Waqas Ahmed

Waqas Ahmed, Dean Diepeveen, Ferdous Sohel

Realistic shadow generation is crucial for achieving seamless image compositing, yet existing methods primarily focus on single-object insertion and often fail to generalize when multiple foreground objects are composited into a background scene. In practice, however, modern compositing pipelines and real-world applications often insert multiple objects simultaneously, necessitating shadows that are jointly consistent in terms of geometry, attachment, and location. In this paper, we address the under-explored problem of multi-object shadow generation, aiming to synthesize physically plausible shadows for multiple inserted objects. Our approach exploits the multimodal capabilities of a pre-trained text-to-image diffusion model. An image pathway injects dense, multi-scale features to provide fine-grained spatial guidance, while a text-based pathway encodes per-object shadow bounding boxes as learned positional tokens and fuses them via cross-attention. An attention-alignment loss further grounds these tokens to their corresponding shadow regions. To support this task, we augment the DESOBAv2 dataset by constructing composite scenes with multiple inserted objects and automatically derive prompts combining object category and shadow positioning information. Experimental results demonstrate that our method achieves state-of-the-art performance in both single and multi-object shadow generation settings.

Waqas Ahmed, Sheeba Samuel, Kevin Coakley et al.

To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent and robust performance across runs. Our empirical analysis reveals that even under controlled initialization and training conditions, the accuracy of the model can exhibit significant variability. To address this issue, we propose a Custom Loss Function (CLF) that reduces the sensitivity of training outcomes to stochastic factors such as weight initialization and data shuffling. By fine-tuning its parameters, CLF explicitly balances predictive accuracy with training stability, leading to more consistent and reliable model performance. Extensive experiments across diverse architectures for both image classification and time series forecasting demonstrate that our approach significantly improves training robustness without sacrificing predictive performance. These results establish CLF as an effective and efficient strategy for developing more stable, reliable and trustworthy neural networks.

Sheeba Samuel, Daniel Mietchen, Jungsan Kim et al.

Digital libraries curate millions of research software artefacts yet lack scalable infrastructure for assessing whether those artefacts remain executable. Existing automated assessment tools treat static repository completeness -- what a repository contains -- as a proxy for execution success -- whether it runs. We term this the readiness-outcome conflation and present ReproScore, a two-tier framework that explicitly separates reproducibility readiness (RRS) from reproducibility outcome (ROS), combining them into a coverage-adaptive Composite Score (RCS). RRS comprises 26 sub-metrics across five categories; ROS provides execution-based probes when sandbox infrastructure is available; a community rubric externalises weighting priorities as versioned YAML profiles. Evaluated on 423 GitHub repositories from a large-scale ground-truth corpus spanning five failure modes, two complementary findings emerge: the environment category strongly discriminates failure mode, confirming static signals capture meaningful structural differences; yet RRS exhibits near-zero binary success correlation, empirically quantifying the readiness-outcome gap at repository scale. Together, these findings validate the architectural separation as both necessary and non-trivial, positioning ReproScore as scalable infrastructure for reproducibility-aware curation in digital library workflows.

Gulraiz Khan, Kenneth Y. Wertheim, Kevin Pimbblet et al.

Morphable Models (3DMMs) are a type of morphable model that takes 2D images as inputs and recreates the structure and physical appearance of 3D objects, especially human faces and bodies. 3DMM combines identity and expression blendshapes with a basic face mesh to create a detailed 3D model. The variability in the 3D Morphable models can be controlled by tuning diverse parameters. They are high-level image descriptors, such as shape, texture, illumination, and camera parameters. Previous research in 3D human reconstruction concentrated solely on global face structure or geometry, ignoring face semantic features such as age, gender, and facial landmarks characterizing facial boundaries, curves, dips, and wrinkles. In order to accommodate changes in these high-level facial characteristics, this work introduces a shape and appearance-aware 3D reconstruction system (named SARS by us), a c modular pipeline that extracts body and face information from a single image to properly rebuild the 3D model of the human full body.

Waqas Ahmed

The cybersecurity of Industrial Control Systems that manage critical infrastructure such as Water Distribution Systems has become increasingly important as digital connectivity expands. BATADAL benchmark data is a good source of testing intrusion detection techniques, but it presents several important problems, such as imbalance in the number of classes, multivariate time dependence, and stealthy attacks. We consider a hybrid ensemble learning model that will enhance the detection ability of cyber-attacks in WDS by using the complementary capabilities of machine learning and deep learning models. Three base learners, namely, Random Forest , eXtreme Gradient Boosting , and Long Short-Term Memory network, have been strictly compared and seven ensemble types using simple averaged and stacked learning with a logistic regression meta-learner. Random Forest analysis identified top predictors turned into temporal and statistical features, and Synthetic Minority Oversampling Technique (SMOTE) was used to overcome the class imbalance issue. The analyics indicates that the single Long Short-Term Memory network model is of poor performance (F1 = 0.000, AUC = 0.4460), but tree-based models, especially eXtreme Gradient Boosting, perform well (F1 = 0.7470, AUC=0.9684). The hybrid stacked ensemble of Random Forest , eXtreme Gradient Boosting , and Long Short-Term Memory network scored the highest, with the attack class of 0.7205 with an F1-score and a AUC of 0.9826 indicating that the heterogeneous stacking between model precision and generalization can work. The proposed framework establishes a robust and scalable solution for cyber-attack detection in time-dependent industrial systems, integrating temporal learning and ensemble diversity to support the secure operation of critical infrastructure.

Stefan Lüdtke, Fernando Moya Rueda, Waqas Ahmed et al.

We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.

Sheikh Muhamad Hizam, Waqas Ahmed, Muhammad Fahad et al.

A smart home is grounded on the sensors that endure automation, safety, and structural integration. The security mechanism in digital setup possesses vibrant prominence and the biometric facial recognition system is novel addition to accrue the smart home features. Understanding the implementation of such technology is the outcome of user behavior modeling. However, there is the paucity of empirical research that explains the role of cognitive, functional, and social aspects of end-users acceptance behavior towards biometric facial recognition systems at homes. Therefore, a causal research survey was conducted to comprehend the behavioral intention towards the use of a biometric facial recognition system. Technology Acceptance Model (TAM)was implied with Perceived System Quality (PSQ) and Social Influence (SI)to hypothesize the conceptual framework. Data was collected from 475respondents through online questionnaires. Structural Equation Modeling(SEM) and Artificial Neural Network (ANN) were employed to analyze the surveyed data. The results showed that all the variables of the proposed framework significantly affected the behavioral intention to use the system. The PSQ appeared as the noteworthy predictor towards biometric facial recognition system usability through regression and sensitivity analyses. A multi-analytical approach towards understanding the technology user behavior will support the efficient decision-making process in Human-centric computing.

Waqas Ahmed, Amir Rasool, Neeraj Kumar et al.

Cash payment is still king in several markets, accounting for more than 90\ of the payments in almost all the developing countries. The usage of mobile phones is pretty ordinary in this present era. Mobile phones have become an inseparable friend for many users, serving much more than just communication tools. Every subsequent person is heavily relying on them due to multifaceted usage and affordability. Every person wants to manage his/her daily transactions and related issues by using his/her mobile phone. With the rise and advancements of mobile-specific security, threats are evolving as well. In this paper, we provide a survey of various security models for mobile phones. We explore multiple proposed models of the mobile payment system (MPS), their technologies and comparisons, payment methods, different security mechanisms involved in MPS, and provide analysis of the encryption technologies, authentication methods, and firewall in MPS. We also present current challenges and future directions of mobile phone security.

Waqas Ahmed, Habiba Akter, Sheikh M. Hizam et al.

Learning behavior mechanism is widely anticipated in managed settings through the formal syllabus. However, heading for learning stimulus whilst daily mobility practices through urban transit is the novel feature in learning sciences. Theory of planned behavior (TPB), technology acceptance model (TAM), and service quality of transit are conceptualized to assess the learning behavioral intention (LBI) of commuters in Greater Kuala Lumpur. An online survey was conducted to understand the LBI of 117 travelers who use the technology to engage in the informal learning process during daily commuting. The results explored that all the model variables i.e., perceived ease of use, perceived usefulness, service quality, and subjective norms are significant predictors of LBI. The perceived usefulness of learning during traveling and transit service quality has a vibrant impact on LBI. The research will support the informal learning mechanism from commuters point of view. The study is a novel contribution to transport and learning literature that will open the new prospect of research in urban mobility and its connotation with personal learning and development.

María Óskarsdóttir, Waqas Ahmed, Katrien Antonio et al.

Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we construct a network by linking claims with all their involved parties, including the policyholders, brokers, experts, and garages. Next, we establish fraud as a social phenomenon in the network and use the BiRank algorithm with a fraud specific query vector to compute a fraud score for each claim. From the network, we extract features related to the fraud scores as well as the claims' neighborhood structure. Finally, we combine these network features with the claim-specific features and build a supervised model with fraud in motor insurance as the target variable. Although we build a model for only motor insurance, the network includes claims from all available lines of business. Our results show that models with features derived from the network perform well when detecting fraud and even outperform the models using only the classical claim-specific features. Combining network and claim-specific features further improves the performance of supervised learning models to detect fraud. The resulting model flags highly suspicions claims that need to be further investigated. Our approach provides a guided and intelligent selection of claims and contributes to a more effective fraud investigation process.

Waqas Ahmed, Sheikh Muhamad Hizam, Ilham Sentosa et al.

Smart city is synchronized with digital environment and its transportation system is vitalized with RFID sensors, Internet of Things (IoT) and Artificial Intelligence. However, without user's behavioral assessment of technology, the ultimate usefulness of smart mobility cannot be achieved. This paper aims to formulate the research framework for prediction of antecedents of smart mobility by using SEM-Neural hybrid approach towards preliminary data analysis. This research undertook smart mobility services adoption in Malaysia as study perspective and applied the Technology Acceptance Model (TAM) as theoretical basis. An extended TAM model was hypothesized with five external factors (digital dexterity, IoT service quality, intrusiveness concerns, social electronic word of mouth and subjective norm). The data was collected through a pilot survey in Klang Valley, Malaysia. Then responses were analyzed for reliability, validity and accuracy of model. Finally, the causal relationship was explained by Structural Equation Modeling (SEM) and Artificial Neural Networking (ANN). The paper will share better understanding of road technology acceptance to all stakeholders to refine, revise and update their policies. The proposed framework will suggest a broader approach to individual level technology acceptance.

Jawad Ali, Ahmad Shahrafidz Khalid, Eiad Yafi et al.

Internet of Things (IoT) occupies a vital aspect of our everyday lives. IoT networks composed of smart-devices which communicate and transfer the information without the physical intervention of humans. Due to such proliferation and autonomous nature of IoT systems make these devices threatened and prone to a severe kind of threats. In this paper, we introduces a behavior capturing, and verification procedures in blockchain supported smart-IoT systems that can be able to show the trust-level confidence to outside networks. We defined a custom \emph{Behavior Monitor} and implement on a selected node that can extract the activity of each device and analyzes the behavior using deep machine learning strategy. Besides, we deploy Trusted Execution Technology (TEE) which can be used to provide a secure execution environment (enclave) for sensitive application code and data on the blockchain. Finally, in the evaluation phase we analyze various IoT devices data that is infected by Mirai attack. The evaluation results show the strength of our proposed method in terms of accuracy and time required for detection.

Sheikh Muhammad Hizam, Waqas Ahmed

Service quality possesses the vital prominence in usability of innovative products and services. As technological innovation has made the life synchronized and effective, Internet of Things (IoT) is matter of discussion everywhere. From users' perspective, IoT services are always embraced by various system characteristics of security and performance. A service quality model can better present the preference of such technology customers. the study intends to project theoretical model of service quality for internet of things (IoT). Based on the existing models of service quality and the literature in internet of things, a framework is proposed to conceptualize and measure service quality for internet of things.This study established the IoT-Servqual model with four dimensions (i.e., Privacy, Functionality, Efficiency, and Tangibility) of multiple service quality models. These dimensions are essential and inclined towards the users' leaning of IoT Services. This paper contributes to research on internet of things services by development of a comprehensive framework for customers' quality apprehension. This model will previse the expression of information secrecy of users related with internet of things (IoT). This research will advance understanding of service quality in modern day technology and assist firms to devise the fruitful service structure.

Saira Khan, Khalid Iqbal, Safi Faizullah et al.

Big Data is used by data miner for analysis purpose which may contain sensitive information. During the procedures it raises certain privacy challenges for researchers. The existing privacy preserving methods use different algorithms that results into limitation of data reconstruction while securing the sensitive data. This paper presents a clustering based privacy preservation probabilistic model of big data to secure sensitive information..model to attain minimum perturbation and maximum privacy. In our model, sensitive information is secured after identifying the sensitive data from data clusters to modify or generalize it.The resulting dataset is analysed to calculate the accuracy level of our model in terms of hidden data, lossed data as result of reconstruction. Extensive experiements are carried out in order to demonstrate the results of our proposed model. Clustering based Privacy preservation of individual data in big data with minimum perturbation and successful reconstruction highlights the significance of our model in addition to the use of standard performance evaluation measures.