Reviewerly/exHarmony huggingface dataset:
Recognition Algorithms Based on the Selection of 2D Representative Pseudo-objects

Building of a recognition algorithms (RA) based on the selection of representative pseudo-objects and providing a solution to the problem of recognition of objects represented in a big-dimensionality feature space (BDFS) are described in this article. The proposed approach is based on the formation of a set of 2D basic pseudo-objects and the determination of a suitable set of 2D proximity functions (PF) when designing an extreme RA. The article contains a parametric description of the proposed RA. It is presented in the form of sequence of computational procedures. And the main ones are procedures for determining: the functions of differences among objects in a 2D subspace of representative features (TSRF); groups of interconnectedness pseudo-objects (GIPO) in the same subspace; a set of basic pseudo-objects; functions of differences between the basic pseudo-object in a TSRF. There are also groups of interconnectedness and basic PF; the integral recognizing operator with respect to basic PF. The results of a comparative analysis of the proposed and known RA are presented. The main conclusion is that the implementation of the approach proposed in this paper makes it possible to switch from the original BDFS to the space of representative features (RF), the dimension of which is significantly lower.
Enhanced Mixup Training: a Defense Method Against Membership Inference Attack

Membership inference attacks (MIAs) have powerful attack ability to threaten the privacy of users. In general, it mainly utilizes model-based or metric-based inference methods to infer whether a particular data sample is in the training dataset of the target model. While several defenses have been proposed to mitigate privacy risks, they still suffer from some limitations: 1) Current defenses are limited to defend model-based attack and are still vulnerable to other types of attacks. 2) They make the impractical assumption that the defender already knows adversaries’ attack strategies. In this paper, we present Enhanced Mixup Training (EMT) as a defense against MIAs. Specifically, EMT benefits from recursive mixup training, which mixes training data by using devised Enhanced Mix Item during the training process. Compared with existing defenses, EMT fundamentally improves the accuracy and generalization of the target model, and hence effectively reduces the risk of MIAs. We prove theoretically that EMT corresponds to a specific type of data-adaptive regularization which leads to better generalization. Moreover, our defense is adaptive and does not require knowing how adversaries launch attacks. Our experimental results on Location30, Purchase100 and Texas100 datasets show that our EMT successfully mitigates both model-based and metric-based attacks without the accuracy loss.
CopyrightLY: Blockchain and Semantic Web for Decentralised Copyright Management

CopyrightLY focuses on building an authorship and rights management layer for the ONTOCHAIN ecosystem. It provides a set of services to claim authorship, on both content and data. Moreover, it also makes it possible to attach reuse terms to these claims, which state the conditions to reuse the associated data or content. This authorship and rights management layer will constitute the foundation for future services built on top of the ONTOCHAIN ecosystem, like social media copyright management, media monetisation through Non-Fungible Tokens or specialised media and data marketplaces.
Face Mask Detection with Computer Vision &amp; Deep Learning

Computer vision has been in high demand due to the Coronavirus pandemic to improve healthcare sector. During this time detecting small objects is a tougher task, as it uses both classification and detection using video illustration. This Object Detection demonstrated a superior feature ie, Mask Detection compared to other object detection models. This Face mask detection using YOLOv3 performed well. This Face mask detection measures performance at the same time with strong GPU and works with less computation power We add dataset which consists of both people wearing face masks and without facemask, The model is trained by this dataset consisting of face mask and no face mask. Real time video can also be used to verify whether the person is wearing mask or not. This Face Mask Detection model attained good output with 96% classification.
Organic matter in aqueous soil extracts: Prediction of compositional attributes from bulk soil mid-IR spectra using partial least square regressions

Water-extractable organic matter (WEOM) is labile and a key component of soil organic matter. Thus, the prediction of soil WEOM concentration and composition-related characteristics is of great interest. The main objective of this study was to model and predict dissolved organic C (DOC) concentration and WEOM composition-related attributes of aqueous soil extracts using mid-infrared (IR) spectra of bulk soils coupled with partial least square (PLS) regression. Absorbance of UV light at 254 nm (Abs254), considered proportional to the concentration of aromatic substances in soil extracts, and light emission intensities proportional to concentrations of some components controlling WEOM fluorescence, were used as composition-related attributes of the soil extracts. The DOC-normalized Abs254 and emission intensities were used as composition-related attributes of WEOM. Application of PLS regressions for predicting spectroscopy-based composition-related attributes of aqueous soil extracts, using bulk soil IR spectra, is novel. Mid-IR spectra were determined for 216 soil samples collected from different (i) Israeli climate regions (Mediterranean and Semi-arid), (ii) types of land use (field crops, orchard and non-cultivated land), (iii) two depths (0–10 and 30–60 cm), (iv) sampling seasons (Fall and Spring). Prediction of DOC concentrations in the soil extracts was of limited and variable success evaluated by the coefficient of determination and slope of the linear regression of predicted vs measured values, with some soil subsets yielding no satisfactory predictions. However, Abs254 and emission intensities of fluorescent humic-like components were predicted more successfully than DOC concentrations, suggesting that WEOM aromatic and fluorescent components are better presented in soil IR spectra compared to WEOM aliphatic substances. Yet, prediction of the specific UV absorbance (SUVA), using bulk soil IR spectra, was less successful than prediction of Abs254. Prediction of DOC-normalized emission intensities of fluorescent components (analogous to SUVA in fluorescence spectroscopy) was not successful. The differences between the success of predicting extract properties, i.e., Abs254 and fluorescence emission intensities, and the prediction of DOC-normalized derivatives, suggest that concentrations of aromatic (fluorescent) components in soil extracts are better predicted using PLS regression analysis of bulk soil mid-IR spectra than the WEOM composition.
Swarm Intelligence in Cooperative Environments: Introducing the N-Step Dynamic Tree Search Algorithm

Uncertainty and partial or unknown information about environment dynamics have led reward-based methods to play a key role in the Single-Agent and Multi-Agent Learning problem. Tree-based planning approaches such as Monte Carlo Tree Search algorithm have been a striking success in single-agent domains where a perfect simulator model is available, e.g., Go and chess strategic board games. This paper presents a decentralized tree-based planning scheme, that combines forward planning with direct reinforcement learning temporal-difference updates applied to the multi-agent setting. Forward planning requires an engine model which is learned from experience and represented via function approximation. Evaluation and validation are carried out in the Hunter-Prey Pursuit cooperative environment and performance is compared with state-of-the-art RL techniques. N-Step Dynamic Tree Search (NSDTS) pretends to adapt the most successful single-agent learning methods to the multi-agent boundaries in a decentralized system structure, dealing with scalability issues and exponential growth of computational resources suffered by centralized systems. NSDTS demonstrates to be a remarkable advance compared to the conventional Q-Learning temporal-difference method.
Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.
Attention‐guided transformation‐invariant attack for black‐box adversarial examples

With the development of media convergence, information acquisition is no longer limited to traditional media, such as newspapers and televisions, but more from digital media on the Internet, where media contents should be under supervision by platforms. At present, the media content analysis technology of Internet platforms relies on deep neural networks (DNNs). However, DNNs show vulnerability to adversarial examples, which results in security risks. Therefore, it is necessary to adequately study the internal mechanism of adversarial examples to build more effective supervision models. When coming to practical applications, supervision models are mostly faced with black-box attacks, where cross-model transferability of adversarial examples has attracted increasing attention. In this paper, to improve the transferability of adversarial examples, we propose an attention-guided transformation-invariant adversarial attack method, which incorporates an attention mechanism to disrupt the most distinctive features and simultaneously ensures adversarial attack invariance under different transformations. Specifically, we dynamically weight the latent features according to an attention mechanism and disrupt them accordingly. Meanwhile, considering the lack of semantics in low-level features, high-level semantics are introduced as spatial guidance to make low-level feature perturbations concentrate on the most discriminative regions. Moreover, since the attention heatmaps may vary significantly across different models, a transformation-invariant aggregated attack strategy is proposed to alleviate overfitting to the proxy model attention. Comprehensive experimental results show that the proposed method can significantly improve the transferability of adversarial examples.
Unsupervised domain adaptation for vibration-based robotic ground classification in dynamic environments

Vibration-based Robotic Ground Classification (V-RGC) is an ability of a field robot identifying the ground types (e.g., grass and clay) according to the proprioceptive vibration sequence. It has proved that the accurate and read-time V-RGC contributes a lot to the avoidance of non-geometric hazards, route planning, and pose estimation. However, V-RGC in a dynamic environment (i.e., data distribution may drift) has not yet been taken into consideration, which motivates us to propose a novel classification method named Joint Domain Adaptation Semi-Supervised Extreme Learning Machine (JDA-S2ELM). First, the projected maximum mean discrepancy (MMD) criterion is introduced to expand the classification boundaries from the source domain to the target domain in a computationally-efficient way. Second, the joint-distribution domain adaptation (DA) is proposed to realize a cascaded marginal- and conditional-distribution DA training framework, which shows a higher and more stable accuracy. Third, target-domain manifold regularization is added to smooth the classification boundaries to cut through low-density regions, thus further increasing the target-domain classification accuracy. The real-world experiment demonstrates that the proposed JDA-S2ELM could increase the target-domain accuracy from about 30% to 90%, which means that V-RGC is adaptable to a dynamic environment.
Reflection, rewinding, and coin-toss in EasyCrypt

In this paper we derive a suite of lemmas which allows users to internally reflect EasyCrypt programs into distributions which correspond to their denotational semantics (probabilistic reflection). Based on this we develop techniques for reasoning about rewinding of adversaries in EasyCrypt. (A widely used technique in cryptology.) We use our reflection and rewindability results to prove the security of a coin-toss protocol.
Temporally-aware Convolutional Block Attention Module for Video Text Detection

Scene text in video carries rich semantic information that is of great value in various content-based video applications. Existing methods have been proposed to improve accuracy, such as combining tracking; however, many have complex structures and are difficult to execute in real-time. Therefore, to run in real-time, this paper proposes a simple and practical feature refinement module, named Temporally-aware Convolutional Block Attention Module (TCBAM), based on a novel self-attention recurrent neural network. The model exploits still-image-based feature maps to refine temporal constant feature maps for better capturing widely varied appearances of video text. For better generalization, we also provide the flow-based data augmentation method with artificial data. Explements on the scene text video datasets including ICDAR2013 Video, Minetto, and RoadText-1K demonstrate that the proposed methods perform the competitive accuracy to the state-of-the-art models within real-time running. Our method with ResNext-50 can run at 17 FPS with 73.11 F-score on ICDAR 2013 Video without complex tracking methods.
High-Performance Image Acquisition and Processing for Stereoscopic Diagnostic Systems with the Application of Graphical Processing Units

In recent years, cinematography and other digital content creators have been eagerly turning to Three-Dimensional (3D) imaging technology. The creators of movies, games, and augmented reality applications are aware of this technology's advantages, possibilities, and new means of expression. The development of electronic and IT technologies enables the achievement of a better and better quality of the recorded 3D image and many possibilities for its correction and modification in post-production. However, preparing a correct 3D image that does not cause perception problems for the viewer is still a complex and demanding task. Therefore, planning and then ensuring the correct parameters and quality of the recorded 3D video is essential. Despite better post-production techniques, fixing errors in a captured image can be difficult, time consuming, and sometimes impossible. The detection of errors typical for stereo vision related to the depth of the image (e.g., depth budget violation, stereoscopic window violation) during the recording allows for their correction already on the film set, e.g., by different scene layouts and/or different camera configurations. The paper presents a prototype of an independent, non-invasive diagnostic system that supports the film crew in the process of calibrating stereoscopic cameras, as well as analysing the 3D depth while working on a film set. The system acquires full HD video streams from professional cameras using Serial Digital Interface (SDI), synchronises them, and estimates and analyses the disparity map. Objective depth analysis using computer tools while recording scenes allows stereographers to immediately spot errors in the 3D image, primarily related to the violation of the viewing comfort zone. The paper also describes an efficient method of analysing a 3D video using Graphics Processing Unit (GPU). The main steps of the proposed solution are uncalibrated rectification and disparity map estimation. The algorithms selected and implemented for the needs of this system do not require knowledge of intrinsic and extrinsic camera parameters. Thus, they can be used in non-cooperative environments, such as a film set, where the camera configuration often changes. Both of them are implemented with the use of a GPU to improve the data processing efficiency. The paper presents the evaluation results of the algorithms' accuracy, as well as the comparison of the performance of two implementations-with and without the GPU acceleration. The application of the described GPU-based method makes the system efficient and easy to use. The system can process a video stream with full HD resolution at a speed of several frames per second.
Semi-Supervised Learning: Structure, Reflectance and Lighting Estimation From a Night Image Pair

While unsupervised approaches have been proposed to estimate reflectance and shading layers for images, the decomposition process is a challenging, under-determined inverse problem. Previous unsupervised approaches for intrinsic decomposition of images are strictly reliant on special image sequences, for example, image sequences for a fixed scene with changing illumination or multi-view images containing rich illumination variation. As an alternative to these unsupervised intrinsic decomposition methods, we propose a semi-supervised method. Our method adopts a simplified scene representation to greatly reduce the complexity of spatially varying lighting, which allows us to partially restore the lighting from a night image pair. Moreover, we design several novel unsupervised guiding losses, and the training data used in our method are easily collected by setting different exposure times for a standard stereo setup. We further demonstrate the effectiveness of the proposed method by quantitatively and qualitatively comparing our method with recent works on the modified MPI dataset and a collected highly dynamic dataset named NightCampus. The NightCampus dataset has been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kewangtt/srlefnip</uri> .
Concordant Contrastive Learning for Semi-supervised Node Classification on Graph

Semi-supervised object classification has been a fundamental problem in relational data modeling recently. The problem has been extensively studied in the literature of graph neural networks (GNNs). Based on the homophily assumption, GNNs smooth the features of the adjacent nodes, resulting in hybrid class distributions in the feature space when the labeled nodes are scarce. Besides, the existing methods inherently suffer from the non-robustness, due to the deterministic propagation. To address the above two limitations, we propose a novel method Concordant Contrastive Learning (CCL) for semi-supervised node classification on graph. Specifically, we generate two group data augmentations by randomly masking node features and separately perform node feature propagation with low- and high-order graph topology information. Further, we design two granularity regularization losses. The coarse-grained regularization loss (i.e., center-level contrastive loss) preserves the identity of each class against the rest, which benefits to guide the discriminative class distributions. The fine-grained regularization loss (i.e., instance-level contrastive loss) enforces consistency between soft assignments for different augmentations of the same node. Extensive experiments on different benchmark datasets imply that CCL significantly outperforms a wide range of state-of-the-art baselines on the task of semi-supervised node classification.
Conceptual Design and Analysis of a Fixed Wing Mini Unmanned Aerial Vehicle for Humanitarian Assistance Operations

Application of Unmanned Aerial Vehicles (UAVs) in civilian and humanitarian roles is evolving these days. M/s Zipline, a US based company has recently demonstrated use of a fixed wing mini UAV for medical logistics. The publically available data of Zipline UAV suggests that the present capability is limited to 1.5 Kg of medical supplies delivered using parachute mechanism. The proposed research question is whether the current payload capacity of 1.5 Kg can be increased by undertaking UAV design process on the lines of traditional aircraft conceptual design process. Therefore, conceptual UAV design process on the lines of aircraft conventional design process is undertaken. Various tools such as MS Excel, VSP 3.0, XFLR 5, CATIA V5 and ANSYS 16 are used during various stages of the conceptual design process.
Partial Label Learning Based on Disambiguation Correction Net With Graph Representation

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. This learning method is dedicated to finding out the true label for each training instance. Most of the current PLL algorithms directly disambiguate the candidate labels without correcting the disambiguated results, making the algorithms vulnerable to the influence of instances easily misjudged. In this paper, GraphDCN, an innovative disambiguation correction net with inductive graph representation learning model is proposed. GraphDCN consists of a disambiguation model and a correction model. For a given instance, the disambiguation model tries to fit its underlying ground-truth label through the candidate label distribution of the instances connected with the given one, while the correction model tries to maximize the distance between the disambiguated labels and non-candidate labels, and uses the label probability thresholds to correct the disambiguated labels that may be wrong. As the training goes on, both the disambiguation and correction models alternately and iteratively boost their performance. Moreover, when considering the implementation of the disambiguation model, a partial cross entropy formulation is proposed to estimate the ground-truth label loss by updating the ambiguity confidence matrix, which can be proved satisfying convergence in PLL. Simulation results reveal the overwhelming performance of GraphDCN.
Deep Metric Learning for Human Action Recognition with SlowFast Networks

In this paper, we propose deep metric learning for human action recognition with SlowFast networks. We adopt SlowFast Networks to extract slow-changing spatial semantic information of a single target entity in the spatial domain with fast-changing motion information in the temporal domain. Since deep metric learning is able to learn the class difference between human actions, we utilize deep metric learning to learn a mapping from the original video to the compact features in the embedding space. The proposed network consists of three main parts: 1) two branches independently operating at low and high frame rates to extract spatial and temporal features; 2) feature fusion of the two branches; 3) joint training network of deep metric learning and classification loss. Experimental results on the KTH human action dataset demonstrate that the proposed method achieves faster runtime with less model size than C3D and R3D, while ensuring high accuracy.