Reviewerly/exHarmony huggingface dataset:
Satellite Video Scene Classification Using Low-Rank Sparse Representation Two-Stream Networks

Satellite video scene classification (SVSC) is a challenging work in remote sensing. The main procedure of SVSC is spatial–temporal feature extraction. Unfortunately, massive numbers of dim small moving targets and the low signal-to-noise ratio (SNR) of satellite video bring great challenges to feature extraction. It is difficult to apply traditional feature extraction methods to SVSC because they are used to classify the actions of high-quality video. According to the theory of low-rank sparse decomposition, a Low-rank Sparse Representation Two-stream Network (LSRTN) is designed to increase the classification accuracy of two-stream networks. First, we propose a Low-rank Sparse Component Analysis Network (LSCAN) to decompose satellite videos into low-rank background images and sparse moving target sequences. The LSCAN possesses the advantage of low-rank sparse decomposition to solve small targets and has the capability to adjust the features using the data. Moreover, the LSCAN can efficiently improve the feature extraction of low SNR video. Second, a two-stream structure that was proven to be effective for multiclass video classification was applied to obtain the spatial features and temporal features in each stream. Finally, a fully connected layer integrates the features to classify the satellite video scenes. To utilize the label information, we refine the loss function to adjust the degree of low-rank sparse characteristics and ensure the classification accuracy of training. The experimental results demonstrate that the proposed method achieves better performance than the baseline methods for the SVSC task.
Weakly Supervised Semantic and Attentive Data Mixing Augmentation for Fine-Grained Visual Categorization

As a key factor, the availability of large-scale training samples determines the improvement of visual performance.However, the size of Fine-Grained Visual Categorization (FGVC) datasets is always limited.Therefore, overfitting as an issue in FGVC-related training needs to be solved.Data mixing augmentation is a widely-used data augmentation method.In most of the recently proposed data mixing augmentation methods, random patch selection may generate meaningless training samples and result in model instability during the training process.This paper proposes a data mixing augmentation strategy termed Semantic and Attentive Data Mixing (SADMix) to select semantic patches for the generation of new training samples.In SADMix, a certain number of critical regions are localized according to convolutional activations.An image patch is selected from these localized regions for the generation of new training samples.The size, aspect ratio, and center location of these image patches are changed according to the random values from a beta distribution.These image patches with semantic information are used to mix two training images.According to the class activation map (CAM), training images and their labels are mixed proportionally to generate new mixed training samples and the corresponding labels.The proposed SADMix is tested on three fine-grained datasets, which are CUB-200-2011, FGVC Aircraft, and Stanford Cars, respectively.The experimental results confirm the effectiveness of the proposed SADMix.
Multi-target prediction for dummies using two-branch neural networks

Multi-target prediction (MTP) serves as an umbrella term for machine learning tasks that concern the simultaneous prediction of multiple target variables. Classical instantiations are multi-label classification, multivariate regression, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. Despite the significant similarities, all these domains have evolved separately into distinct research areas over the last two decades. This led to the development of a plethora of highly-engineered methods, and created a substantially-high entrance barrier for machine learning practitioners that are not experts in the field. In this work we present a generic deep learning methodology that can be used for a wide range of multi-target prediction problems. We introduce a flexible multi-branch neural network architecture, partially configured via a questionnaire that helps end users to select a suitable MTP problem setting for their needs. Experimental results for a wide range of domains illustrate that the proposed methodology manifests a competitive performance compared to methods from specific MTP domains.
ControlVAE: Tuning, Analytical Properties, and Performance Analysis

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) controller, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point) that is effective in avoiding posterior collapse and learning disentangled representations. While prior work developed alternative techniques for controlling the KL divergence, we show that our PI controller has better stability properties and thus better convergence, thereby producing better disentangled representations from finite training data. In order to improve the ELBO of ControlVAE over that of the regular VAE, we provide a simplified theoretical analysis to inform the choice of set point for the KL-divergence of ControlVAE. We evaluate the proposed method on three tasks: image generation, language modeling, and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, our method can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, it can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation.
ANALYSIS OF FACTORS AFFECTING CYBER SECURITY OF A HIGHER MILITARY EDUCATIONAL INSTITUTION

The impact of the development and dissemination of information and communication technologies (ICT) in higher military educational institutions (HMEI) is considered in the article, as on the one hand, it increases its efficiency and promotes the training of highly qualified personnel (tactical, operational and strategic level of military education) for the Security Sector and defense of Ukraine, which is extremely necessary in the case of armed aggression by the Russian Federation, and on the other hand, it makes its information space vulnerable to cyberattacks, which the issue of cybersecurity of HMEI raises. At the same time, the author focuses on the analysis of cyber-attacks on educational institutions in recent years, which are due to the development of methods (means) of their implementation and wide access to them by various users, including attackers. In addition, Distributed Denial of Service (DDoS) cyber-attack is the most common cyber threat to international educational institutions, according to an analytical report by Netscout (a developer of ICT solutions to combat DDoS cyberattacks in the United States). It has been analyzed that criminals have recently used DDoS cyberattacks to extort money. Moreover, DDoS cyberattacks were aimed at banks, stock exchanges, travel agencies, currency exchanges and educational institutions. Therefore, the cybersecurity of HMEI needs constant attention from the participants of its provision. In addition, the analysis shows that the cybersecurity of any university is influenced by external and internal factors, which confirm the relevance of the chosen area of research. Therefore, the cybersecurity of HMEI requires an analysis of the factors that affect it, in order to choose the best option for its implementation. Accordingly, the essence and main features of the impact of factors on the cybersecurity of HMEI are identified and their characteristics are presented. The influence of factors on the cybersecurity of HMEI has been decomposed, in particular on the interdependence and criticality of their impact. The necessity of taking into account and constant monitoring of the influence of external and internal factors on the cybersecurity of HMEI is substantiated, which allows to get situational awareness of the current state of cybersecurity and to make appropriate decisions to the management.
Normalizing Flow for Synthetic Medical Images Generation

Deep generative models, such as generative adversarial network (GAN) and variational autoencoder (VAE), have been utilized extensively for medical image generation. While these models made remarkable progress in medical image synthesis, they can not explicitly learn the probability density function of the input data and are highly sensitive to the hyperparameter selections. To mitigate these issues, a new type of deep generative model, called Normalizing Flows (NFs), have emerged in recent years. In this paper, we investigate NFs as an alternative for synthesizing medical images. In particular, we utilize realNVP, a popular NF model for the purpose of synthesizing medical images. To evaluate our synthesized images, we propose to utilize Wasserstien distance along with the permutation test to quantify the quality of the generated images. Within our quantifying metric, our results indicate that the two sample distributions, the first being the samples obtained from our NF model and second being the original dataset, are similar providing a promising indication of normalizing flow’s capability in medical images generation.
Granularity-aware Area Prototypical Network with Bimargin Loss for Few Shot Relation Classification

Relation Classification is one of the most important tasks in text mining. Previous methods either require large-scale manually-annotated data or rely on distant supervision approaches which suffer from the long-tail problem. To reduce the expensive manually-annotating cost and solve the long-tail problem, prototypical networks are widely used in few-shot RC tasks. Despite their remarkable performance, current prototypical networks ignore the different granularities of relations, which degrades the classification performance dramatically. Moreover, the optimization of current prototypical networks simply relies on the cross-entropy loss, which cannot consider the intra-relation compactness and the dispersion among relations in a semantic space. It is not robust enough for current prototypical network in real-world and complicated scenarios. In this paper, we propose an area prototypical network with a granularity-aware measurement, aiming to considering the different granularities of relations. Each relation is represented as an area whose width can reflect the granularity level of relation. Moreover, to improve the robustness, bimargin loss is designed to force area prototypical network to improve the intra-relation compactness and inter-relation dispersion for the feature representation in a semantic space. Extensive experiments on two public datasets are conducted and evaluate the effectiveness of our proposed model.
Rateless codes for near-perfect load balancing in distributed matrix-vector multiplication

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes---computing nodes that unpredictably slow down or fail---is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes, thus resulting in a lot of redundant computation. We propose a rateless fountain coding strategy that achieves the best of both worlds---we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the m rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes. Evaluation on parallel and distributed computing yields as much as three times speedup over uncoded schemes.
DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains, including the security sector. Several law enforcement agencies (LEAs) employ AI in their daily operations for forensics and surveillance. AR is also gaining traction in security, particularly with the advent of affordable wearable devices. Equipping police officers with the tools to facilitate an elevated situational awareness (SA) in patrolling and tactical scenarios is expected to improve LEAs' safety and capacity to deliver crucial blows against terrorist and/or criminal threats. Methods: In this paper we present DARLENE, an ecosystem incorporating novel AI techniques for activity recognition and pose estimation tasks, combined with a wearable AR framework for visualization of the inferenced results via dynamic content adaptation according to the wearer's stress level and operational context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs' SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans' and objects' detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results, constituting the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
Precision optimized pose and trajectory planning for vertically articulated robot arms

Vertically articulated robot arms are widely used in various industries for the realization of conventional handling tasks due to their high flexibility, good workspace-to-footprint ratio and low price. However, due to their kinematic structure, their accuracy, precision and stiffness are highly dependent on the robots configuration. Standards such as EN-ISO-9283 define procedures for determining accuracy and precision by averaging measurements at different points in the robot's main workspace. While these averages can be used to estimate the robots overall performance, they do not support process developers in identifying precision optimal poses. This paper presents a generic and comprehensive routine for the combined identification of precision-optimal robot poses and trajectories for pick and place processes. The routine consists of several cascading optimization loops with the objectives of minimizing the structural and dimensional sensitivity costs of the trajectories' start and end configurations, minimizing the number of actuated joints and minimizing the required direction changes of the joints in motion. Using a sampling-based trajectory planner (OMPL in MoveIt! / ROS), a smooth and collision free trajectory for an exemplary pick and place process is generated and experimentally validated for a KUKA LWR iiwa 14. The optimized trajectory is benchmarked against a conventional, unoptimized reference trajectory for the same handling task. The experimental validation shows a significant increase in the repeatability of the positioning of 36µm (at 3σ) compared to both the nominal repeatability of 150µm and the repeatability of the reference trajectory of 94µm (at 3σ, 2.6-fold increase). Transferring the developed routine to inherently more precise robot models will lead to highly precise vertically articulated robot arms capable of solving more demanding assembly tasks.
Securing Internet of Things devices by enabling Ethereum blockchain using smart contracts

The development of the IoT is rapidly growing. These IoT devices are mainly deployed to control and report environmental changes, prevent risks, and bring many beneficial services. However, these benefits may open doors to the adversaries in conjunction with security vulnerabilities and privacy issues. In the recent past, Blockchain has been an emerging technology that reaches several use-cases apart from cryptocurrency. For instance, IoT integration with Blockchain implementation yet indefinite required further research because of resource-constrained IoT devices and ledger-based Blockchain protocol design. This paper presents the systematic implementation of securing IoT devices by enabling the Ethereum Blockchain smart contract. The results show that the collected information is securely stored in the Blockchain after successful authentication. Practical Application: Blockchain innovations have the power to transform manufacturing, construction, healthcare and building supply chains by eliminating the middleman, streamlining operations, improving overall security, and simplifying data management. Onboarding, recordkeeping, client screening, data management, security, privacy, and transaction and trade processing are examples of several practice applications in the financial, insurance, and eHealth services industries. Thus, this study ensures security by enabling Ethereum blockchain and smart contracts in an authentic blockchain applications for building sustainable environments to improve readability and trustworthiness of the transactions.
Visually Grounded Task and Motion Planning for Mobile Manipulation

Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve robot behaviors that take extended periods of time (e.g., long-distance navigation). In this paper, we develop a visual grounding approach to help robots probabilistically evaluate action feasibility, and introduce a TAMP algorithm, called GROP, that optimizes both feasibility and efficiency. We have collected a dataset that includes 96, 000 simulated trials of a robot conducting mobile manipulation tasks, and then used the dataset to learn to ground symbolic spatial relationships for action feasibility evaluation. Compared with competitive TAMP baselines, GROP exhibited a higher task-completion rate while maintaining lower or comparable action costs. In addition to these extensive experiments in simulation, GROP is fully implemented and tested on a real robot system.
Real-time Face Recognition using SVM, MLP and CNN

Real-time face recognition targets to match a human’s face in a digital image or a video frame against faces in a database, which plays an important role in various fields nowadays. Current face recognition methods use different classification algorithms to achieve their functionalities for different purposes such as identification and entertainment. However, most current face recognition methods are fulfilled by only one algorithm, which causes inaccurate predictions. This paper proposes a method that fuses support vector machine (SVM), Multi-layer Perceptron (MLP) and Convolutional Neural Network (CNN), into a single real-time face recognition system for Real-time face recognition and enhancing the prediction accuracy. Specifically, we first crawl the data from the internet, process them into a 1-D array and store them into the database for the first step. Then, the data from the database is used to train models including SVM, MLP and CNN. Next, we fused these three algorithms to develop the application and calibrate the camera to reduce distortion. Finally, we develop the front-end interface of this application using the Tkinter module. To verify the effectiveness of our method, we compare our methods with SVM, MLP and CNN. The accuracy of the fused algorithm is 90%. In comparison, the accuracies of SVM, MLP and CNN are 60%, 70% and 80%, respectively. The results demonstrate that the fused algorithm outperforms the other three situations and have the potential to contribute to the future development of real-time face recognition systems.
Metadata Privacy Preservation for Blockchain-Based Healthcare Systems

Blockchain-based healthcare systems provide a patient-centric and accountable way to manage and share electronic health records. Thanks to its unique features, the blockchain is employed to record the metadata and to carry out access control. Nevertheless, the transparent nature of blockchain also poses a new challenge to these systems. We identify that the metadata stored on the blockchain leaks the relationship between doctors and patients. Based on this relationship, the adversary can launch linkage attacks to infer patients' information. Hence, it is necessary to protect metadata privacy. However, strong privacy protection may reduce accountability. Striking a balance between accountability and privacy preservation is a major challenge for the blockchain and its applications. In this paper, we first elaborate on the reasons why the metadata could leak the privacy of patients in blockchain-based healthcare systems. After that, we propose privacy-preserving and accountable protocols to deal with this problem for two different healthcare scenarios: the single doctor case and the group consultation case. Finally, the theoretical analysis demonstrates the practicality of our protocols.
Post-quantum cryptography-driven security framework for cloud computing

Abstract Data security in the cloud has been a major issue since the inception and adoption of cloud computing. Various frameworks have been proposed, and yet data breach prevails. With encryption being the dominant method of cloud data security, the advent of quantum computing implies an urgent need to proffer a model that will provide adequate data security for both classical and quantum computing. Thus, most cryptosystems will be rendered susceptible and obsolete, though some cryptosystems will stand the test of quantum computing. The article proposes a model that comprises the application of a variant of McEliece cryptosystem, which has been tipped to replace Rivest–Shamir–Adleman (RSA) in the quantum computing era to secure access control data and the application of a variant of N-th degree truncated polynomial ring units (NTRU) cryptosystem to secure cloud user data. The simulation of the proposed McEliece algorithm showed that the algorithm has a better time complexity than the existing McEliece cryptosystem. Furthermore, the novel tweaking of parameters S and P further improves the security of the proposed algorithms. More so, the simulation of the proposed NTRU algorithm revealed that the existing NTRU cryptosystem had a superior time complexity when juxtaposed with the proposed NTRU cryptosystem.