Reviewerly/exHarmony huggingface dataset:
Mini-Me, You Complete Me! Data-Driven Drone Security via DNN-based Approximate Computing

The safe operation of robotic aerial vehicles (RAV) requires effective security protection of their controllers against cyber-physical attacks. The frequency and sophistication of past attacks against such embedded platforms highlight the need for better defense mechanisms. Existing estimation-based control monitors have tradeoffs, with lightweight linear state estimators lacking sufficient coverage, and heavier data-driven learned models facing implementation and accuracy issues on a constrained real-time RAV. We present Mini-Me, a data-driven online monitoring framework that models the program-level control state dynamics to detect runtime data-oriented attacks against RAVs. Mini-Me leverages the internal dataflow information and control variable dependencies of RAV controller functions to train a neural network-based approximate model as the lightweight replica of the original controller programs. Mini-Me runs the minimal approximate model and detects malicious control state deviation by comparing the estimated outputs with those outputs calculated by the original controller program. We demonstrate Mini-Me on a widely adopted RAV physical model as well as popular RAV virtual models based on open-source firmware, ArduPilot and PX4, and show its effectiveness in detecting five types of attack cases with an average 0.34% space overhead and 2.6% runtime overhead.
Closed-form Minkowski sums of convex bodies with smooth positively curved boundaries

This article derives closed-form parametric formulas for the Minkowski sums of convex bodies in d-dimensional Euclidean space with boundaries that are smooth and have all positive sectional curvatures at every point. Under these conditions, there is a unique relationship between the position of each boundary point and the surface normal. The main results are presented as two theorems. The first theorem directly parameterizes Minkowski sum boundaries using the unit normal vector at each surface point. Although simple to express mathematically, such a parameterization is not always practical to obtain computationally. Therefore, the second theorem derives a more useful parametric closed-form expression using the gradient that is not normalized. In the special case of two ellipsoids, the proposed expressions are identical to those derived previously using geometric interpretations. In order to examine the results, numerical validations and comparisons of the Minkowski sums between two superquadric bodies are conducted. Applications to generate configuration space obstacles in motion planning problems and to improve optimization-based collision detection algorithms are introduced and demonstrated.
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation

Although recent semantic segmentation methods have made remarkable progress, they still rely on large amounts of annotated training data, which are often infeasible to collect in the autonomous driving scenario. Previous works usually tackle this issue with Unsupervised Domain Adaptation (UDA), which entails training a network on synthetic images and applying the model to real ones while minimizing the discrepancy between the two domains. Yet, these techniques do not consider additional information that may be obtained from other tasks. Differently, we propose to exploit self-supervised monocular depth estimation to improve UDA for semantic segmentation. On one hand, we deploy depth to realize a plug-in component which can inject complementary geometric cues into any existing UDA method. We further rely on depth to generate a large and varied set of samples to Self-Train the final model. Our whole proposal allows for achieving state-of-the-art performance (58.8 mIoU) in the GTA5 → CS benchmark. Code is available at https://github.com/CVLAB-Unibo/d4-dbst.
What Can I Do Here? Learning New Skills by Imagining Visual Affordances

A generalist robot equipped with learned skills must be able to perform many tasks in many different environments. However, zero-shot generalization to new settings is not always possible. When the robot encounters a new environment or object, it may need to finetune some of its previously learned skills to accommodate this change. But crucially, previously learned behaviors and models should still be suitable to accelerate this relearning. In this paper, we aim to study how generative models of possible outcomes can allow a robot to learn visual representations of affordances, so that the robot can sample potentially possible outcomes in new situations, and then further train its policy to achieve those outcomes. In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model, attempt to reach them, and thereby update both its skills and its outcome model. We show that this approach can be used to train goal-conditioned policies that operate on raw image inputs, and can rapidly learn to manipulate new objects via our proposed affordance-directed exploration scheme.
Agile incident response (AIR): Improving the incident response process in healthcare

Recent industrial reports show an increased number of cybersecurity incidents, which inflict significant financial losses. Although organisations have been increasing their investments towards information security, incidents continue to occur. Most organisations adopt traditional linear incident response (IR) frameworks to prevent, detect, contain, eradicate and learn lessons from information security incidents. However, due to their rigidness, such linear frameworks are often ineffective. In this study, inspired by the Agile Manifesto, we propose the Agile IR Framework to refine, adjust, and improve the current linear IR process. We use the IR framework of UK's National Health Service (NHS) as an illustrative case, critically analysing the current linear IR framework and demonstrating how it can be transformed into a hybrid IR framework. Using an illustrative case study from the healthcare domain, this study contributes to the incident response literature by showcasing how the integration of Agile principles in archetypical linear IR processes can improve incident response.
SGCL

Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which has aroused increasing research interest in recent years. Simultaneously, signed graphs consisting of both positive and negative links have become ubiquitous with the growing popularity of social media. However, existing works on graph contrastive representation learning are only proposed for unsigned graphs (containing only positive links) and it remains unexplored how they could be applied to signed graphs due to the distinct semantics and complex relations between positive and negative links. Therefore we propose a novel Signed Graph Contrastive Learning model (SGCL) to bridge this gap, which to the best of our knowledge is the first research to employ graph contrastive representation learning on signed graphs. Concretely, we design two types of graph augmentations specific to signed graphs based on a significant signed social theory, i.e., balance theory. Besides, inter-view and intra-view contrastive learning are proposed to learn discriminative node representations from perspectives of graph augmentations and signed structures respectively. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods on both real-world social datasets and online game datasets.
CoMCLOUD: Virtual Machine Coalition for Multi-Tier Applications Over Multi-Cloud Environments

Applications hosted in commercial clouds are typically multi-tier and comprise multiple tightly coupled virtual machines (VMs). Service providers (SPs) cater to the users using VM instances with different configurations and pricing depending on the location of the data center (DC) hosting the VMs. However, selecting VMs to host multi-tier applications is challenging due to the trade-off between cost and quality of service (QoS) depending on the placement of VMs. This paper proposes a multi-cloud broker model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoMCLOUD</i> to select a sub-optimal VM coalition for multi-tier applications from an SP with minimum coalition pricing and maximum QoS. To strike a trade-off between the cost and QoS, we use an ant-colony-based optimization technique. The overall service selection game is modeled as a first-price sealed-bid auction aimed at maximizing the overall revenue of SPs. Further, as the hosted VMs often face demand spikes, we present a parallel migration strategy to migrate VMs with minimum disruption time. Detailed experiments show that our approach can improve the federation profit up to 23% at the expense of increased latency of approximately 15%, compared to the baselines.
Zero-Shot Learning via Contrastive Learning on Dual Knowledge Graphs

Graph Convolutional Networks (GCNs), which can integrate both explicit knowledge and implicit knowledge together, have shown effectively for zero-shot learning problems. Previous GCN-based methods generally leverage a single category (relationship) knowledge graph for zero-shot learning. However, in practical scenarios, multiple types of relationships among categories are usually available which can be represented as multiple knowledge graphs. To this end, we propose a novel dual knowledge graph contrastive learning framework to perform zero-shot learning. The proposed model fully exploits multiple relationships among different categories for zero-shot learning by employing graph convolutional representation and contrastive learning techniques. The main benefit of the proposed contrastive learning module is that it can effectively encourage the consistency of the category representations from different knowledge graphs while enhancing the discriminability of the generated category classifiers. We perform extensive experiments on several benchmark datasets and the experimental results show the superior performance of our approach.
Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction

Augmented reality assisted assembly training (ARAAT) is an effective and affordable technique for labor training in the automobile and electronic industry. In general, most tasks of ARAAT are conducted by real-time hand operations. In this paper, we propose an algorithm of dynamic gesture recognition and prediction that aims to evaluate the standard and achievement of the hand operations for a given task in ARAAT. We consider that the given task can be decomposed into a series of hand operations and furthermore each hand operation into several continuous actions. Then, each action is related with a standard gesture based on the practical assembly task such that the standard and achievement of the actions included in the operations can be identified and predicted by the sequences of gestures instead of the performance throughout the whole task. Based on the practical industrial assembly, we specified five typical tasks, three typical operations, and six standard actions. We used Zernike moments combined histogram of oriented gradient and linear interpolation motion trajectories to represent 2D static and 3D dynamic features of standard gestures, respectively, and chose the directional pulse-coupled neural network as the classifier to recognize the gestures. In addition, we defined an action unit to reduce the dimensions of features and computational cost. During gesture recognition, we optimized the gesture boundaries iteratively by calculating the score probability density distribution to reduce interferences of invalid gestures and improve precision. The proposed algorithm was evaluated on four datasets and proved to increase recognition accuracy and reduce the computational cost from the experimental results.
PIFS Scheme for HEad Pose Estimation Aimed at Faster Face Recognition

In the context of 2D face biometrics, pose represents one challenging intra-class variation that, over the years, has been approached in a number of different ways, such as through inherently pose-invariant features, or by means of specifically trained deep networks, or even by frontalization techniques aimed to restore a canonical pose. To this regard, the idea behind this paper is to perform head pose estimation (HPE) previous to the face recognition step, so that the probe’s resulting yaw, pitch and roll values can be used to possibly find the pose-wise closest elements in the gallery, thus reducing one-to-many matching time. We exploit a common approach to both pose estimation and feature matching, where fractal encoding is used to extract a feature vector and to compare it to a reference template through a metric distance for both HPE and the facial recognition. Experiments conducted on the Biwi Kinect Head Pose database and the AFLW2000 dataset show state-of-the art level of precision for head pose estimation along with a 10x efficiency boost in the overall face recognition task, compared to popular deep learning approaches, whilst achieving also an edge in terms of recognition accuracy.
A Data Trading Scheme With Efficient Data Usage Control for Industrial IoT

The development of Industrial Internet of Things (IIoT) provides massive abundant data resources for trading and mining. However, the existing data trading schemes achieve data usage control at the cost of high latency, thereby resulting in poor service quality as the values of IIoT data degrade over time. This article proposes a monitor-based usage control model to enforce data usage policies on the user side, which eliminates frequent interactions between owners and users. Based on that, a data trading scheme with efficient usage control for IIoT (called DTSI) is devised, which utilizes blockchain smart contract and software guard extensions (SGX) to enable owners to fully control users’ identities and operations at minimal overhead. Security analysis shows that DTSI effectively prevents data abuse and ensures the fair exchange of data. Meanwhile, extensive experiments are conducted on the DTSI prototype comparing with the state-of-the-art schemes with real-world IIoT datasets, which demonstrates the efficiency of DTSI.
Enforcing Temporal Consistency in Video Depth Estimation

Most existing monocular depth estimation methods are trained on single images and have unsatisfactory temporal stability in video prediction. They may rely on post processing to solve this issue. A few video based depth estimation methods use reconstruction framework like structure-from-motion or sequential modeling. These methods have assumptions in the scenarios that they can apply thus limits their real applications. In this work, we present a simple approach for improving temporal consistency in video depth estimation. Specifically, we learn a prior from video data and this prior can be imposed directly into any single image monocular depth method. During testing, our method just performs end-to-end forward inference frame by frame without any sequential module or multi-frame module. In the meanwhile, we propose an evaluation metric that quantitatively measures temporal consistency of video depth predictions. It does not require labelled depth ground truths and only assesses flickering between consecutive frames. Experiments show our method can achieve improved temporal consistency in both standard benchmark and general cases without any post processing and extra computational cost. A subjective study indicates that our proposed metric is consistent with the visual perception of users, and our results with higher consistency scores are indeed preferred. These features make our method a practical video depth estimator to predict dense depth of real scenes and enable several video depth based applications.
Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.
A priori-guided multi-layer rain-aware network for single image deraining

Rain removal from single image is a big challenge due to the limited information in a single image. In this work, we present a priori-guided multi-layer rain-aware convolutional neural network based deraining model, which can learn the density and directional details of rain efficiently. The density perception is implemented by the concatenation of multi-layer detail learning units. The direction perception is implemented by introducing the guided directional priors operators for detail learning network. In order to better restore clean image, the signal-to-noise ratio and structural similarity based loss function is utilized. Numerical experiments on synthetic and real rainy images are given to investigate the performance of proposed method. Comparison with recent state-of-the-art methods suggest that the proposed method can remove rain steaks and protect details of background efficiently. • A priori-guided rain-aware network (PGRAN) for single image deraining is proposed, which learns rain details in different directions by introducing guided Prewitt operator. • A new detail network is designed for rain density-aware by concatenating multi-detail units, which learns rain details in different density levels. • The peak signal to noise ratio and structural similarity based regularizers are introduced in the loss function to improve the deraining results. • Numerical experiments are implemented on three datasets, two synthetic and a real rain dataset. Comparisons with several state-of-the-art methods are also tested and discussed.
Internet of Things security threats and key technologies

With the rapid development of the Internet of Things (IoT), new security issues are increasingly emerging. Security issues are key issues affecting the development of data sharing. Many countries are paying more and more attention to the security of data sharing. IoT represents the next evolution of the internet, taking a huge leap in its ability to gather, analyze, and distribute data that we can turn into information, knowledge and ultimately wisdom. In this context, IoT becomes immensely important. Based on IoT real attack cases in the world, this paper analyzes the security situation of IoT, analyzes the security threats of IoT from three dimensions of physical security, computing security, and data security, and discusses the key technologies that need to be focused on in the security of Internet of Things.
Motion-Sphere: Visual Representation of the Subtle Motion of Human Joints

Understanding and differentiating subtle human motion over time as sequential data is challenging. We propose Motion-sphere, which is a novel trajectory-based visualization technique, to represent human motion on a unit sphere. Motion-sphere adopts a two-fold approach for human motion visualization, namely a three-dimensional (3D) avatar to reconstruct the target motion and an interactive 3D unit sphere, that enables users to perceive subtle human motion as swing trajectories and color-coded miniature 3D models for twist. This also allows for the simultaneous visual comparison of two motions. Therefore, the technique is applicable in a wide range of applications, including rehabilitation, choreography, and physical fitness training. The current work validates the effectiveness of the proposed work with a user study in comparison with existing motion visualization methods. Our study’s findings show that Motion-sphere is informative in terms of quantifying the swing and twist movements. The Motion-sphere is validated in threefold ways: validation of motion reconstruction on the avatar, accuracy of swing, twist, and speed visualization, and the usability and learnability of the Motion-sphere. Multiple range of motions from an online open database are selectively chosen, such that all joint segments are covered. In all fronts, Motion-sphere fares well. Visualization on the 3D unit sphere and the reconstructed 3D avatar make it intuitive to understand the nature of human motion.
Predicting apparent personality from body language: benchmarking deep learning architectures for adaptive social human–robot interaction

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Overview on Binary Optimization Using Swarm-Inspired Algorithms

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility.Based on simple rules, simple reactive agents -swarm (e.g.fish, bird, and ant)directly or indirectly exchange information to find an optimal solution.Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application.This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarmbased techniques.Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap.We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach -binary decision variables and binary search space -is more efficient for binary optimisation in accuracy and computational cost.