Reviewerly/exHarmony huggingface dataset:
Fast Asymmetric and Discrete Cross-Modal Hashing With Semantic Consistency

Hashing has attracted widespread attention in the field of supervised cross-modal retrieval due to its advantages in search and storage. However, there are still some issues to be addressed, e.g.: 1) how to effectively combine sample and label semantics to learn hash codes; 2) how to reduce high computational requirements brought by computing a pairwise similarity matrix; and 3) how to effectively solve discrete optimization problems. To cope with them, a fast asymmetric and discrete cross-modal hashing (FADCH) method is proposed in this article. First, matrix factorization is leveraged to collaboratively construct a common semantic subspace between different modalities. Second, semantic consistency is preserved by aligning the common semantic subspace with the semantic representation constructed from labels, which effectively exploits the semantic complementarity of labels and samples. Third, we embed labels into hash codes and keep the correlation between different modal samples by using a pairwise similarity matrix. Fourth, we use an asymmetric strategy with relaxation to associate hash codes with semantic representation, which not only avoids the difficulty of symmetric frame optimization but also embeds more semantic information into the Hamming space. In addition, a strongly orthogonal constraint is introduced to optimize the hash codes. Finally, an effective optimization algorithm is developed to directly generate discrete hash codes while reducing the complexity from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> . The experimental results on three benchmark datasets illustrate the superiority of the FADCH method.
Enhancing Artificial Intelligence Control Mechanisms: Current Practices, Real Life Applications and Future Views

The popularity of Artificial Intelligence has grown lately with the potential it promises for revolutionizing a wide range of different sectors. To achieve the change, whole community must overcome the Machine Learning (ML) related explainability barrier, an inherent obstacle of current sub symbolism-based approaches, e.g. in Deep Neural Networks, which was not existing during the last AI hype time including some expert and rule-based systems. Due to lack of transparency, privacy, biased systems, lack of governance and accountability, our society demands toolsets to create responsible AI solutions for enabling of unbiased AI systems. These solutions will help business owners to create AI applications which are trust enhancing, open and transparent and also explainable. Properly made systems will enhance trust among employees, business leaders, customers and other stakeholders. The process of overseeing artificial intelligence usage and its influence on related stakeholders belongs to the context of AI Governance. Our work gives a detailed overview of a governance model for Responsible AI, emphasizing fairness, model explainability, and responsibility in large-scale AI technology deployment in real-world organizations. Our goal is to provide the model developers in an organization to understand the Responsible AI with a comprehensive governance framework that outlines the details of the different roles and the key responsibilities. The results work as reference for future research is aimed to encourage area experts from other disciplines towards embracement of AI in their own business sectors, without interpretability shortcoming biases.
Ambient intelligence-based multimodal human action recognition for autonomous systems

Human activity recognition can deduce the behaviour of one or more people from a set of sensor measurements. Despite its widespread applications in monitoring activities, robotics, and visual surveillance, accurate, meticulous, precise and efficient human action recognition remains a challenging research area. As human beings are moving towards the establishment of a smarter planet, human action recognition using ambient intelligence has become an area of huge potential. This work presents a method based on Bi-Convolutional Recurrent Neural Network (Bi-CRNN) -based Feature Extraction and then Random Forest classification for achieving outcomes utilizing Ambient Intelligence that are at the cutting edge of human action recognition for Autonomous Robots. The auto fusion technique used has improved fusion for utilizing and processing data from various sensors. This paper has drawn comparisons with already existing algorithms for Human Action Recognition (HAR) and tried to propose a heuristic and constructive hybrid deep learning-based algorithm with an accuracy of 94.7%.
Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naïve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster–Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.
Emerging Technologies Used in Health Management and Efficiency Improvement During Different Contact Tracing Phases Against COVID-19 Pandemic

Confronted with the COVID-19 health crisis, the year 2020 represented a turning point for the entire world. It paved the way for health-care systems to reaffirm their foundations by using different technologies such as sensors, wearables, mobile applications, drones, robots, Artificial Intelligence (AI), Machine Learning (ML) and the Internet of Things (IoT). A lot of domains have been renovated such as diagnosis, treatment, and monitoring, as well as previously unprecedented domains such as contact tracing. Contact tracing, in conjunction with the emergence, spread, and public compliance for vaccines, was a critical step for controlling and limiting the spread of the pandemic. Traditional contact tracing is usually dependent on individuals ability to recall their interactions, which is challenging and yet not effective. Consequently, further development and usage of automated, privacy-preserving, digital contact-tracing was required. As the pandemic is coming to an end, it is vital to collect and learn the effective used technologies that aided in fighting the virus in order to be prepared for any future pandemics and to be aware of any literature gaps that must be filled. This paper surveys state-of-the-art architectures, platforms, and applications combating COVID-19 at each phase of the five basic contact tracing phases, including case identification, contacts identification and rapid exposure notification, surveillance, regular follow up and prevention. In addition, there is a phase of preparation and post-pandemic services for current and needed future technology that will aid in the fight against any incoming infectious diseases.
Generative Adversarial Networks for anomaly detection in aerial images

Generative Adversarial Networks (GANs) are commonly used as a system able to perform unsupervised learning. We propose and demonstrate the use of a GAN architecture, known as the fast Anomaly Generative Adversarial Network (f-AnoGAN), to solve the problem of anomaly detection from aerial images. This architecture was previously applied to medical images and, in this work, we adapt it for use on satellite or aerial photographs. To test the effectiveness of this approach, we implemented anomaly detection schemes based on the Bi-directional Generative Adversarial Network (BiGAN), the image-z-image mapping (izi), the z-image-z (ziz) mapping, and a deep convolutional autoencoder (AE). The results show that the f-AnoGAN outperformed others, achieving AUC (area under the curve) values of 0.99 and 0.92 for urban and rural spaces image sets, respectively.
CLIP knows image aesthetics

Most Image Aesthetic Assessment (IAA) methods use a pretrained ImageNet classification model as a base to fine-tune. We hypothesize that content classification is not an optimal pretraining task for IAA, since the task discourages the extraction of features that are useful for IAA, e.g., composition, lighting, or style. On the other hand, we argue that the Contrastive Language-Image Pretraining (CLIP) model is a better base for IAA models, since it has been trained using natural language supervision. Due to the rich nature of language, CLIP needs to learn a broad range of image features that correlate with sentences describing the image content, composition, environments, and even subjective feelings about the image. While it has been shown that CLIP extracts features useful for content classification tasks, its suitability for tasks that require the extraction of style-based features like IAA has not yet been shown. We test our hypothesis by conducting a three-step study, investigating the usefulness of features extracted by CLIP compared to features obtained from the last layer of a comparable ImageNet classification model. In each step, we get more computationally expensive. First, we engineer natural language prompts that let CLIP assess an image's aesthetic without adjusting any weights in the model. To overcome the challenge that CLIP's prompting only is applicable to classification tasks, we propose a simple but effective strategy to convert multiple prompts to a continuous scalar as required when predicting an image's mean aesthetic score. Second, we train a linear regression on the AVA dataset using image features obtained by CLIP's image encoder. The resulting model outperforms a linear regression trained on features from an ImageNet classification model. It also shows competitive performance with fully fine-tuned networks based on ImageNet, while only training a single layer. Finally, by fine-tuning CLIP's image encoder on the AVA dataset, we show that CLIP only needs a fraction of training epochs to converge, while also performing better than a fine-tuned ImageNet model. Overall, our experiments suggest that CLIP is better suited as a base model for IAA methods than ImageNet pretrained networks.
Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.
Developing a novel hybrid method based on dispersion entropy and adaptive boosting algorithm for human activity recognition

With the rapid development of technology, human activity recognition (HAR) from sensor data has become a key element for many real-world applications, such as healthcare, disease diagnosis and smart home systems. Although there have been several studies conducted on HAR, traditional methods remain inadequate in balancing efficiency, accuracy and speed. Moreover, existing studies have not identified a solution to managing imbalanced data in different activities groups of HAR, although that is major issue in determining satisfactory performance.this study proposes a new hybrid approach involving hierarchical dispersion entropy (HDE) and Adaptive Boosting with convolutional neural networks (AdaB_CNN) for classifying human activities, such as running downstairs/upstairs, walking and other daily activities, from sensor data. The proposed model is comprised of the following steps: firstly, HAR data are segmented into intervals using a sliding window technique, and then the segmented data are decomposed into different frequency bands. Following this, the dispersion entropy of different frequency bands is computed to produce a feature vector set. Then, the extracted features are reduced using Joint Approximate Diagonalization of Eigenmatrices (JADE) to further eliminate redundant information. The final feature vector set is then fed into the AdaB_CNN method to classify human activities.The proposed approach is tested on three publicly available datasets: WISDM, UCI_HAR 2012, and PAMAP2. The experimental results demonstrate that the proposed model attains a superior performance in HAR to most current methods.The findings reveal that the proposed HDE based AdaB_CNN model has the capability to efficiently recognize different activities from sensor technologies. It has the potential to be implemented in a hardware system to classify human activity.
Graph-powered learning methods in the Internet of Things: A survey

The trend of the era of the Internet of Everything has promoted the integration of various industries and the Internet of Things (IoT) technology, and the scope of influence of the IoT is developing in a wider and deeper level. With the extension of the fields involved, the in-depth progress of the IoT is facing a bottleneck. For example, the security of IoT network and software have problems that are difficult to reconcile. Graph-powered learning methods such as graph embedding and graph neural network (GNN) are expected. How to use the graph learning method in IoT is a question that has to be discussed in relation to the future of the Internet of Things. This paper comprehensively discusses related research and summarizes the progress of using graph-powered learning to promote the network anomaly detection, malware detection, IoT device management, service recommendation and other aspects of IoT. And discuss the results of using graph theory and graph-powered learning methods according to the IoT fields such as smart transportation, Industrial Internet of Things (IIoT), Social Internet of Things (SIoT), smart medical care, smart home, smart grid, and smart city. Finally, in view of the existing issues and trends, this paper proposes future research directions including city various predictions, dynamics and heterogeneity, semantic analysis, resource consumption, point cloud, digital twins, and remote sensing.
Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range monopole-monopole interactions with applications in nuclear physics and in other many-body systems. In addition, it owns a rich phase diagram with different phases and the corresponding phase transition surfaces. The aim of this work is twofold: on one hand, to propose a quantum simulation of the model at the present limits of the trapped ions facilities and, on the other hand, to show how to use a machine learning algorithm on top of the quantum simulation to accurately determine the phase of the system. Concerning the quantum simulation, this proposal is scalable with polynomial resources to larger Agassi systems. Digital quantum simulations of nuclear physics models assisted by machine learning may enable one to outperform the fastest classical computers in determining fundamental aspects of nuclear matter.
The Effects of Avatar and Environment Design on Embodiment, Presence, Activation, and Task Load in a Virtual Reality Exercise Application

The development of embodied Virtual Reality (VR) systems involves multiple central design choices. These design choices affect the user perception and therefore require thorough consideration. This article reports on two user studies investigating the influence of common design choices on relevant intermediate factors (sense of embodiment, presence, motivation, activation, and task load) in a VR application for physical exercises. The first study manipulated the avatar fidelity (abstract, partial body vs. anthropomorphic, full-body) and the environment (with vs. without mirror). The second study manipulated the avatar type (healthy vs. injured) and the environment type (beach vs. hospital) and, hence, the avatar-environment congruence. The full-body avatar significantly increased the sense of embodiment and decreased mental demand. Interestingly, the mirror did not influence the dependent variables. The injured avatar significantly increased the temporal demand. The beach environment significantly reduced the tense activation. On the beach, participants felt more present in the incongruent condition embodying the injured avatar.
Algebraic Formulation and Application of Multi-Input Single-Output Hierarchical Fuzzy Systems With Correction Factors

Hierarchical fuzzy systems (HFSs) are successfully applied to control systems and computer networks. Particularly, correction factor is introduced to regulate fuzzy rules and alleviate the problems of memory usage and real-time performance of systems. This article considers the multi-input single-output HFSs with correction factors, and proposes an algebraic framework by using the semitensor product of matrices. First, a kind of fuzzy correction factor matrix (FCFM) is constructed to realize the fuzzification of correction factor, based on which, an algebraic form is developed for two-input single-output fuzzy systems. Second, the structure diagram of HFSs is decomposed into several fuzzy logic units, which have the form of two-input single-output fuzzy systems with FCFM. Based on this operation, the algebraic formulation of serial, parallel and hybrid HFSs with correction factors is proposed. Finally, the effectiveness of HFSs with FCFM is verified by studying the on-ramp metering of freeway.
Geometric and Textural Augmentation for Domain Gap Reduction

Research has shown that convolutional neural networks for object recognition are vulnerable to changes in depiction because learning is biased towards the low-level statistics of texture patches. Recent works concentrate on improving robustness by applying style transfer to training examples to mitigate against over-fitting to one depiction style. These new approaches improve performance, but they ignore the geometric variations in object shape that real art exhibits: artists deform and warp objects for artistic effect. Motivated by this observation, we propose a method to reduce bias by jointly increasing the texture and geometry diversities of the training data. In effect, we extend the visual object class to include examples with shape changes that artists use. Specifically, we learn the distribution of warps that cover each given object class. Together with augmenting textures based on a broad distribution of styles, we show by experiments that our method improves performance on several cross-domain benchmarks.
Occlusion-robust Face Alignment using A Viewpoint-invariant Hierarchical Network Architecture

The occlusion problem heavily degrades the localization performance of face alignment. Most current solutions for this problem focus on annotating new occlusion data, introducing boundary estimation, and stacking deeper models to improve the robustness of neural networks. However, the performance degradation of models remains under extreme occlusion (i.e. average occlusion of over 50%) because of missing a large amount of facial context information. We argue that exploring neural networks to model the facial hierarchies is a more promising method for dealing with extreme occlusion. Surprisingly, in recent studies, little effort has been devoted to representing the facial hierarchies using neural networks. This paper proposes a new network architecture called GlomFace to model the facial hierarchies against various occlusions, which draws inspiration from the viewpoint-invariant hierarchy of facial structure. Specifically, GlomFace is functionally divided into two modules: the part-whole hierarchical module and the whole-part hierarchical module. The former captures the part-whole hierarchical dependencies of facial parts to suppress multi-scale occlusion information, whereas the latter injects structural reasoning into neural networks by building the whole-part hierarchical relations among facial parts. As a result, GlomFace has a clear topological interpretation due to its correspondence to the facial hierarchies. Extensive experimental results indicate that the proposed GlomFace performs comparably to existing state-of-the-art methods, especially in cases of extreme occlusion. Models are available at https://github.com/zhuccly/GlomFace-Face-Alignment.
Granular-based state estimation for nonlinear fractional control systems and its circuit cognitive application

The issue of continuous observer design for integer order nonlinear systems has been widely studied in existing works. Yet, there are few works that closely related to granular computing theory and discrete signal to better estimate system states. In this paper, by using the granular function description, we propose a novel impulsive observer design algorithm to ensure the cognitive convergence of error dynamic systems. Especially, the resulting criterion reflects well the relationship between the fractional order and the pulse signal. A numerical simulation is provided to illustrate effectiveness of the given method.