Reviewerly/exHarmony huggingface dataset:
Detection of GAN-Generated Images by Estimating Artifact Similarity

Recently, researchers have been dedicated to discovering Generative Adversarial Network (GAN) artifacts and using them to identify generated images. However, current approaches exhibit restricted performance when testing against unseen GAN models, which is also known as the cross-domain scenario. To overcome this limitation, we propose a novel GAN-generated image detection framework by estimating artifact similarity, which is inspired by relation network. The proposed method consists of two stages, including representation learning and representation comparison. For representation learning, ResNet-50 equipped with Instance Normalization in the Shallow layers (ResNet-INS) is constructed as the embedding network to extract generalized features. For representation comparison, Category and Domain-Aware loss function (CDA loss) is designed by leveraging both category and domain information efficiently, which can enlarge inter-class discrepancy of different categories (GAN-generated or pristine images) and improve intra-class compactness from different domains (source attributions) in the same category. Extensive experiments are conducted which consider various cross-domain scenarios to verify the generalization of the proposed method. Besides, our method exhibits satisfying robustness against common post-processings, even when data augmentation is not considered during the training stage.
A New Secure Model for Data Protection over Cloud Computing

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.
The Role of IoT in the Fight Against Covid-19 to Restructure the Economy

In the current Covid-19 situation, people all over the world must begin to adhere to social distancing guidelines. However, ensuring that people follow these social distancing guidelines has proven to be a difficult challenge for the government. To combat this issue, authorities have implemented IoT solutions such as remote health monitoring and contact tracing techniques to effectively manage the spread of coronavirus. To address this issue in depth, the current paper provides a comprehensive review of how IoT applications have aided the healthcare, education, home, entertainment, transportation, hospitality, and retail sectors of an economy. Additionally, the risk and challenges posed by the widespread adoption of IoT solutions are also addressed. Finally, the paper suggests ways to mitigate existing challenges associated with the implementation of IoT solutions.
KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity Linking

Entity linking is a fundamental task for a successful use of knowledge graphs in many information systems. It maps textual mentions to their corresponding entities in a given knowledge graph. However, with the rapid evolution of knowledge graphs, a large number of entities is continuously added over time. Performing entity linking on new, or unseen, entities poses a great challenge, as standard entity linking approaches require large amounts of labeled data for all new entities, and the underlying model must be regularly updated. To address this challenge, several zero-shot entity linking approaches have been proposed, which don't require additional labeled data to perform entity linking over unseen entities and new domains. Most of these approaches use large language models, such as BERT, to encode the textual description of the mentions and entities in a common embedding space, which allows linking mentions to unseen entities. While such approaches have shown good performance, one big drawback is that they are not able to exploit the entity symbolic information from the knowledge graph, such as entity types, relations, popularity scores and graph embeddings. In this paper, we present KG-ZESHEL, a knowledge graph-enhanced zero-shot entity linking approach, which extends an existing BERT-based zero-shot entity linking approach with mention and entity auxiliary information. Experiments on two benchmark entity linking datasets, show that our proposed approach outperforms the related BERT-based state-of-the-art entity linking models.
A comprehensive framework of multiple semantics preservation in neural style transfer

Recently neural style transfer has achieved great development, but there is still a big gap compared with manual creation. Most of the existing methods ignore the comprehensive consideration of preserving various semantic information of original content images, resulting in distortion or loss of original content features of the generated works, which are dull and difficult to convey the original themes and emotions. In this paper, we analyze the ability of the existing methods to maintain single semantic information and propose a fast style transfer framework with multi-semantic preservation. The experiments indicate that our method can effectively retain the original semantic information including salience and depth features, so that the final artwork has better visual effect by highlighting its regional focus and depth information. Compared with existing methods, our method has better ability in semantic preservation and can generate more artworks with distinct regions, controllable semantics, diverse contents and rich emotions.
Automated Evolutionary Design of CNN Classifiers for Object Recognition on Satellite Images

In the paper, the automated evolutionary approach FEDOT-NAS for the design of convolutional neural networks is proposed. It allows building object recognition models for remote sensing tasks. The comparison of the proposed approach with state-of-the-art tools for neural architecture search is conducted for several examples of satellite-related datasets. The results of the experiments confirm the correctness and effectiveness of the proposed approach. The implementation of FEDOT-NAS is available as an open-source tool.
Multi-sensory assessment for hand pattern recognition

This study presents and assesses a multi-sensory hand pattern recognizer. We experimentally evaluated multiple features from the selected sensors, detected the most significant ones, and set the best combination possible to reach optimal hand shape detection. multilayer perceptron (MLP) and Decision Tree (DT) classifiers were used, and we used these two classification techniques to ensure that the results were not related to the classification technique. Leap Motion (LM) controllers and a VIVE Pro Eye camera were used to detect hand keypoints, and those last sensors are vision-based. We integrated this with a MYO armband, which streams eight EMG channels and inertial measurement unit data. All the sensors’ data were synchronized and streamed in real-time to be treated and fused. We evaluated our integration on the classification of the American Sign Language (ASL). We worked with seven participants, and we detected the best features subset. The overall results were satisfactory, wherein we reached an average classification of 98.31% with the optimal subset.
Evaluating and Ranking of Critical Success Factors of Cloud Enterprise Resource Planning Adoption Using MCDM Approach

Digital technology advancement and the Internet of Things (IoT) are playing a major role to take a big leap towards achieving Industry 4.0.Cloud-based data management and big data analytics have given rise to adopt Cloud Enterprise Resource Planning (CERP).The CERP has become a significant tool for the success of the information management system (IMS) which is ultimately responsible for the success of any organization.The selection of CERP depends on many critical success factors (CSFs) that must be considered while evaluating and selecting a CERP.In this work, identified CSFs of CERP are modeled using a multi-criteria decision-making (MCDM) approach.The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) based modelling have been carried out to derive the ranking of the CSFs responsible for the CERP.The group decision-making (GDM) based AHP has also been adopted to build the decision-making model.The paper models 5 dimensions and 20 sub-criteria factors to provide the prioritized rank of dimensions and sub-criteria factors.The AHP and FAHP models identify the ranking of the 5 dimensions as Organizational Behavior, Cloud ERP Essentials, Technological Advancement, Innovational Ideas, and Environmental Impact.
Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.
Wideband, Real-time Spectro-Temporal RF Identification

RF emissions' detection, classification, and spectro-temporal localization are crucial not only for tasks relating to understanding, managing, and protecting the RF spectrum, but also for safety and security applications such as detecting intruding drones or jammers. Achieving this goal for wideband spectrum and in real-time is a challenging problem. Existing methods are limited to a small bandwidth, and lack the capability to detect and classify multiple RF emissions in every part of a wide spectrum with a unified detection and classification solution. We present WRIST, a Wideband, Real-time RF Identification system with Spectro-Temporal detection,framework and system. Our resulting deep learning (DL) model is capable to detect, classify, and precisely locate RF emissions in time and frequency using RF samples of 100 MHz spectrum in real-time(over 6Gbps incoming I&Q streams). Such capabilities are made feasible by leveraging a deep learning-based one-stage object detection framework, and transfer learning to a multi-channel visual-based RF signals representation. We also introduce an iterative training approach which leverages synthesized and augmented RF data to efficiently build large labelled datasets of RF emissions. WRIST's detector achieves 90 mean Average Precision even in extremely congested environment in the wild. WRIST model classifies five technologies (Bluetooth, Lightbridge, Wi-Fi, XPD, and ZigBee) and is easily extendable to others.
Sliding window change point detection based dynamic network model inference framework for airport ground service process

With the rapid development of civil aviation, the number of airport flights is continually increasing and the low efficiency of flight ground services has become one of the main reasons for flight delays. Obtaining a precise description of the flight ground service network is of great importance to the efficient and safe operation of airports. In this study, we constructed a dynamic network model inference framework based on sliding window change point detection. First, a change point detection algorithm was proposed to divide all time points into two parts, namely, a set of possible change points and a set of completely impossible change points, which greatly reduces the true change point search space. Then, two novel change point elimination judgment rules were designed and combined with the structure learning algorithm to achieve iterative optimization within the possible change point sets, obtain the final true change points, and realize precise dynamic network inference. Based on the experimental results, our proposed method has a higher accuracy and computational efficiency than the state-of-the-art methods, and it can be easily adapted to large-scale datasets. Finally, the proposed model was used in an airport flight ground service analysis. Each day was divided into four stages and the potential dependence relationships between different nodes in each stage were accurately determined. Thus, the model has great practical significance for the accurate scheduling and efficient operation of airport ground services.
A Deep Learning Model for Anomalous Wireless Link Detection

Machine learning (ML) techniques play a significant role in detecting anomalous wireless links. However, to date, to the extent of our knowledge, there is no robust classifier that would work in a realistic scenario where various anomalies could appear concurrently in the time-series gleaned from the network monitoring tools. In this paper, we propose a new deep learning based classifier and show that is able to outperform the state of the art for existing link layer anomalies. Our evaluation results demonstrate that the state-of-the ML models perform with an average accuracy of about 63%, whereas the average accuracy of the proposed DL model is around 90%, indicating a significant improvement of 27 percentage points anomaly detection performance.
Robust UKF-based filtering for tracking a maneuvering hypersonic glide vehicle

To track a non-cooperative hypersonic glide vehicle (HGV) without any precise information, an approach to the state estimation is presented based on a robust UKF-based filter (RUKFBF) in this paper. The HGV has an uncertain reentry motion because of unknown maneuvers which is a primary factor leading to degradation of tracking accuracy. Aiming at enhancing accuracy, the strong tracking algorithm (STA) is introduced to addressing the model error caused by a bank-reversal maneuver of HGV. Furthermore, the Huber technique is employed to deal with possible measurement model errors. In the RUKFBF, mutual interferences are suppressed between the STA and the Huber technique via two strategies. The one is that the calculation of the fading factor in the STA adopts an unmodified measurement noise covariance, and the other one is that two judgment criteria are proposed to limit large fading factors in the presence of measurement model errors. To simulate real tracking scenarios, the RUKFBF is tested through tracking a HGV trajectory considering a practical guidance strategy. Simulation results demonstrate the effectiveness of the RUKFBF in the presence of model errors and the observability of the estimated state.
Multi-Fusion Sensors for Action Recognition based on Discriminative Motion Cues and Random Forest

Through proper monitoring and recognition of actions of the human body many applications are possible in the areas of robotics, surveillance or personal health monitoring. Wearable inertial sensors and depth cameras significantly important for human action recognition results. Numerous computer vision methods are used for recognizing humanly actions but the performance is affected by location and orientation sensitivity or background clutter. In this paper, we proposed a novel approach to recognize the action, each action extract on the base of feature extraction method of time-domain, directional angle and depth motion map, for human action recognition (HAR) system. We extract different valuable features by different feature extraction methods. We used Random Forest Algorithm as a classifier with a benchmark UTD-MHAD dataset that achieves an accuracy of 90%. The results prove that the recognition provided by our proposed method is comparatively better in term of effectiveness and imprecision.
Research on Multi-AUV Cooperative Obstacle Avoidance Method During Formation Trajectory Tracking

This research is oriented to the obstacle avoidance problem of multi-AUV formations in three-dimensional space. The four AUVs form and maintain a line-shaped formation on the vertical plane based on the virtual leader method to track the trajectory and achieve stable convergence. When the AUV detects the obstacle in real-time, it will carry out autonomous and coordinated obstacle avoidance operations according to the proposed improved artificial potential field method. Then, the convergence of the way is demonstrated through the generalized energy function. Finally, the simulation verifies the usability of the vertical plane formation and the effectiveness of the improved artificial potential field method. Research proves that method we proposed can achieve stable convergence of formation trajectory tracking.
Multimedia Learning in Virtual and Mixed Reality

This chapter reviews media comparison research on the effects of various immersive technologies, including virtual reality and two types of mixed reality, augmented reality and augmented virtuality, on learning outcomes, as well as some boundary conditions for these effects. In sum, previous meta-analyses report that low immersion virtual reality (d = .22–.41) and low immersion augmented reality (d = .46–.68) improve learning outcomes compared to other instructional media, with small-to-medium-sized effects. However, high immersion virtual reality (median d = .10) and high immersion augmented reality (median d = .16) are less promising. Research on augmented virtuality is sparse, but shows positive effects on learning (median d = .45) based on a few studies. Theoretical implications of these immersive technologies regarding cognitive frameworks, as well as their practical implications on the future of technology in the classroom, are discussed.
Image-Based Texture Styling for Motion Effect Rendering

A motion platform provides the vestibular stimuli that elicit the sensations of self-motion and thereby improves the immersiveness. A representative example is a 4D Ride, which presents a video of POV shots and motion effects synchronized with the camera motion in the video. Previous research efforts resulted in a few automatic motion effect synthesis algorithms for POV shots. Although effective in generating gross motion effects, they do not consider fine features on the ground, such as a rough or bumpy road. In this paper, we propose an algorithm for styling the gross motion effects using a texture image. Our algorithm transforms a texture image into a high-frequency style motion and merges it with the original motion while respecting both perceptual and device constraints. A user study demonstrated that texture styling could increase immersiveness, realism, and harmony.
BiasHeal: On-the-Fly Black-Box Healing of Bias in Sentiment Analysis Systems

Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to make an SA system heal bias and construct fair results when bias is detected. We propose and investigate six self-healing strategies. Our evaluation results on two datasets show that the best strategy can construct fair results and improve accuracy on the two datasets by 2.76% and 2.85%, respectively. To the best of our knowledge, our work is the first self-healing method that can be deployed to ensure SA fairness without requiring access to the SA engine or its training data.
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machine Learning

HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5 % while increasing the difference in video multimethod assessment fusion to 5.6 points.