User profiling based on browsing behavior has traditionally been applied to improve web personalization and marketing strategies. However, leveraging browsing patterns to assess cybersecurity risks remains underexplored. In this paper, we propose a profiling framework based on domain name system (DNS) traffic analysis. Our approach models user browsing behavior using two main factors: browsing intent and domain reputation. By aggregating risk weights derived from accessed domains, we compute a personalized browsing risk score that reflects the user’s exposure to online threats. We validate the effectiveness of our framework through experiments that demonstrate its ability to differentiate users with varying levels of browsing risk. Our findings offer new insights into user-centric cybersecurity assessment using minimal yet meaningful data sources.

Abstract Stress significantly impacts individuals, particularly in professions like nursing and driving, leading to severe health risks and accidents. Accurate stress measurement is critical for effective interventions, yet research is hindered by incomplete datasets and inconsistent methodologies, slowing the development of reliable predictive models. This paper introduces a framework for cross-contextual stress prediction, enabling the generation of general stress prediction models adaptable to specific domain challenges. The methodology leverages two general daily life datasets and three domain-specific datasets, employing steps such as dataset selection, feature extraction, significant feature identification, feature preprocessing, fine-tuning, domain adaptation, and application to specific contexts. Through this framework, key vital signs were identified as significant predictors of stress, including electrocardiography (ECG), heart rate (HR), heart rate variability (HRV) - low frequency (LF), electrodermal activity (EDA), body temperature (TEMP), and skin conductance response (SCR). The experiments conducted include: 1) Utilizing HR and HRV-LF through domain adaptation from general to automobile driving datasets; 2) Applying EDA, HR, and TEMP from general to specific nurse activity datasets; and 3) Adapting ECG, HR, and TEMP from general to automobile driving datasets. Results demonstrate the potential of the proposed framework for cross-contextual stress prediction, with HR and HRV-LF identified as pivotal features. When applied to target datasets specific to stress scenarios, the model achieved a 62% F1 score, demonstrating the effectiveness of the feature-based Correlation Alignment (CORAL) technique combined with Random Forest models in transferring learned knowledge across domains. These findings highlight the robustness of the approach in adapting general stress prediction models to specific contexts, paving the way for real-world applications such as stress monitoring in driving and nursing during high-stress periods like COVID-19.

The rapid adoption of Industry 4.0 has seen Information Technology (IT) networks increasingly merged with Operational Technology (OT) networks, which have traditionally been isolated on air-gapped and fully trusted networks. This increased attack surface has resulted in compromises of Cyber-Physical Systems (CPS) with significant economic and life safety consequences. This paper proposes a hybrid model of anomaly detection of security threats to CPS by blending the signature-based and threshold-based Intrusion Detection Systems (IDS) commonly used in IT networks, with a Machine Learning (ML) model designed to detect behaviour-based anomalies in OT networks. This hybrid model achieves more rapid detection of known threats through signature-based and threshold-based detection strategies, and more accurate detection of unknown threats via behaviour-based anomaly detection using ML algorithms.

In this research, a coevolutionary collision free multi-robot path planning that makes use of A* is proposed. To find collision-free paths for all robots, we generate a route for each of robot using A* path finding but introducing restrictions for each collision found. Afterward, a co-evolutionary optimization process is implemented for introducing changes in the initial paths to find a combination of routes that is collision-free. The approach has been tested in mazes with increasing the number of robots, showing a robust performance although at high time expenses. Nevertheless, several enhancements are proposed to tackle this issue.

Photo-response non-uniformity (PRNU), as a class of device fingerprint, plays a key role in the forgery detection/localization for visual media. The state-of-the-art PRNU-based forensics methods generally rely on the multi-scale trace analysis and result fusion, with Markov random field model. However, such hand-crafted strategies are difficult to provide satisfactory multi-scale decision, exhibiting a high false-positive rate. Motivated by this, we propose an end-to-end multi-scale decision fusion strategy, where a mapping from multi-scale forgery probabilities to binary decision is achieved by a supervised deep fully connected neural network. As the first time, the deep learning technology is employed in PRNU-based forensics for more flexible and reliable integration of multi-scale information. The benchmark experiments exhibit the state-of-the-art accuracy performance of our method in both pixel-level and image-level, especially for false positives. Additional robustness experiments also demonstrate the benefits of the proposed method in resisting noise and compression attacks.

The device fingerprint, photo-response non-uniformity (PRNU), has attracted great interest in image tampering detection and localization. The classical PRNU-based tampering detection generally depends on the correlation analysis, with the normalized correlation and hand-crafted predictor. The predictor detects unreliable regions and determines them as forgery, regardless of other information. However, the operation is arbitrary and the auxiliary information provided by such a predictor is hard to achieve satisfactory results. Motivated by this, we propose a lightweight forgery detection strategy, where a localization result is directly predicted by a supervised convolutional neural network (CNN). For the first time, CNN is introduced to compute the correlation coefficient in PRNU-based forgery detection. We perform an extensive evaluation in both pixel-level and image-level experiments, and the results show that the proposed method achieves significant performance gains.

In online display advertising, guaranteed contracts and real-time bidding (RTB) are two major ways to sell impressions for a publisher. For large publishers, simultaneously selling impressions through both guaranteed contracts and in-house RTB has become a popular choice. Generally speaking, a publisher needs to derive an impression allocation strategy between guaranteed contracts and RTB to maximize its overall outcome (e.g., revenue and/or impression quality). However, deriving the optimal strategy is not a trivial task, e.g., the strategy should encourage incentive compatibility in RTB and tackle common challenges in real-world applications such as unstable traffic patterns (e.g., impression volume and bid landscape changing). In this paper, we formulate impression allocation as an auction problem where each guaranteed contract submits virtual bids for individual impressions. With this formulation, we derive the optimal bidding functions for the guaranteed contracts, which result in the optimal impression allocation. In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable. The experiments conducted on real-world datasets demonstrate the effectiveness of our method.

In online display advertising, advertisers usually participate in real-time bidding to acquire ad impression opportunities. In most advertising platforms, a typical impression acquiring demand of advertisers is to maximize the sum value of winning impressions under budget and some key performance indicators constraints, (e.g. maximizing clicks with the constraints of budget and cost per click upper bound). The demand can be various in value type (e.g. ad exposure/click), constraint type (e.g. cost per unit value) and constraint number. Existing works usually focus on a specific demand or hardly achieve the optimum. In this paper, we formulate the demand as a constrained bidding problem, and deduce a unified optimal bidding function on behalf of an advertiser. The optimal bidding function facilitates an advertiser calculating bids for all impressions with only m parameters, where m is the constraint number. However, in real application, it is non-trivial to determine the parameters due to the non-stationary auction environment. We further propose a reinforcement learning (RL) method to dynamically adjust parameters to achieve the optimum, whose converging efficiency is significantly boosted by the recursive optimization property in our formulation. We name the formulation and the RL method, together, as Unified Solution to Constrained Bidding (USCB). USCB is verified to be effective on industrial datasets and is deployed in Alibaba display advertising platform.

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices.

Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.

Real-time bidding (RTB) is an important mechanism in online display advertising, where a proper bid for each page view plays an essential role for good marketing results. Budget constrained bidding is a typical scenario in RTB where the advertisers hope to maximize the total value of the winning impressions under a pre-set budget constraint. However, the optimal bidding strategy is hard to be derived due to the complexity and volatility of the auction environment. To address these challenges, in this paper, we formulate budget constrained bidding as a Markov Decision Process and propose a model-free reinforcement learning framework to resolve the optimization problem. Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint. Therefore, we innovate a reward function design methodology for the reinforcement learning problems with constraints. Based on the new reward design, we employ a deep neural network to learn the appropriate reward so that the optimal policy can be learned effectively. Different from the prior model-based work, which suffers from the scalability problem, our framework is easy to be deployed in large-scale industrial applications. The experimental evaluations demonstrate the effectiveness of our framework on large-scale real datasets.

With the improved elasticity of the computing landscape, resources can be added or removed on demand. These new capabilities require a closer look at the link between business processes and IT infrastructure. Therefore, it seems prudent to focus on a conceptual blueprint that will encourage research in developing pragmatic offerings in monitoring, measuring, and analyzing this relationship with the purpose of measuring generated business value. This paper proposes a monitoring framework that can be integrated into enterprise architecture and would address these issues.

Curve extraction is an important and basic technique in image processing and computer vision. Due to the complexity of the images and the limitation of segmentation algorithms, there are always a large number of noisy pixels in the segmented binary images. In this paper, we present an approach based on ant colony system (ACS) to detect nonparametric curves from a binary image containing discontinuous curves and noisy points. Compared with the well-known Hough transform (HT) method, the ACS-based curve extraction approach can deal with both regular and irregular curves without knowing their shapes in advance. The proposed approach has many characteristics such as faster convergence, implicit parallelism and strong ability to deal with highly-noised images. Moreover, our approach can extract multiple curves from an image, which is impossible for the previous genetic algorithm based approach. Experimental results show that the proposed ACS-based approach is effective and efficient.

This research aims to bring up a strategy called Multi-Object Oriented Augmented Reality, based on the Augmented Reality technique and the location of Mobile Learning Objects, which allows learner to see the suitable learning contents superimposed upon the specific learning objects and enhance the interactive in a mobile learning environment. The three characteristics of the proposed approach, Learning-Object Oriented, Guidance Ability and Highly Interactive will enhance Mobile Learning in a more adaptive and interesting way.

This paper presents a collaborative mobile learning system: Mobile Virtual Campus (MVC) powered by the loction-based dynamic grouping algorithm. The MVC system has been developed to provide an innovative and interactive platform for online mobile learners by utilizing the location awareness and other built-in sensory compoments in mobile devices. On the platform, the mobile learners can learn collaboratively and interactively either at a distance or face-to-face in the mobile learning environment.

This paper presents an infrastructure for developing problem-based pervasive learning environments. Building such environments necessitates having many autonomous components dealing with various tasks and heterogeneous distributed resources. Our proposed infrastructure is based on a multi-agent system architecture to integrate various components of the environments. The infrastructure includes a location- and context-awareness service, a question and answer service, an adaptive mechanism; problem based ubiquitous learning models, social networking issues, and the evaluation of multimedia inputs. Furthermore, student modeling issues among components are considered. The design of the infrastructure as well as its components is discussed. This paves the way towards the development of pervasive learning applications.

The insufficiency of local search and slow convergence in later generations are two main disadvantages of niche genetic algorithm (NGA). In this paper, we propose an improved novel niche genetic algorithm with local search ability. Depending on the number of iteration, the new algorithm adopts the mechanism of crossover operator and mutation operator in niche population instead of between different niches to make the searching more effective. This new method is used in Shubert function optimization and experimental results show its superiority compared with GA and NGA.

A fuzzy pattern matching algorithm is presented in this paper. This algorithm is employed to recognize landmarks placed in a robot working environment. A robot localization system with a map-based-navigation is designed by applying the fuzzy pattern matching algorithm. The robot localization will have a reliable robot vision system since robustness of this algorithm. Using this algorithm for pattern recognition, an input pattern, a landmark in the robot working environment, will always find a match with its prototype pattern, which is represented a location in the robot map.