Reviewerly/exHarmony huggingface dataset:
Learning Response-Consistent and Background-Suppressed Correlation Filters for Real-Time UAV Tracking

With the advantages of discriminative correlation filter (DCF) in tracking accuracy and computational efficiency, the DCF-based methods have been widely used in the field of unmanned aerial vehicles (UAV) for target tracking. However, UAV tracking inevitably encounters various challenging scenarios, such as background clutter, similar target, partial/full occlusion, fast motion, etc. These challenges generally lead to multi-peak interferences in the response map that cause the target drift or even loss. To tackle this problem, a response-consistent and background-suppressed correlation filter is proposed for UAV tracking. First, a response-consistent module is developed, in which two response maps are generated by the filter and the features extracted from adjacent frames. Then, these two responses are kept to be consistent with the response from the previous frame. By utilizing the l2-norm constraint for consistency, this module not only can avoid sudden changes of the target response caused by background interferences but also enables the learned filter to preserve the discriminative ability of the previous filter. Second, a novel background-suppressed module is proposed, which makes the learned filter to be more aware of background information by using an attention mask matrix. With the introduction of this module into the DCF framework, the proposed method can further suppress the response interferences of distractors in the background. Finally, extensive comparative experiments have been conducted on three challenging UAV benchmarks, including UAV123@10fps, DTB70 and UAVDT. Experimental results have proved that our tracker has better tracking performance compared with 22 other state-of-the-art trackers. Moreover, our proposed tracker can run at ∼36 FPS on a single CPU for real-time UAV tracking.
GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content

The mobile cloud gaming industry has been rapidly growing over the last decade. When streaming gaming videos are transmitted to customers' client devices from cloud servers, algorithms that can monitor distorted video quality without having any reference video available are desirable tools. However, creating No-Reference Video Quality Assessment (NR VQA) models that can accurately predict the quality of streaming gaming videos rendered by computer graphics engines is a challenging problem, since gaming content generally differs statistically from naturalistic videos, often lacks detail, and contains many smooth regions. Until recently, the problem has been further complicated by the lack of adequate subjective quality databases of mobile gaming content. We have created a new gaming-specific NR VQA model called the Gaming Video Quality Evaluator (GAMIVAL), which combines and leverages the advantages of spatial and temporal gaming distorted scene statistics models, a neural noise model, and deep semantic features. Using a support vector regression (SVR) as a regressor, GAMIVAL achieves superior performance on the new LIVE-Meta Mobile Cloud Gaming (LIVE-Meta MCG) video quality database.
Web Phishing Detection Using Web Crawling, Cloud Infrastructure and Deep Learning Framework

The pandemic of COVID-19 obliges citizens to follow the “work from home “scheme. The Internet is also a powerful channel for social connections. The huge dependency of people on digital media opens doors to fraud. Phishing is a form of cybercrime that is used to rob users of passwords from online banking, e-commerce, online schools, digital markets, and others. Phishers create bogus websites like the original and deliver users spam mails. When an online user visits fake web pages via spam, phishers steal their credentials. As a result, it is important to identify these forms of fraudulent websites until they do any harm to victims. Inspired by the ever-changing existence of phishing websites. This paper reviews the work on Phishing attack detection and aims to examine techniques that mainly detect and help in preventing phishing attacks rather than mitigating them. Here we offered a general overview of the most effective phishing attack detection strategies focused on deep learning.
Control Architecture for a Quadrotor Transporting a Cable-Suspended Load of Uncertain Mass

This paper presents an architecture for controlling a quadrotor transporting a cable-suspended load of uncertain mass. A family of trajectories is proposed that is composed by three phases—lift-off, transit, and landing—and implemented as a hybrid system. The proposed control system uses an adaptive geometric controller with asymptotic tracking stability. The mass of the transported load was estimated using an adaptive mechanism, which adjusts the action resorting to a geometric control method. The resulting system was validated in simulation with a mid-flight mass reduction of the transported load, and tests were performed using a range of values of load mass and maximum forward velocity. There is work in the literature that approaches the cable-suspended component of the problem, and there are also papers focused on uncertainty in the model, mass included. This work aimed to solve these two problems simultaneously, having the uncertain component being the mass of the suspended load.
SAR Ship Detection Based on Improved YOLOv7-Tiny

Ship detection in Synthetic Aperture Radar (SAR) images is an important remote sensing technology. It plays an essential role in maritime monitoring and warfare. Traditional SAR ship detection methods suffer from the interference of complex backgrounds. The speckle noise and the special imaging system of SAR will deteriorate the performance of traditional methods in SAR image interpretation. Deep learning has recently been widely used in SAR image interpretation while achieving state-of-the-art performance. In this paper, a novel SAR ship detection method based on Improved You Only Look Once version 7-tiny (Improved YOLOv7-tiny) is proposed. Based on the YOLOv7-tiny network, we have made the following improvements. First, the coordinate attention is plugged into the backbone network to ensure the lightweight design. Second, the Spatial Pyramid Pooing (SPP) of the backbone network and the SIoU loss function are modified to improve performance. Eventually, the proposed method has achieved better detection performance than YOLOv7-tiny and YOLOv7 on the HRSID dataset.
EQRbot: A chatbot delivering EQR argument-based explanations

Recent years have witnessed the rise of several new argumentation-based support systems, especially in the healthcare industry. In the medical sector, it is imperative that the exchange of information occurs in a clear and accurate way, and this has to be reflected in any employed virtual systems. Argument Schemes and their critical questions represent well-suited formal tools for modeling such information and exchanges since they provide detailed templates for explanations to be delivered. This paper details the EQR argument scheme and deploys it to generate explanations for patients' treatment advice using a chatbot (EQRbot). The EQR scheme (devised as a pattern of Explanation-Question-Response interactions between agents) comprises multiple premises that can be interrogated to disclose additional data. The resulting explanations, obtained as instances of the employed argumentation reasoning engine and the EQR template, will then feed the conversational agent that will exhaustively convey the requested information and answers to follow-on users' queries as personalized Telegram messages. Comparisons with a previous baseline and existing argumentation-based chatbots illustrate the improvements yielded by EQRbot against similar conversational agents.
A Novel Path Planning Method for Aerial UAV based on Improved Genetic Algorithm

In the actual flight mission of modern drones, the range of obstacles in the sensor detection environment will be deviated, and the control system will also have control errors when guiding the drone to fly. Therefore, accurate path planning is very important. In this paper, the novel path planning method for aerial UAV based on improved genetic algorithm is studied. The traditional path planning algorithm is relatively mature and has been widely used in the pathfinding problem of a single UAV, which consider less of the complexity, hence, our model will take the multi-UAV path planning as the target. In the designed pipeline the genetic algorithm is firstly designed, with the prior information of UAV performance constraint model. GA is not good at detailed search of local areas, which leads to poor local search ability of the algorithm and affects the optimization performance of the algorithm, hence, the PSO is combined for the optimization. The parameter of the GA crossover and mutation step is modified with the PSO to achieve the better performance. By simulating the proposed model, the path planning performance under the complex environment is tested.
Two-photon correlations in detuned resonance fluorescence

Abstract We discuss two-photon correlations from the side peaks that are formed when a two-level system emitter is driven coherently, with a detuning between the driving source and the emitter (quasi-resonance fluorescence). We do so in the context of the theories of frequency-resolved photon correlations and homodyning, showing that their combination leads to a neat picture compatible with perturbative two-photon scattering that was popular in the early days of quantum electrodynamics. This should help to control, enhance and open new regimes of multiphoton emission. We also highlight a way to evidence the quantum coherent nature of the process from photoluminescence only, through the observation of a collapse of the symmetry of the lineshape accompanied by a surge of its intensity of emission. We discuss several of our results in the light of recent experimental works.
Wide and deep neural networks achieve consistency for classification

While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are consistent for classification, i.e., whether such models minimize the probability of misclassification for arbitrary data distributions. In this work, we identify and construct an explicit set of neural network classifiers that are consistent. Since effective neural networks in practice are typically both wide and deep, we analyze infinitely wide networks that are also infinitely deep. In particular, using the recent connection between infinitely wide neural networks and neural tangent kernels, we provide explicit activation functions that can be used to construct networks that achieve consistency. Interestingly, these activation functions are simple and easy to implement, yet differ from commonly used activations such as ReLU or sigmoid. More generally, we create a taxonomy of infinitely wide and deep networks and show that these models implement one of three well-known classifiers depending on the activation function used: 1) 1-nearest neighbor (model predictions are given by the label of the nearest training example); 2) majority vote (model predictions are given by the label of the class with the greatest representation in the training set); or 3) singular kernel classifiers (a set of classifiers containing those that achieve consistency). Our results highlight the benefit of using deep networks for classification tasks, in contrast to regression tasks, where excessive depth is harmful.
Knowledge Amalgamation for Object Detection With Transformers

Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, most of these approaches are tailored for convolutional neural networks (CNNs). However, there is a tendency that transformers, with a completely different architecture, are starting to challenge the domination of CNNs in many computer vision tasks. Nevertheless, directly applying the previous KA methods to transformers leads to severe performance degradation. In this work, we explore a more effective KA scheme for transformer-based object detection models. Specifically, considering the architecture characteristics of transformers, we propose to dissolve the KA into two aspects: sequence-level amalgamation (SA) and task-level amalgamation (TA). In particular, a hint is generated within the sequence-level amalgamation by concatenating teacher sequences instead of redundantly aggregating them to a fixed-size one as previous KA works. Besides, the student learns heterogeneous detection tasks through soft targets with efficiency in the task-level amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that the sequence-level amalgamation significantly boosts the performance of students, while the previous methods impair the students. Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
An Interactive Knowledge and Learning Environment in Smart Foodsheds

The Internet of Food (IoF) is an emerging field in smart foodsheds, involving the creation of a knowledge graph (KG) about the environment, agriculture, food, diet, and health. However, the heterogeneity and size of the KG present challenges for downstream tasks, such as information retrieval and interactive exploration. To address those challenges, we propose an interactive knowledge and learning environment (IKLE) that integrates three programming and modeling languages to support multiple downstream tasks in the analysis pipeline. To make IKLE easier to use, we have developed algorithms to automate the generation of each language. In addition, we collaborated with domain experts to design and develop a dataflow visualization system, which embeds the automatic language generations into components and allows users to build their analysis pipeline by dragging and connecting components of interest. We have demonstrated the effectiveness of IKLE through three real-world case studies in smart foodsheds.
Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls for IoT

Devices in the Internet of Things (IoT) usually use cloud storage and cloud computing to save storage and computing cost. Therefore, the efficient realization of one-to-many communication of data on the premise of ensuring the security of cloud storage data is a challenge. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can not only protect the security of data in the cloud and achieve one-to-many communication but also achieve fine-grained access control for data. However, the single-authority CP-ABE faces the crisis of single point of failure. In order to improve security, the Multi-Authority CP-ABE (MA-CP-ABE) is adopted. Although there are provably-secure MA-CP-ABE schemes, Edward Snowden’s research shows that provably-secure cryptographic schemes are vulnerable to backdoor attacks, resulting in secret disclosure, and thus threatening security. In addition, ABE requires huge computational overhead in key generation, encryption and decryption, which increase with the increase in the number of attributes and the complexity of the access structure, and there are a large number of resource-constrained devices in the IoT. To mitigate this issue, we construct the Online/Offline MA-CP-ABE with Cryptographic Reverse Firewalls (OO-MA-CP-ABE-CRFs) scheme. This scheme not only uses Cryptographic Reverse Firewall (CRF) to resist backdoor attacks but also uses online/offline key generation, online/offline encryption and outsourcing encryption technology to optimize the efficiency of the MA-CP-ABE scheme with reverse firewall, reducing the storage and computing cost of users. Finally, the security of the OO-MA-CP-ABE-CRFs scheme is proved, and the experimental results indicate that the scheme is efficient and practical.
Research on Medical Security System Based on Zero Trust

With the rapid development of Internet of Things technology, cloud computing, and big data, the combination of medical systems and information technology has become increasingly close. However, the emergence of intelligent medical systems has brought a series of network security threats and hidden dangers, including data leakage and remote attacks, which can directly threaten patients' lives. To ensure the security of medical information systems and expand the application of zero trust in the medical field, we combined the medical system with the zero-trust security system to propose a zero-trust medical security system. In addition, in its dynamic access control module, based on the RBAC model and the calculation of user behavior risk value and trust, an access control model based on subject behavior evaluation under zero-trust conditions (ABEAC) was designed to improve the security of medical equipment and data. Finally, the feasibility of the system is verified through a simulation experiment.
Bit Reduction for Locality-Sensitive Hashing

Locality-sensitive hashing (LSH) has gained ever-increasing popularity in similarity search for large-scale data. It has competitive search performance when the number of generated hash bits is large, reversely bringing adverse dilemmas for its wide applications. The first purpose of this work is to introduce a novel hash bit reduction schema for hashing techniques to derive shorter binary codes, which has not yet received sufficient concerns. To briefly show how the reduction schema works, the second purpose is to present an effective bit reduction method for LSH under the reduction schema. Specifically, after the hash bits are generated by LSH, they will be put into bit pool as candidates. Then mutual information and data labels are exploited to measure the correlation and structural properties between the hash bits, respectively. Eventually, highly correlated and redundant hash bits can be distinguished and then removed accordingly, without deteriorating the performance greatly. The advantages of our reduction method include that it can not only reduce the number of hash bits effectively but also boost retrieval performance of LSH, making it more appealing and practical in real-world applications. Comprehensive experiments were conducted on three public real-world datasets. The experimental results with representative bit selection methods and the state-of-the-art hashing algorithms demonstrate that the proposed method has encouraging and competitive performance.
Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML). Feature transformation aims to transform original data to identify an optimal feature space that enhances the performances of a downstream ML model. Existing studies either combines preprocessing, feature selection, and generation skills to empirically transform data, or automate feature transformation by machine intelligence, such as reinforcement learning. However, existing studies suffer from: 1) high-dimensional non-discriminative feature space; 2) inability to represent complex situational states; 3) inefficiency in integrating local and global feature information. To fill the research gap, we propose a novel group-wise cascading actor-critic perspective to develop the AI construct of automated feature transformation. Specifically, we formulate the feature transformation task as an iterative, nested process of feature generation and selection, where feature generation is to generate and add new features based on original features, and feature selection is to remove redundant features to control the size of feature space. Our proposed framework has three technical aims: 1) efficient generation; 2) effective policy learning; 3) accurate state perception. For an efficient generation, we develop a tailored feature clustering algorithm and accelerate generation by feature group-group crossing based generation. For effective policy learning, we propose a cascading actor-critic learning strategy to learn state-passing agents to select candidate feature groups and operations for fast feature generation. Such a strategy can effectively learn policies when the original feature size is large, along with exponentially growing feature generation action space, in which classic Q-value estimation methods fail. For accurate state perception of feature space, we develop a state comprehension method considering not only pointwise feature information but also pairwise feature-feature correlations. Finally, we present extensive experiments and case studies to illustrate 24.7% improvements in F1 scores compared with SOTAs and robustness in high-dimensional data.
Data Augmentation-based Novel Deep Learning Method for Deepfaked Images Detection

Recent advances in artificial intelligence have led to deepfake images, enabling users to replace a real face with a genuine one. deepfake images have recently been used to malign public figures, politicians, and even average citizens. deepfake but realistic images have been used to stir political dissatisfaction, blackmail, propagate false news, and even carry out bogus terrorist attacks. Thus, identifying real images from fakes has got more challenging. To avoid these issues, this study employs transfer learning and data augmentation technique to classify deepfake images. For experimentation, 190,335 RGB-resolution deepfake and real images and image augmentation methods are used to prepare the dataset. The experiments use the deep learning models: convolutional neural network (CNN), Inception V3, visual geometry group (VGG19) and VGG16 with a transfer learning approach. Essential evaluation metrics (accuracy, precision, recall, F1-score, confusion matrix and AUC-ROC curve score) are used to test the efficacy of the proposed approach. Results revealed that the proposed approach achieves an accuracy, recall, F1-score and AUC-ROC score of 90% and 91% precision, with our fine-tuned VGG16 model outperforming other DL models in recognizing real and deepfakes.
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation

Recently, Graph Neural Networks (GNNs) achieve remarkable success in Recommendation. To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance. Most GCL methods consist of data augmentation and contrastive loss (e.g., InfoNCE). GCL methods construct the contrastive pairs by hand-crafted graph augmentations and maximize the agreement between different views of the same node compared to that of other nodes, which is known as the InfoMax principle. However, improper data augmentation will hinder the performance of GCL. InfoMin principle, that the good set of views shares minimal information and gives guidelines to design better data augmentation. In this paper, we first propose a new data augmentation (i.e., edge-operating including edge-adding and edge-dropping). Then, guided by InfoMin principle, we propose a novel theoretical guiding contrastive learning framework, named Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL). Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items. Extensive experiments on four public benchmark datasets demonstrate the effectiveness of LDA-GCL.