to widely used gradient descent approaches such as Adagrad and RMSprop. We evaluate our method using standard example network architectures on widely available datasets and compare against alternatives elsewhere in the literature. In our experiments, our adaptive learning rate shows a smoother and faster convergence than that exhibited by the alternatives, with better or comparable performance.

cs.LG↗

arXiv2021

LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data

We apply methods from randomized numerical linear algebra (RandNLA) to develop improved algorithms for the analysis of large-scale time series data. We first develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. We show that the accuracy of approximations lies within $(1+\bigO{\varepsilon})$ of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Our proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach.

stat.ME↗

arXiv2020

A hybrid deep-learning approach for complex biochemical named entity recognition

Named entity recognition (NER) of chemicals and drugs is a critical domain of information extraction in biochemical research. NER provides support for text mining in biochemical reactions, including entity relation extraction, attribute extraction, and metabolic response relationship extraction. However, the existence of complex naming characteristics in the biomedical field, such as polysemy and special characters, make the NER task very challenging. Here, we propose a hybrid deep learning approach to improve the recognition accuracy of NER. Specifically, our approach applies the Bidirectional Encoder Representations from Transformers (BERT) model to extract the underlying features of the text, learns a representation of the context of the text through Bi-directional Long Short-Term Memory (BILSTM), and incorporates the multi-head attention (MHATT) mechanism to extract chapter-level features. In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels. Moreover, conditional random field (CRF) is used to label sequence tags because this probabilistic method does not need strict independence assumptions and can accommodate arbitrary context information. The experimental evaluation on a publicly-available dataset shows that the proposed hybrid approach achieves the best recognition performance; in particular, it substantially improves performance in recognizing abbreviations, polysemes, and low-frequency entities, compared with the state-of-the-art approaches. For instance, compared with the recognition accuracies for low-frequency entities produced by the BILSTM-CRF algorithm, those produced by the hybrid approach on two entity datasets (MULTIPLE and IDENTIFIER) have been increased by 80% and 21.69%, respectively.

cs.CL↗