Reviewerly/exHarmony huggingface dataset:
Exploiting enhanced and robust RGB-D face representation via progressive multi-modal learning

Existing RGB-based 2D face recognition approaches are sensitive to facial variations, posture, occlusions, and illumination. Current depth-based methods have been proved to alleviate the above sensitivity by introducing geometric information but rely heavily on high-quality depth from high-cost RGB-D cameras. To this end, we propose a Progressive Multi-modal Fusion framework to exploit enhanced and robust face representation for RGB-D facial recognition based on low-cost RGB-D cameras, which also deals with incomplete RGB-D modal data. Due to the defects such as holes caused by low-cost cameras, we first design a depth enhancement module to refine the low-quality depth and correct depth inaccuracies. Then, we extract and aggregate augmented feature maps of RGB and depth modality step-by-step. Subsequently, the masked modeling scheme and iterative inter-modal feature interaction module aim to fully exploit the implicit relations among these two modalities. We perform comprehensive experiments to verify the superior performance and robustness of the proposed solution over other FR approaches on four challenging benchmark databases.
A graphical approach for filter pruning by exploring the similarity relation between feature maps

The vast majority of repetitive pruning and retraining techniques used on CNNs require multi-stage optimization, which undermines the potential computing savings from pruning. The similarity relationship between the output feature maps of the filters is first observed and represented as a graphical model in this paper in order to solve this issue. Vertex redundancy in a graph is determined using the degree of a vertex and the weight of its edges. Pruning is then carried out "one-shot" by removing the filters associated with redundant feature maps from each layer. In order to preserve performance, the network must finally be retrained. The CIFAR-10 dataset is identified using the classifier produced by applying this proposed methodology to several deep learning models. In this instance, both the parameters reduction and FLOPs reduction of the trimmed model are significantly improved. This method, tested on VGG-16, reduces parameters by 90.8% and FLOPs by 60.0% while sacrificing little accuracy (=0.16%) in comparison to the baseline. The results based on ResNet-110 show that applying the proposed approach can reduce FLOPs and parameters by 70.4% while preserving baseline accuracy with a loss of just 0.05%.
GradFuzz: Fuzzing deep neural networks with gradient vector coverage for adversarial examples

Deep neural networks (DNNs) are susceptible to adversarial attacks that add perturbations to the input data, leading to misclassification errors and causing machine-learning systems to fail. For defense, adversarial training leverages possible crashing inputs, i.e., adversarial examples; but, the input space of DNNs is enormous and high-dimensional, making it difficult to find in a wide range. Coverage-guided fuzzing is promising in this respect. However, this leaves the question of what coverage metrics are appropriate for DNNs. We observed that the abilities of existing coverage metrics are limited. They lack gradual guidance toward crashes because of a simple search for a wide neuron activation area. None of the existing approaches can simultaneously achieve high crash quantity, high crash diversity, and efficient fuzzing time. Apart from this, the evaluation methodologies adopted by state-of-the-art fuzzers need rigorous improvements. To address these problems, we present a new DNN fuzzer named GradFuzz. Our idea is the gradient vector coverage, which provides gradual guidance to misclassified categories. We implemented our system and performed experiments under rigorous evaluation methodologies. Our evaluation results indicate that GradFuzz outperforms state-of-the-art DNN fuzzers: GradFuzz can locate a more diverse set of errors, beneficial to adversarial training, on the MNIST and CIFAR-10 datasets without sacrificing both crash quantity and fuzzing efficiency.
Position-aware graph neural network for session-based recommendation

Session-based recommendations (SBRs) make recommendations using the current interaction sequence of users. Recent studies on SBRs have primarily used graph neural networks (GNNs) to model the session sequence; however, such methods treat the same items in a session as a single node, thus ignoring differences between items in different positions. Moreover, they do not use other sessions to learn users' short-term preferences. Therefore, we propose a novel position-aware graph neural network (PA-GNN) for SBRs. First, this model uses a session in the form of a position-aware graph as an input to completely use the position information of the item and apply the attention mechanism to learn users' long-term interests. Second, it combines other sessions to learn the user's short-term preferences. Third, it integrates long-term interests and short-term preferences for predictions. The experimental results using three benchmark e-commerce datasets demonstrate that the PA-GNN model performs excellently and is superior to the latest baselines on SBRs.
A vertex-arc path planning method for metal droplet-based 3D printing of thin-walled parts with sharp corners

Metal droplet-based 3D printing has inherent advantages in the printing of multi-material structures such as lattices with tunable thermal expansion coefficients. However, since these structures are often composed of thin-walled polygons with sharp corners, existing path planning methods are prone to lead to staircase effects or voids, reducing the print quality of the structures. Herein, a vertex-arc path planning method to print high-quality thin-walled structures with sharp corners was proposed. First, conventional path planning of the raster patterns and contour patterns were analyzed. The equidistant adjacent droplets and layers and the precise corner angle cannot be satisfied simultaneously, resulting in staircase effects or voids in corners. To address this problem, a vertex-arc method was proposed to construct a V-shaped basic building block. The block could be translated and rotated to print each vertex of polygons, and then connected with the blocks on the adjacent vertices to form a thin-walled polygon with any number of sides and any angles. The effect of overlapping errors in the corner of the block was minimized by an error marginalization optimization strategy. The end-to-end connections of the blocks were achieved by an arc approaching straight-line method. Thin-walled structures printing experiments were further conducted to verify the vertex-arc method. Compared with raster and contour patterns, the vertex-arc method could print thin-walled structures with higher dimensional accuracy and without staircase effects and internal voids. This work provides an efficient path planning method for printing high-quality complex structures with sharp corners.
Fuzzy constraint prioritization to solve heavily constrained problems with the genetic algorithm

Genetic algorithms (GAs) are approximate solving methods that have been originally proposed to achieve unconstrained optimization. To handle constrained problems, which is the case for the majority of real-life circumstances, GAs must be equipped with a constraint-handling mechanism. Transformation functions (TFs) are among the constraint-handling approaches that intervene in the phenotypic space. In this paper, we study the impact of considering constraint priorities on the GA performance when it deals with heavily constrained problems. Priorities are set by integrating a constraint order into the TF definition. We consider different TF forms enhanced with a fuzzy inference engine to find the best constraint ordering. Finally, we conduct an experimental study to assess the performance of the proposed approach on the semi-supervised graph partitioning problem. The obtained results show with statistical evidence that the proposed fuzzy method is promising.
Investigations of c-differential uniformity of permutations with Carlitz rank 3

The c-differential uniformity is recently proposed to reflect resistance against some variants of differential attack. Finding functions with low c-differential uniformity is attracting attention from many researchers. For even characteristic, it is known that permutations of low Carlitz rank have good cryptographic parameters, for example, low differential uniformity, high nonlinearity, etc. In this paper we show that permutations with low Carlitz rank have low c-differential uniformity. We also investigate c-differential uniformity of permutations with Carlitz rank 3 in detail.
Leveraging blockchain in response to a pandemic through disaster risk management: an IF-MCDM framework

The COVID-19 pandemic has underscored the necessity for strategic approaches to manage global catastrophes. This study argues that disaster risk management (DRM) is an essential approach to mitigate the impact of global calamities, such as a pandemic. However, due to the uncertainty emerging from variables such as time and demand, managing DRM effectively involves high complexity. Blockchain technology (BcT) can be implemented to help address these challenges due to its potential to build trust, transparency, and accountability in complex operations. However, no research has quantitatively examined the applicability of BcT in the sub-dimensions of DRM to address the COVID-19 pandemic. The goal and scope of this study is to explore the role of BcT in DRM operations during the COVID-19 pandemic through an Intuitionistic Fuzzy Multi-Criteria Decision Making (IF-MCDM) framework. More specifically, the Intuitionistic Fuzzy Analytic Network Process (IF-ANP) method was utilized to calculate the weights of key criteria (i.e., BcT benefits), while Intuitionistic Fuzzy VIKOR (IF-VIKOR) was used to prioritize the alternatives (i.e., the sub-dimensions of DRM). The findings of this study are threefold. First, supporting effective coordination turned out to be the most essential benefit of BcT to build resilience in response to the COVID-19 pandemic. Second, disaster management was found to be the most appropriate DRM sub-dimension for possible BcT implementation during the COVID-19 pandemic. Lastly, eleven distinct activities involved in disaster management and governance and financial protection were discovered to be the most applicable for BcT. The findings of this study could assist disaster risk managers to assess whether (or not) BcT is suitable for the sub-dimensions of DRM to build national and organizational resilience in the wake of the COVID-19 pandemic.
From Digital Economy to Global Economy—A Cybernetics Approach

The Digital Economy is a new form of economy that has grown rapidly in recent years, becoming a real oxymoron for more and more specialists. In this research, we aimed to make a systematic review of the existing literature in the field, to present the differences between digitization, digitalization and digital transformation and to define the Global Economy as a Cybernetic System in the Digital Age. Moreover, we will investigate what the role of the COVID-19 pandemic was in accelerating the digitalization of the Digital Economy and what the future post-COVID-19 actions of digital transformation could be.
Law Enforcement and the Policing of Cyberspace

Today, people all around the globe rely on an internet connection to function in twenty-first century society. The evolution of technology along with society itself have led to having an online presence being an important aspect of life. Users of the internet can connect and interact with one another globally, with it being easily accessible and available. Cyberspace is a new crucial point for modernisms, social media and businesses, bringing efficiency and enjoyment to many. Juxtaposed to this is the opportunity this connectivity affords online criminality to take place. Cyberspace poses significant challenges for law enforcement, where generic policing strategies can not necessarily be applied. The 'non-physical' nature of cyberspace leads to issues when everyday police forces try to govern the digital world and units must steer away from their usual day-to-day operations, in this fast-developing arena. This chapter will analyse the different challenges that police, and law enforcement agencies face when attempting to police the cyber world and protect the public from the ever growing and evolving risk of cybercrime.
An AI-Based Platform Architecture with Situational Awareness for Travel Plans

Demand for non-face-to-face services is increasing in all industries due to the impact of the global pandemic caused by COVID-19 over the past three years. This phenomenon also occurs in the travel industry without exception. Travel consumption patterns are changing in the form of FIT (Foreign Independent Tour) rather than package tours provided by existing travel agencies. Now, we are entering the era of travel tech using smartphones. To this end, technological efforts are being made to simultaneously address the two characteristics of individual free travel and non-face-to-face situations. In this paper, we propose an AI-based platform architecture with situation awareness for travel plans. It provides a personalized service using the ontology-based mobile service so that tourists can make travel plans according to their own circumstances, after considering the situation of individual tourists through sensor collection data while analyzing the congestion situation of tourist destinations through public data. The mobile platform consists of a total of four layers (Infrastructure, Cross-Cutting Infrastructure, Domain, Application), and has been implemented in an environment of Android 8.0 or higher and iOS 11.0 or higher through the AWS website.
A novel hybrid deep learning model for aspect based sentiment analysis

Summary The usage of social media, forums, and e‐commerce websites have been widely increased. Feedback from customers has a big impact on the final product. A service provider, merchant, or manufacturer need all the information, even if it is just a comment or a review about a service or a product. So, it is vital to look at input from users, and therefore sentiment analysis has received a lot of interest. Sentiment analysis is a method for identifying and analyzing text in order to determine the features, qualities, and viewpoints of particular user. Extracting user aspects is the main part of this process, and it is used to group the user aspects. In recent years, convolutional neural network (CNN) models have gained popularity in natural language processing. Thus, this research proposes a novel hybrid CNN model by concatenating the bidirectional long short‐term memory and CNN models to process the data sequentially by learning their high‐level features. The concatenated method minimizes the loss of critical information. Benchmark product reviews and hotel review datasets are employed in the experiments, and accuracies of 93.6% for the product review dataset and 92.7% for the hotel review dataset are achieved by the proposed hybrid model when compared to state‐of‐the‐art techniques.
Automated Hyperparameter Optimization of Gradient Boosting Decision Tree Approach for Gold Mineral Prospectivity Mapping in the Xiong’ershan Area

The weak classifier ensemble algorithms based on the decision tree model, mainly include bagging (e.g., fandom forest-RF) and boosting (e.g., gradient boosting decision tree, eXtreme gradient boosting), the former reduces the variance for the overall generalization error reduction while the latter focuses on reducing the overall bias to that end. Because of its straightforward idea, it is prevalent in MPM (mineral prospectivity mapping). However, an inevitable problem in the application of such methods is the hyperparameters tuning which is a laborious and time-consuming task. The selection of hyperparameters suitable for a specific task is worth investigating. In this paper, a tree Parzen estimator-based GBDT (gradient boosting decision tree) model (TPE-GBDT) was introduced for hyperparameters tuning (e.g., loss criterion, n_estimators, learning_rate, max_features, subsample, max_depth, min_impurity_decrease). Then, the geological data of the gold deposit in the Xiong ‘ershan area was used to create training data for MPM and to compare the TPE-GBDT and random search-GBDT training results. Results showed that the TPE-GBDT model can obtain higher accuracy than random search-GBDT in a shorter time for the same parameter space, which proves that this algorithm is superior to random search in principle and more suitable for complex hyperparametric tuning. Subsequently, the validation measures, five-fold cross-validation, confusion matrix and success rate curves were employed to evaluate the overall performance of the hyperparameter optimization models. The results showed good scores for the predictive models. Finally, according to the maximum Youden index as the threshold to divide metallogenic potential areas and non-prospective areas, the high metallogenic prospect area (accounts for 10.22% of the total study area) derived by the TPE-GBDT model contained &gt; 90% of the known deposits and provided a preferred range for future exploration work.
Probabilistic Forecasts of Global Horizontal Irradiance for Solar Systems

Solar power is one of the most attractive green energy sources and plays a vital role in daily electricity supply. Since the amount of available solar power is uncontrollable, it is essential to forecasting its availability so that power plants can arrange power supply in advance. Global horizontal irradiance (GHI) is the key indicator of available solar power, highly accurate forecasts for which are required to successfully integrate solar energy into the power grid. In this letter, we propose probabilistic solar irradiance transformer (ProSIT); a novel deep learning model for multihorizon probabilistic GHI forecasting based on both historical GHI and natural irradiance impact factors. ProSIT is able to capture the correlations of the heterogeneous impact factors of GHI as well as the long- and short-term temporal dependencies across the historical sequence. ProSIT also features residual connections and gating mechanisms to suppress superfluous components <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> . Rather than simply providing single forecasting values as in existing methods, ProSIT employs a probabilistic output module to afford auxiliary forecasting confidence and bounds, which are particularly desirable in practical applications. We conduct experiments on two real-world datasets and demonstrate that ProSIT can achieve considerably better performance than state of the art.
Training on multimodal mobile-assisted language learning: a suggested model for pre-service EFL teachers*

This article describes the design and implementation of a training model on Multimodal Mobile-Assisted Language Learning (M-MALL) for EFL pre-service (PSTs) teachers. Albeit EFL teachers' positive attitude towards technology integration into language teaching, they may still lack guidance and support on how to implement mobile technologies in their teaching contexts. Based on these, a training model to integrate M-MALL into PST education was suggested. An M-MALL training program was developed for the purposes of the study based on the needs, related literature, and the gap stemming from the lack of training opportunities in PST education. 16 PSTs studying in an ELT Department in Turkey participated. Following the implementation of a systematic eight-week training, participants were asked to write reflections on the training they received. The PSTs, in general, reported positive views on receiving training. Receiving such thorough training resulted in many gains for the participants such as improvement in teaching skills, expanding perspective on effective mobile technology integration, and being up to date as a teacher. The results have implications for language teachers and curriculum designers, and the suggested model provides a guideline for PST educators and language practitioners who would like to improve teaching competencies in a digital world.
Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method.
An Improved Whale Optimizer with Multiple Strategies for Intelligent Prediction of Talent Stability

Talent resources are a primary resource and an important driving force for economic and social development. At present, researchers have conducted studies on talent introduction, but there is a paucity of research work on the stability of talent introduction. This paper presents the first study on talent stability in higher education, aiming to design an intelligent prediction model for talent stability in higher education using a kernel extreme learning machine (KELM) and proposing a differential evolution crisscross whale optimization algorithm (DECCWOA) for optimizing the model parameters. By introducing the crossover operator, the exchange of information regarding individuals is facilitated and the problem of dimensional lag is improved. Differential evolution operation is performed in a certain period of time to perturb the population by using the differences in individuals to ensure the diversity of the population. Furthermore, 35 benchmark functions of 23 baseline functions and CEC2014 were selected for comparison experiments in order to demonstrate the optimization performance of the DECCWOA. It is shown that the DECCWOA can achieve high accuracy and fast convergence in solving both unimodal and multimodal functions. In addition, the DECCWOA is combined with KELM and feature selection (DECCWOA-KELM-FS) to achieve efficient talent stability intelligence prediction for universities or colleges in Wenzhou. The results show that the performance of the proposed model outperforms other comparative algorithms. This study proposes a DECCWOA optimizer and constructs an intelligent prediction of talent stability system. The designed system can be used as a reliable method of predicting talent mobility in higher education.