Rising unemployment has led to many discouraged job seekers. While the impact of job seekers’ motivation on interview performance is acknowledged in previous research, little attention has been given to understanding the effect of training on ...

• We present InterviewApp, an online interview training tool and evaluate its role in supporting interview motivation and performance.
• Autonomy is found to mediate the effect of interview training on interview performance.
• Findings ...

Federated Learning (FL) introduces a novel methodology with the potential to achieve enhanced privacy and security assurances compared to existing methods. This is achieved by allowing multiple users to collaboratively train a global model ...

• A thorough investigation of different poisoning attacks is conducted.
• The proposed methodology dynamically ensures resilience against Byzantine failures.
• The DGF algorithm does not need a priori assumptions on the number of ...

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level ...

• Dataset of 4712 artworks annotated with 38116 labelled object instances from 139 categories.
• First dataset of smell-related objects in artworks.
• Challenging dataset in terms of occlusion, object sizes, and spatial object ...

In an increasingly globalized market, where world container traffic since 2000 has almost quadrupled, the prediction of demand is an element of great importance for the optimal business development of a company. This work focuses on demand ...

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• Both Prophet and SARIMA ably predict highly seasonal time series data 12 months ahead.
• Prophet outperforms in yearly forecasts; SARIMA excels in monthly predictions.
• Establishment of diverse KPIs for thorough analysis enhances ...

In recent years, multi-agent reinforcement learning (MARL) has been widely used as a multi-UAV autonomous countermeasure. However, because training is usually performed in a self-play manner, the existing algorithms may suffer from strategic ...

• The attention mechanism can deal with the dynamic change of the number of UAVs in air combat.
• The self-play training paradigm may lead to strategy circles.
• Training against diverse opponent strategies can effectively avoid ...

This paper proposes an adaptive uniform search framework designed for constrained multi-objective optimization. The framework comprises three key components: a global uniform exploration strategy, a local greedy exploitation strategy, and a ...

• GUE removes close individuals, promoting even distribution.
• LGE divides areas for effective local optimization.
• A novel switch adapts search between GUE and LGE.

The study recognized the significance of accurately addressing censoring and censored data in survival analysis as well as the possibility of biases if these concepts are not approached with caution by researchers. If censored participants are ...

• Censored observations may give rise to biased results.
• The study aims to update the censored observation.
• Multistate survival data using Survival Proximity Score Method.
• Threshold probability has been assumed to compare.

An inverse problem of finding an unknown heat source for a class of linear parabolic equations is considered. Such problems can typically be converted to a direct problem with non-local conditions in time instead of an initial value problem. ...

Face parsing infers a pixel-wise label map for each semantic facial component. Previous methods generally work well for uncovered faces, however, they overlook facial occlusion and ignore some contextual areas outside a single face, especially ...

• Propose a Tanh-transforms neural network for occluded face parsing.
• The four-point Tanh-transform is used to enhance facial component recognition.
• Design a four-point block structure and an occlusion-aware loss function.
• A ...

The traditional Wasserstein distance is inadequate for comparing two distributions in different metric spaces. The Gromov-Wasserstein distance (GWD), which evolves from the Gromov-Hausdorff distance, constructs intraspace pairs to ensure ...

• We propose a method to calculate the Gromov-Wasserstein distance between point sets. It is 20 times faster, uses 20 times less memory, and is rotationally invariant.
• We provide theoretical assurance for our BRISGW. Both quantitative ...

As software systems undergo escalating complexity, the identification of bugs and defects becomes pivotal for ensuring seamless user experiences and averting potentially costly post-release issues. This study addresses this critical need by ...

As a system allowing intra-network devices to automatically communicate over the Internet, the Internet of Things (IoT) faces increasing popularity in modern applications and security threats — particularly network intrusions that target both ...

• A novel multi-task learning approach for IoT traffic classification.
• A hybrid oversampling method combining traditional methods and GAN.
• Comparative analysis of Autoencoders with different structures.
• Utilisation of uncertainty-...

In this paper, we present a sustainable Smart City Solid Waste Management System (SCSWMS) that integrates trending technologies such as Internet of Things (IoT), Low Power Wide Area Networks (LPWANs), and Intelligent Traffic Systems (ITS) to ...

• Smart waste bin networked, geo-located able to detect and alert firing risks and monitor its filling level.
• IoT real-time cloud-based platform.
• User-friendly mobile application and its corresponding web version for the municipal ...

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include ...

• We define a QoS-aware placement/scheduling problem for edge AI as an ILP.
• We consider two time scales for placement/scheduling while predicting requests.
• We prove the problem is NP-hard for both placement and scheduling.
• We ...

Most dynamic sign word misclassifications are caused by redundant spatial–temporal (SPT) feature pruning that lacks language semantic and temporal dependencies. SPT feature recognition is one of the important aspects for the evaluation of the ...

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• FS-EFCNN is an automatic sign-based FS model for sign language recognition.
• The FS model combines 3D spatial–temporal features and Fourier features.
• The FS-EFCNN is verified using 3D real-life data sets without complex ...

Deep learning has driven remarkable advancements in medical image segmentation. The requirement for comprehensive annotations, however, poses a significant challenge due to the labor-intensive and expensive nature of expert annotation. Addressing ...

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• MixSegNet: Innovating medical image segmentation with mixed-supervision.
• A novel data pre-processing method: Mimicking real-world clinical annotation diversity.
• Utilizing CNN and ViT: Enhanced feature extraction in segmentation ...

Incomplete knowledge of the environment leads an agent to make decisions under uncertainty. One of the major dilemmas in Reinforcement Learning (RL) where an autonomous agent has to balance two contrasting needs in making its decisions is: ...

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• Prediction of steering wheel angle in autonomous driving system.
• Comparison of exploration strategies within deep recurrent reinforcement learning.
• Integration of VDBE method into the deep recurrent q-learning framework.
• ...

In a theoretical context of side-channel attacks, optimal bounds between success rate, guessing entropy and statistical distance are derived with a simple majorization (Schur-concavity) argument. They are further theoretically refined for ...

Function-as-service (FaaS) platforms promise a simpler programming model for cloud computing, given that providers take care of the overall resource management while the developers can concentrate only on writing their applications in the scope ...

• The Prebaking technique improves the cold start latency in FaaS deployments.
• The proposed technique is based on process checkpoint/restore.
• The technique improves the cold start latency of three popular runtime environments.
• ...

Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ...

• Deep ordinal classification.
• Hybrid ordinal dropout as regularisation methodology for deep neural networks.
• Fusion of standard and ordinal dropout techniques.
• Balance factor for the influence of the ordinality in the hybrid ...