Reviewerly/exHarmony huggingface dataset:
Enhanced the Data Hiding in Geometrical image using stego-Crypto techniques with machine laerning

Steganography and Cryptography are two significant and well known procedures are accustomed to encoding with covering up of information in the machine learning (ML). Cryptography is a method of mystery correspondence while darkening the mystery correspondence is Steganography. The mystery correspondence is brought through numerous sources like picture, video & audio records. A few calculations and strategies were created based on cryptographic and Steganography possessions. These half of together Cryptography and Steganography, it utilizes LSB replacement in mathematical method of choosing pixels and mystery key cryptography in giving more security to concealing the information in ML. The quantity of pixels is chosen based on substance of information. This section clarifies the utilizing of JPEG picture as STEGO IMAGE with measurements of mathematical images as CIPHER KEY. Test outcome are checked for equal possessions of Cryptography with Steganography.
Sign Language Detection from Hand Gesture Images using Deep Multi-layered Convolution Neural Network

Automatic detection of sign language from hand gesture images is crucial nowadays. Accurate detection and classification of sign language can help people with hearing and speech disorder. In this paper, a deep multi-layered convolution neural network is proposed for this purpose. In the proposed approach, 32 convolution filters with 3 x3 kernel, LeakyReLU activation function and 2 x2 max pooling operation have been performed in the deep multi-layered CNN structure. SoftMax activation function has been used in the output layer. The proposed approach has been evaluated on a database containing both static (54000 images and 36 classes) and dynamic (49613 images and 23 classes) hand gesture images. Experimental results demonstrate the efficacy of the proposed methodology in sign language detection task.
Dataset2Vec: learning dataset meta-features

Abstract Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task. In this paper, first, we propose a meta-feature extractor called Dataset2Vec that combines the versatility of engineered dataset meta-features with the expressivity of meta-features learned by deep neural networks. Primary learning tasks or datasets are represented as hierarchical sets, i.e., as a set of sets, esp. as a set of predictor/target pairs, and then a DeepSet architecture is employed to regress meta-features on them. Second, we propose a novel auxiliary meta-learning task with abundant data called dataset similarity learning that aims to predict if two batches stem from the same dataset or different ones. In an experiment on a large-scale hyperparameter optimization task for 120 UCI datasets with varying schemas as a meta-learning task, we show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and thus demonstrate the usefulness of learned meta-features for datasets with varying schemas for the first time.
A Minimally Supervised Approach Based on Variational Autoencoders for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a crucial ability for fully autonomous robots. This letter proposes a new deep learning-based minimally supervised method for detecting anomalies in autonomous robots. We contribute a new Variational Auto-Encoder architecture able to model very long multivariate sensor logs exploiting a new incremental training method, which induces a progress-based latent space that can be used to detect anomalies both at runtime and offline. While most existing approaches are trained in a semi-supervised fashion and require big batches of nominal observations, our method is trained using unlabeled observations of a robot performing a task, containing both nominal and anomalous executions. Only a very little amount (even just one) of labeled nominal executions is then required to partition the learned latent space into nominal and anomalous regions. Experimental results show that our method outperforms state-of-the-art anomaly detectors commonly used in robotics both in terms of false positive rate and alert delay.
A Quaternion Gated Recurrent Unit Neural Network for Sensor Fusion

Recurrent Neural Networks (RNNs) are known for their ability to learn relationships within temporal sequences. Gated Recurrent Unit (GRU) networks have found use in challenging time-dependent applications such as Natural Language Processing (NLP), financial analysis and sensor fusion due to their capability to cope with the vanishing gradient problem. GRUs are also known to be more computationally efficient than their variant, the Long Short-Term Memory neural network (LSTM), due to their less complex structure and as such, are more suitable for applications requiring more efficient management of computational resources. Many of such applications require a stronger mapping of their features to further enhance the prediction accuracy. A novel Quaternion Gated Recurrent Unit (QGRU) is proposed in this paper, which leverages the internal and external dependencies within the quaternion algebra to map correlations within and across multidimensional features. The QGRU can be used to efficiently capture the inter- and intra-dependencies within multidimensional features unlike the GRU, which only captures the dependencies within the sequence. Furthermore, the performance of the proposed method is evaluated on a sensor fusion problem involving navigation in Global Navigation Satellite System (GNSS) deprived environments as well as a human activity recognition problem. The results obtained show that the QGRU produces competitive results with almost 3.7 times fewer parameters compared to the GRU. The QGRU code is available at.
Extracting knowledge from Deep Neural Networks through graph analysis

The popularity of deep learning has increased tremendously in recent years due to its ability to efficiently solve complex tasks in challenging areas such as computer vision and language processing. Despite this success, low-level neural activity reproduced by Deep Neural Networks (DNNs) generates extremely rich representations of the data. These representations are difficult to characterise and cannot be directly used to understand the decision process. In this paper we build upon our exploratory work where we introduced the concept of a co-activation graph and investigated the potential of graph analysis for explaining deep representations. The co-activation graph encodes statistical correlations between neurons’ activation values and therefore helps to characterise the relationship between pairs of neurons in the hidden layers and output classes. To confirm the validity of our findings, our experimental evaluation is extended to consider datasets and models with different levels of complexity. For each of the considered datasets we explore the co-activation graph and use graph analysis to detect similar classes, find central nodes and use graph visualisation to better interpret the outcomes of the analysis. Our results show that graph analysis can reveal important insights into how DNNs work and enable partial explainability of deep learning models.
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

Estimating the states of surrounding traffic participants stays at the core of autonomous driving. In this paper, we study a novel setting of this problem: model-free single-object tracking (SOT), which takes the object state in the first frame as input, and jointly solves state estimation and tracking in subsequent frames. The main purpose for this new setting is to break the strong limitation of the popular "detection and tracking" scheme in multi-object tracking. Moreover, we notice that shape completion by overlaying the point clouds, which is a by-product of our proposed task, not only improves the performance of state estimation but also has numerous applications. As no benchmark for this task is available so far, we construct a new dataset LiDAR-SOT and corresponding evaluation protocols based on the Waymo Open dataset [29]. We then propose an optimization-based algorithm called SOTracker involving point cloud registration, vehicle shapes, correspondence, and motion priors. Our quantitative and qualitative results prove the effectiveness of our SOTracker and reveal the challenging cases for SOT in point clouds, including the sparsity of LiDAR data, abrupt motion variation, etc. Finally, we also explore how the proposed task and algorithm may benefit other autonomous driving applications, including simulating LiDAR scans, generating motion data, and annotating optical flow. The code and protocols for our benchmark and algorithm are available at https://github.com/TuSimple/LiDAR_SOT/. A video demonstration is at https://www.youtube.com/watch?v=BpHixKs91i8.
Biomedical cross-sentence relation extraction via multihead attention and graph convolutional networks

Most biomedical information extraction efforts are focused on binary relations, there is a strong need to extract drug–gene–mutation n-ary relations among cross-sentences. In recent years, end-to-end biomedical relation extraction with sequence-based or dependency-based method has gained increasing attention. However, handling global dependencies and structural information remains challenges for sequence-based and dependency-based models. Joint exploitation of sequence and graph information may improve biomedical cross-sentence relation extraction. In this paper, we present a hybrid model for extracting biomedical relation in a cross-sentence which aims to address these problems. Our models rely on the self-attention mechanism that directly draws the global dependency relation of the sentence. Furthermore, to preserve the dependency structural information between the words that contain the syntactic dependency relations, we employ graph convolutional networks that encode the dependency structural information to guide the multihead attention to learn the dependency relation. Through extensive experiments on benchmark datasets, we demonstrated the effectiveness of our method.
New trends in quantum machine learning <sup>(a)</sup>

Here we will give a perspective on new possible interplays between Machine Learning and Quantum Physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs in physical hardware, as well as to improve existing models or devise new learning schemes in the quantum domain. Moreover, there are lots of experiments in quantum physics that do generate incredible amounts of data and machine learning would be a great tool to analyze those and make predictions, or even control the experiment itself. On top of that, data visualization techniques and other schemes borrowed from machine learning can be of great use to theoreticians to have better intuition on the structure of complex manifolds or to make predictions on theoretical models. This new research field, named as Quantum Machine Learning, is very rapidly growing since it is expected to provide huge advantages over its classical counterpart and deeper investigations are timely needed since they can be already tested on the already commercially available quantum machines.
Good Practices in the Use of Augmented Reality for the Dissemination of Architectural Heritage of Rural Areas

When it concerns dissemination, the relationship between cultural heritage and technology has not always been easy. On the one hand, the emotions involved in knowing, enjoying, and feeling a real heritage remain should not be eclipsed by the technological experience. On the other hand, technology cannot be relegated to the point where its use is so superficial as to make it irrelevant. Hence, in the search of good practices in the dissemination of cultural heritage, it is essential that interdisciplinary work teams, composed of humanists and technicians, design new experiences that try to achieve a balance between what must be shown and the way in which it is done. This paper aims to show the potential of augmented reality (AR) for the dissemination of cultural heritage, since it allows the perceiving of real remains while offering virtual reconstructions and complementary information, sounds, images, etc. A bibliometric analysis focusing on its use for the preservation of the memory of vernacular architecture is made. Then, from the understanding and interpretation of the ideas exposed in literature, a decalogue of good practices in the use of AR for architectural heritage is established considering the holistic perspectives that any dissemination initiative must have. These initiatives can attract people to rural areas and contribute to the improvement of their social and economic situation. As an example, a proof of concept of an AR experience is presented at the end of the paper.
A Theoretical and Empirical Reflection on Technology Acceptance Models for Autonomous Delivery Robots

In this work, we provide an argument and first empirical insights that existing technology acceptance models fall short when it comes to explaining spontaneous, unplanned and unsolicited encounters between humans and delivery robots on the street. Since technology acceptance models have been defined by the technology's perceived ease of use, perceived usefulness and behavioural intention to use, they are not well suited to explain acceptance in situations in which humans meet robots without any prior intention to use. Nevertheless, acceptance of delivery robots might be a driving force for safe navigation. Thus, the concept of acceptance should not be limited to its current focus on (planned) usage. In consequence, we i) expand the understanding of technology acceptance, ii) propose the concept of Existence Acceptance for autonomous systems, and iii) explore a new model for acceptance in an online study (n = 185). Theoretical considerations hint towards the relevance of existence acceptance models for autonomous systems.
Impact of Tools and Techniques for Securing Consultancy Services

In a digital world moving at a breakneck speed, consultancy services have emerged as one of the prominent resources for seeking effective, sustainable and economically viable solutions to a given crisis. The present day consu... | Find, read and cite all the research you need on Tech Science Press
Poisoning Attacks on Algorithmic Fairness

Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data. While the effects on model accuracy of such poisoning attacks have been widely studied, their potential effects on other model performance metrics remain to be evaluated. In this work, we introduce an optimization framework for poisoning attacks against algorithmic fairness, and develop a gradient-based poisoning attack aimed at introducing classification disparities among different groups in the data. We empirically show that our attack is effective not only in the white-box setting, in which the attacker has full access to the target model, but also in a more challenging black-box scenario in which the attacks are optimized against a substitute model and then transferred to the target model. We believe that our findings pave the way towards the definition of an entirely novel set of adversarial attacks targeting algorithmic fairness in different scenarios, and that investigating such vulnerabilities will help design more robust algorithms and countermeasures in the future.
Research on the construction of TCFL resource database system based on artificial intelligence

The teaching resource bank is very important for the education of linguistics. In the construction of teaching Chinese as a foreign language, we should first build a teaching resource library. Only in this way can we meet the requirements of the Ministry of education and achieve better teaching objectives. The purpose of the construction of teaching resources database is to make the teaching resources can be systematically and scientifically planned, stored in the computer in the form of data, and can be directly extracted from the computer when necessary, so as to realize the efficient utilization of resources. Based on the existing problems of teaching Chinese as a foreign language, this paper puts forward some suggestions on the construction of teaching resource database. In recent years, the rise of artificial intelligence technology provides a new idea for the establishment of teaching resource database. According to the basic idea of artificial intelligence, scientists have established a new database of teaching Chinese as a foreign language, updated and improved the original database, and established a new standard for teaching database according to the characteristics of artificial intelligence technology. A teaching resource database system based on teaching resources and artificial intelligence is established. The system is divided into data layer, logic layer and presentation layer. Artificial intelligence is used as a bridge connecting different parts. In the process of establishing the multimedia database of TCFL, we should develop in all aspects, and pave the way for the future research after laying a solid foundation of data.
Viewpoint adaptation learning with cross-view distance metric for robust vehicle re-identification

Many vehicle re-identification (Re-ID) problems require the robust recognition of vehicle instances across multiple viewpoints. Existing approaches for dealing with the vehicle re-ID problem are insufficiently robust because they cannot distinguish among vehicles of the same type nor recognize high-level representations in deep networks for identical vehicles with various views. To address these issues, this paper proposes a viewpoint adaptation network (VANet) with a cross-view distance metric for robust vehicle Re-ID. This method consists of two modules. The first module is the VANet with cross-view label smoothing regularization (CVLSR), which abstracts different levels of a vehicle’s visual patterns and subsequently integrates multi-level features. In particular, CVLSR based on color domains assigns a virtual label to the generated data to smooth image-image translation noise. Accordingly, this module supplies the viewing angle information of the training data and provides strong robust capability for vehicles across different viewpoints. The second module is the cross-view distance metric, which designs a cascaded cross-view matching approach to combine the original features with the generated ones, and thus, obtain additional supplementary viewpoint information for the multi-view matching of vehicles. Results of extensive experiments on two large scale vehicle Re-ID datasets, namely, VeRi-776 and VehiclelD demonstrate that the performance of the proposed method is robust and superior to other state-of-the-art Re-ID methods across multiple viewpoints.
Multilabel Aerial Image Classification With a Concept Attention Graph Neural Network

Compared with natural images, aerial images collected by satellite sensors/aerial cameras can provide a much larger field of view and often contain multiple objects of interest (multiple labels). There are certain limitations of existing multilabel aerial image classification methods. First, label correlations were often ignored in previous MAIC work, and thus, multilabel classifiers failed to be self-adapted. Second, existing multilabeled data sets for aerial images only cover limited images with fixed labels. Therefore, the underlying semantic correlations of labels cannot be fully included, while such correlation information is implicitly used as common knowledge by human beings. To tackle these concerns, we propose a novel multilabel classification method for aerial images. Our contributions are twofold. First, as the first attempt, label correlations are inferred from both the specific data set and ConceptNet (a popular knowledge graph for common sense). Second, based on graph neural network (GNN), we propose a novel end-to-end aerial image classification model, named the multiple label concept graph (ML-CG). ML-CG builds a concept graph to describe the semantic correlations from both the label set and the ConceptNet. We also incorporate both semantic attention and label attention in the GNN to better extract meaningful information of image labels. Compared with state-of-the-art methods, the effectiveness of the proposed method is demonstrated on both the commonly used UCM data set and a recently proposed DFC15 data set with high image resolution.
The linked legal data landscape: linking legal data across different countries

Abstract The European Union is working towards harmonizing legislation across Europe, in order to improve cross-border interchange of legal information. This goal is supported for instance via standards such as the European Law Identifier (ELI) and the European Case Law Identifier (ECLI), which provide technical specifications for Web identifiers and suggestions for vocabularies to be used to describe metadata pertaining to legal documents in a machine readable format. Notably, these ECLI and ELI metadata standards adhere to the RDF data format which forms the basis of Linked Data, and therefore have the potential to form a basis for a pan-European legal Knowledge Graph. Unfortunately, to date said specifications have only been partially adopted by EU member states. In this paper we describe a methodology to transform the existing legal information system used in Austria to such a legal knowledge graph covering different steps from modeling national specific aspects, to population, and finally the integration of legal data from other countries through linked data. We demonstrate the usefulness of this approach by exemplifying practical use cases from legal information search, which are not possible in an automated fashion so far.
Classification of Cervical Cancer Detection using Machine Learning Algorithms

Cervical cancer is one of the diseases which are most prevalent in females. This is seen when some changes occur in a woman's cervix. To further complicate the issue these cancer cells can spread to other organs such as the liver, bladder, rectum, and even lungs. Earlier detection, screening, and careful precautions have seen higher rates of recovery. Colposcopy and pap-test are two methods to obtain the cells and study the subject. The pap-smear test is more preferred over Colposcopy as it's pain-free, low-cost, and has a more distant range. A proper cell image acquisition and accurate segmentation are the key steps for the correct classification of cells. This article presents a Machine Learning (ML) classification method applied to the Herlev pap-smear image Dataset, using Support Vector Machines (SVMs). For the segmentation phase, active contour models that are driven using Gaussian Fitting Energy were utilized. These segmented images were compared with the manually annotated images that were provided by professional cytologists. The dice index was the parameter used from the comparison, which reported a match of 92%. With polynomial SVMs highest classification accuracy was 95%.