Reviewerly/exHarmony huggingface dataset:
Deepfake Detection for Fake Images with Facemasks

Hyper-realistic face image generation and manipulation have given rise to numerous unethical social issues, e.g., invasion of privacy, threat of security, and malicious political maneuvering, which resulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of the COVID-19. In this paper, we thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes with the facemask. Our result shows that fake facial images with facemask can deceive well-known deepfake detection models, thereby evading the real-world security systems.
On Measuring Vulnerable JavaScript Functions in the Wild

JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications, and is currently used in almost all websites. Because of its popularity, JavaScript has become a frequent target for attackers, who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing work mostly deals with package-level vulnerability tracking and measurements. However this approach is limited to detecting usage of already known vulnerabilities. In this paper we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world projects. We build our framework with the help of a comprehensive dataset of 1,360 verified vulnerable JavaScript functions that we compose based on Snyk vulnerability database and the VulnCode-DB project. Using our framework, we identify 11,148 vulnerable functions in three environments: NPM packages, Chrome web extensions and popular websites. In addition,we conduct an in-depth contextual analysis of the findings in several popular/critical projects and confirm the security exposure of 15 cases. As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to function level, and thus improve accuracy of detection and aid timely patching.
Improved perception of ceramic molds through augmented reality

Abstract Augmented Reality techniques allow the user to visualize part of the real world through a display device by incorporating graphical information into the existing physical information. In this sense, it is important to know how the physical presence of the user in the augmented reality experience can affect the perception and evaluation of the product. To this end, this work presents a theoretical framework that explains how users perceive and evaluate the benefits and quality of augmentation with augmented reality through their physical presence, compared to visualizing the same experience through a video. The application was developed for the exhibition and sale of ceramic molds. Users viewed graphical information about the mold, placed between them and the screen while seeing themselves in the television as if it was a mirror. The experiments showed that the integration of the product into the environment and the spatial presence of the users had a positive effect on the perceived value in terms of usefulness and enjoyment, improved comfort in the purchase decision, and reinforced the overall opinion of the product.
A Meta-Analytic Review on Embodied Pedagogical Agent Design and Testing Formats

Over the past decade, meta-analyses have been conducted to evaluate the impact of embodied pedagogical agents on learning outcomes. Most review studies have evaluated learning outcomes from the perspective of testing labels such as transfer, retention, recognition, free recall, and various other classifications. This is problematic, because studies can be excluded due to the labeling of the assessment, even though the testing format and test taking strategy are the same. This meta-analysis assesses how embodied pedagogical agents impact learning when measured from the testing format and test taking strategy perspective. The new approach, along with new moderating variables to evaluate agent design and content information, supports previous findings that embodied pedagogical agents significantly increase learning; but there are limitations depending on the agent’s design and the type of information being learned. This meta-analysis advances the field by exploring new ways to think about design elements associated with embodied pedagogical agents.
The suppression of spontaneous face touch and resulting consequences on memory performance of high and low self-touching individuals

Spontaneous touching of one's own face (sFST) is an everyday behavior that occurs primarily in cognitively and emotionally demanding situations, regardless of a persons' age or gender. Recently, sFST have sparked scientific interest since they are associated with self-inoculation and transmission of respiratory diseases. Several studies addressed the need to reduce sFST behaviors without discussing the underlying functions of this spontaneous behavior. In addition, the question of why this behavior occurs very frequently in some individuals (high self-touching individuals, HT) but less frequently in others (low self-touching individuals, LT) has not yet been addressed. For the first time, we distinguished between HT and LT and investigated the behavioral consequences of sFST suppression in these two groups. For this purpose, we examined performance outcomes of 49 participants depending on sFST behaviors during a haptic working memory task. In addition, we assessed personality traits of HT and LT using the Freiburg Personality Inventory (FPI-R). The results of our study reveal that suppressing sFST in HT is negatively related to memory performance outcomes. Moreover, HT show tendencies to differ from LT in certain personality traits. Our results highlight the relevance of distinguishing between HT and LT in future studies of sFST.
A Blockchain Information Management Framework for Construction Safety

A successful construction safety management process involves systematic collection, management, and distribution of a significant amount of data through various means including stakeholders’ safety records, inspection procedures, risk assessment analyses, daily reports, preventive control tasks, incidents reports, and post-incident investigations. Despite the tremendous efforts and research in construction safety, still little is known about a systematic method for safety data management that provides accessibility, transparency, reliability, consistency, and traceability. This study aims to address this gap in knowledge by developing a novel information management framework for construction safety processes using blockchain. The proposed framework utilizes distributed ledger technology (DLT) to offer a new architecture of trust for reliably sharing information among all entities involved in a construction project who may not trust one another. The proposed method is designed based on a private blockchain system that maintains a decentralized control of information through permissioned channels to provide a shared view of reliable safety data and records that are accessible, traceable, and immutable. The outcomes of this study contribute to the existing body of knowledge and current state of practice in integrated safety management (ISM) systems that aim to protect construction workers and public through providing timely, reliable, and complete information to all entities involved in a project.
Effects of physical activity intervention on 24-h movement behaviors: a compositional data analysis

We utilized compositional data analysis (CoDA) to study changes in the composition of the 24-h movement behaviors during an activity tracker based physical activity intervention. A total of 231 recently retired Finnish retirees were randomized into intervention and control groups. The intervention participants were requested to use a commercial activity tracker bracelet with daily activity goal and inactivity alerts for 12 months. The controls received no intervention. The 24-h movement behaviors, i.e., sleep, sedentary time (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) were estimated from wrist-worn ActiGraph data using the GGIR R-package. Three balance coordinates describing the composition of movement behaviors were applied: ratio of active vs. passive behaviors, LPA vs. MVPA, and sleep vs. SED. A linear mixed model was used to study changes between the baseline and 6-month time point. Overall, the changes in the 24-h movement behaviors were small and did not differ between the groups. Only the ratio of LPA to MVPA tended to change differently between the groups (group*time interaction p = 0.08) as the intervention group increased LPA similarly to controls but decreased their MVPA. In conclusion, the use of a commercial activity tracker may not be enough to induce changes in the 24-h movement behaviors among retirees.
An Image Compression Encryption Algorithm Based on Chaos and ZUC Stream Cipher

In order to improve the transmission efficiency and security of image encryption, we combined a ZUC stream cipher and chaotic compressed sensing to perform image encryption. The parallel compressed sensing method is adopted to ensure the encryption and decryption efficiency. The ZUC stream cipher is used to sample the one-dimensional chaotic map to reduce the correlation between elements and improve the randomness of the chaotic sequence. The compressed sensing measurement matrix is constructed by using the sampled chaotic sequence to improve the image restoration effect. In order to reduce the block effect after the parallel compressed sensing operation, we also propose a method of a random block of images. Simulation analysis shows that the algorithm demonstrated better encryption and compression performance.
Adaptive Polar-Grid Gaussian-Mixture Model for Foreground Segmentation Using Roadside LiDAR

Roadside LiDAR has become an important sensor for the detection of objects in cities, such as vehicles and pedestrians, which is due to its advantages of all-weather operation and high-ranging accuracy. In order to serve an intelligent transportation system, the efficient and accurate segmentation of vehicles and pedestrians is needed in the coverage area of the LiDAR. In this study, a roadside LiDAR was fixed on brackets on both sides of the road to obtain the point-cloud information on the urban road and the surrounding environment. A segmentation method that is based on a scanning LiDAR sensor is proposed. First, a polar grid that is based on polar coordinates is constructed to count the LiDAR rotations to obtain the original information of the angle and the distance of the point cloud, and the background point-cloud image is dynamically updated over time. By aiming at the complex urban road environment and the interference of trees and light poles in the background, an adaptive polar-grid Gaussian-mixture model (APG-GMM) that uses a point-cloud method is proposed to improve the accuracy of the foreground and background segmentation. A density-adaptive DBSCAN target-clustering algorithm is proposed, as well as a dynamic adaptive neighborhood radius, to solve the problem of the low clustering accuracy that is caused by the uneven density of point clouds that are collected by LiDAR, and to divide the point clouds in the foreground into vehicles and pedestrians. Finally, the method was tested at intersections and urban roads with dense traffic flows. The experimental results show that the proposed algorithm can segment the foreground and background well and can cluster vehicles and pedestrians while reducing the number of calculations and the time complexity.
Energy Management Prediction in Hybrid PV-Battery Systems Using Deep Learning Architecture

On-grid predictive energy management using machine learning is presented in this paper. A photovoltaic array considered in this study is one of the kinds of a renewable sources of energy, where the battery bank acts as a technology for energy storage, in order to optimise energy exchange with the utility grid using logistic regression. The model of prediction can accurately estimate photovoltaic energy output and load one step ahead using a training technique. The optimization problem is constrained by the maximum amount of CO2 produced and the maximum amount of charge stored in a battery bank. The proposed model is tested on dynamic electricity costs. Compared with existing energy systems, the proposed strategy and prediction model can handle more than half of the annual load need.
Assessing Africa’s Agricultural TFP for Food Security and Effects on Human Development: Evidence from 35 Countries

Population growth, food shortages, and low levels of human development have been longstanding issues confronting many African countries. Agricultural productivity remains a critical goal for mitigating these challenges and ensuring overall economic development. Total factor productivity (TFP) is a crucial metric for determining a sector’s overall growth. However, due to a lack of comprehensive assessments of the trends and determinants of TFP growth in African agriculture, there are disagreements. Within the context of inclusive human development, the impact of agricultural productivity is frequently misrepresented in the current literature. This paper estimated TFP growth and assessed its impact on human development in Africa. Due to technological improvement, TFP increased moderately at a 5.4% growth rate across African countries over the period (2001–2019). Empirical evidence indicates that TFP growth enhances human development in the long run, but the effect varies according to levels of human development (HDI) and the nature of growth over time. For instance, higher levels of human development tend to mitigate the impact of TFP. Further analysis revealed that technical efficiency improvement is critical for enhancing food safety and human development. Policy recommendations for improving TFP for food security and human development in Africa are provided. Further investigation into agricultural TFP’s impact beyond the poverty measure in Africa is encouraged.
Multi-View Cosine Similarity Learning with Application to Face Verification

An instance can be easily depicted from different views in pattern recognition, and it is desirable to exploit the information of these views to complement each other. However, most of the metric learning or similarity learning methods are developed for single-view feature representation over the past two decades, which is not suitable for dealing with multi-view data directly. In this paper, we propose a multi-view cosine similarity learning (MVCSL) approach to efficiently utilize multi-view data and apply it for face verification. The proposed MVCSL method is able to leverage both the common information of multi-view data and the private information of each view, which jointly learns a cosine similarity for each view in the transformed subspace and integrates the cosine similarities of all the views in a unified framework. Specifically, MVCSL employs the constraints that the joint cosine similarity of positive pairs is greater than that of negative pairs. Experiments on fine-grained face verification and kinship verification tasks demonstrate the superiority of our MVCSL approach.
$${{\mathrm {Latent}}Out}$$: an unsupervised deep anomaly detection approach exploiting latent space distribution

Abstract Anomaly detection methods exploiting autoencoders (AE) have shown good performances. Unfortunately, deep non-linear architectures are able to perform high dimensionality reduction while keeping reconstruction error low, thus worsening outlier detecting performances of AEs. To alleviate the above problem, recently some authors have proposed to exploit Variational autoencoders (VAE) and bidirectional Generative Adversarial Networks (GAN), which arise as a variant of standard AEs designed for generative purposes, both enforcing the organization of the latent space guaranteeing continuity. However, these architectures share with standard AEs the problem that they generalize so well that they can also well reconstruct anomalies. In this work we argue that the approach of selecting the worst reconstructed examples as anomalies is too simplistic if a continuous latent space autoencoder-based architecture is employed. We show that outliers tend to lie in the sparsest regions of the combined latent/error space and propose the $$\mathrm{VAE}Out$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>VAE</mml:mi> <mml:mi>O</mml:mi> <mml:mi>u</mml:mi> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> and $${{\mathrm {Latent}}Out}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Latent</mml:mi> <mml:mi>O</mml:mi> <mml:mi>u</mml:mi> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> unsupervised anomaly detection algorithms, identifying outliers by performing density estimation in this augmented feature space. The proposed approach shows sensible improvements in terms of detection performances over the standard approach based on the reconstruction error.
Multi-interaction fusion collaborative filtering for social recommendation

GNNs(Graph Neural Networks) use graph structure to make recommendations, receiving more and more attention. Firstly, existing work focuses on aggregating social interaction information, ignoring users who rate on the same items. Secondly, the existing recommendation methods cannot dynamically reflect changes in user interests. Thirdly, existing methods do not take into account the interaction of subgraphs in GNNs and interactions between user and item factors. In this paper, a graph social fusion recommendation (GSFR) method is proposed. GSFR captures multiple social information simultaneously, based on which it can dynamically adjust user interest weight. Specifically, GSFR captures the interactions in subgraphs with a dynamic attention mechanism, which can represent changes in user interests in heterogeneous networks. A mutualistic mechanism is combined to simulate the mutually reinforcing relationship between social behavior and virtual behavior. User and item latent factors are obtained based on space vectors from the aggregation part. Recommendations are made from inherent characteristics of space vectors interaction behaviors. Comprehensive experimental results on three public datasets show the effectiveness of the proposed model.
Generative and Attentive Fusion for Multi-spectral Vehicle Re-Identification

Vehicle re-identification (Re-ID) task has far-reaching practical application value and research significance in busy traffic monitoring scenes. Most existing works are based on the single RGB modality or RGB-IR (Infrared) cross-modality. Still, it is difficult to be widely applied in multi-scene real life because of the inadequacy of a single modality at the limited daytime. Recently, multi-spectral Re-ID has been proposed to simultaneously use the RGB and IR spectral data to build a data cell that can complement each other to deal with this condition. For multi-spectral vehicle Re-ID, existing methods simply fuse features extracted from a data cell with multiple modalities, which ignores the large gap as well as the relationship between different modalities. To address this problem, in this work, we propose the Generative and Attentive Fusion Network (GAFNet) to fuse the multiple data sources for the vehicle Re-ID task. First, at the input-level, we enrich the data cell by adding the generated transitional modality images, which could involve different data distribution from the original modality images. Note that the newly generated images have high similarity with both the target modality and the source modality, therefore they would help reduce the gap between modalities and indirectly improve the consistency inside a data cell, and further enhance the weight of key information. Second, at the feature-level, we concatenate and fuse the features of the original data and the transitional modalities from different dimensions with the proposed attentive feature fusion module to further align the features and enhance their focused areas in the feature maps. Extensive experimental results on two standard benchmarks demonstrate that our method is effective and outperforms the state-of-the-art methods.
The Corporeality of Infotainment on Fans Feedback Towards Sports Comment Employing Convolutional Long-Short Term Neural Network

In ODIs, World Cups and T-20 matches of various sports like Football, Cricket, Hockey, Basketball and Badminton, fans express their feelings and emotions towards the players by posting their status on social media like Facebook, Twitter etc. By collecting these opinions and feelings of the fans from different mediums, this research study has become more focused on a sentimental analysis of the sport with a total of 3759 comments related to Football (both national, international), Cricket, Hockey and Badminton. Since sports related opinions have been taken up in Bengali, global vector (glove) word embedding techniques are used for pre-processing which can retrieve word meanings and synthetic information. It also specializes in creating word vectors, including the structure of word embedding infrastructure, and provides a special advantage over statistics. Three models have been proposed in our study, one of which is a hybrid model of CNN-LSTM. In the proposed CNN-LSTM model, the CNN model is used to quote various features from word embedding that reflect short-term sentiment dependence while creating long-term sentimental relationships between LSTM words. In comparison to the hybrid model, two single models CNN and LSTM are proposed in five categories (i.e. Positive, Negative, Neutral, Happy, Sad). The sport's dataset integrates the CNN-LSTM hybrid model with the glove embedding layer, providing 97.45% accuracy. Lastly, the LSTM-CNN hybrid models perform comparatively better, realizing the feeling of the fans' comments.
Partial multi-label learning via specific label disambiguation

Partial Multi-Label Learning (PML) aims to learn a robust multi-label classifier from training data, where each instance is associated with a set of candidate labels, among which only a subset of them is relevant. Some existing methods consider the noise in the feature space and have made some achievements. However, they ignored that each label might be only related to a subset of original features, and the other features might be noise in different label spaces. Such as, the similarity between two instances may be different in different label spaces, which is crucial in PML. To tackle the problem, we propose a novel framework named P artial multi-l A bel learning via S pecific l A bel D isambiguation( PASAD ), which tries to extract the label-specific features for disambiguation. Specifically, we first adapt the Hilbert–Schmidt Independence Criterion (HSIC) to identify the projection matrix for each label by maximizing the dependence between feature space and each label space. With these matrices, the instances can be mapped into each label-specific feature space to reduce irrelevant information. Besides, label correlations are considered to enrich the label-specific features. Afterward, we propose a specific label propagation method to estimate the labeling confidence values, which adapts the label propagation in each label-specific feature space and considers the interactions between different label spaces. We combine the two stages in an iterative manner. Finally, any binary classifier can be applied to induce a classification model by each label’s new specific features and credible labels. Tremendous experimental results demonstrate the effectiveness and superiority of our proposed method.