Reviewerly/exHarmony huggingface dataset:
Protection of data using image watermarking technique

Digital watermarking is one of the techniques used for copyright protection as well as authentication purpose. In this paper, digital watermark embedding and extraction techniques have been used in which a nested type of watermarking (a watermark inside another watermark) has been presented. The main purpose of using the nested watermarking method is, it increases the embedding capacity so that a large amount of information could be embedded. In this method, one watermark is embedded into another watermark. The resultant watermark is considered as main watermark. The main watermark is encrypted and then embedded into the main image. The main goal in encrypting the watermarks before embedding is increased safety. A5/1 encryption algorithm is used for the encryption and decryption purpose. Therefore, our research work focuses on two important things i.e., increased watermark embedding capacity and increased safety.
QSDC: Future of Quantum Communication A Study

Quantum Secure Direct Communication (QSDC) is a distinctive protocol of quantum communication. It communicates confidential message straight through a quantum channel without a requirement of secret key and cipher-text. It is not solely useful in preventing eavesdropping throughout transmission, but additionally removes the security flaw related with storing and managing the key. Therefore, this protocol of secure communication is different from its typical equivalents and other quantum protocols. In this article, we will bring out the facts that how QSDC is different and better than other protocols of Quantum Communication. The transient details of all the protocols of Quantum communication are studied rigorously and compared with QSDC to demonstrate how QSDC is capable of overcoming the loopholes/shortcomings of alternative protocols. In this paper, protocols of QSDC to mitigate the possible threats available in practical QSDC are discussed and one of the protocol Measurement Device Independent QSDC is demonstrated along with the efficacy of results verified using quantum simulator and quantum machine. The results demonstrate its potential over other protocols of quantum communication and contribution towards empowering secure communication. With the benefits of QSDC over other protocols, it helps as mitigating technique for the possible threats available in other protocols of Quantum communication.
A baseline for semi-supervised learning of efficient semantic segmentation models

Semi-supervised learning is especially interesting in the dense prediction context due to high cost of pixel-level ground truth. Unfortunately, most such approaches are evaluated on outdated architectures which hamper research due to very slow training and high requirements on GPU RAM. We address this concern by presenting a simple and effective baseline which works very well both on standard and efficient architectures. Our baseline is based on one-way consistency and nonlinear geometric and photometric perturbations. We show advantage of perturbing only the student branch and present a plausible explanation of such behaviour. Experiments on Cityscapes and CIFAR-10 demonstrate competitive performance with respect to prior work.
Virtual Reality Application for Laparoscope in Clinical Surgery Based on Siamese Network and Census Transformation

Virtual reality technology is now gradually being used in the field of clinical medicine. This paper presents a method of reconstruction abdominal tissue structure from laparoscope images and videos. The method is designed based on ORB-SLAM and introduced a two branch Siamese Network to extract more features for dense reconstruction. The contributions of this study are as follows: (i) We introduce a data augmentation thread to augment data and a dense reconstruction thread with GPU acceleration to get dense features without interruption of the original sparse reconstruction. (ii) We design an improved Census Transformation to reduces the effect of changes caused by camera gain and bias. For the outer point near the deep discontinuity, the robustness is improved. (iii) An experimental system was built to test the 3D reconstruction of liver, stomach, greater omentum and omental bursa from the public laparoscope datasets. (iv) Introducing the presented method to clinical surgery virtual reality system. This method achieved 1.2 ± 0.8 RMSE reconstruction accuracy in low time cost. Lens distortion has been considered to get more accurate feature detection and matching support for operation scenario application effectively. Compared with the current mainstream algorithm, it demonstrates the practicability and superiority of abdominal tissue images 3D reconstruction by laparoscope in clinical scenarios, such as surgical navigation, auxiliary diagnosis, surgery simulation, etc.
Developing reliably distinguishable color schemes for legends of natural resource taxonomy-based maps

The legends of natural resource taxonomy-based maps (e.g. soil, geological, geomorphological, vegetation, and land cover/land use) need many different distinguishable colors. The existing methods of color selection for map legends are based on the designer subjectivity, ensuring schemes having few colors. An analysis of the modeling and management of colors in digital applications has led to define an algorithm to calculate an objective colorimetric measure of color difference – "DE*ab" – based on the perceptually uniform color model CIELAB. The proposed method consists of a set of specific rules for developing hierarchically structured color schemes and a specific procedure for ensuring selection of a large number of reliably distinguishable colors, based on a color difference threshold. The accuracy of color reproduction in printing processes is also taken into account. The method has been applied to develop a standard of colors for soil maps. It contains 63 colors and has been used for developing a soil map having 41 standard colors. A user test of the method results proved that thresholds of 10 DE*ab units and 15 DE*ab units ensure obtaining acceptably distinguishable colors for displaying/printing maps by using high-quality, respectively, current devices. Three datasets that support the research are given.
Socially robotic: making useless machines

Abstract As robots increasingly become part of our everyday lives, questions arise with regards to how to approach them and how to understand them in social contexts. The Western history of human–robot relations revolves around competition and control, which restricts our ability to relate to machines in other ways. In this study, we take a relational approach to explore different manners of socializing with robots, especially those that exceed an instrumental approach. The nonhuman subjects of this study are built to explore non-purposeful behavior, in an attempt to break away from the assumptions of utility that underlie the hegemonic human–machine interactions. This breakaway is accompanied by ‘learning to be attuned’ on the side of the human subjects, which is facilitated by continuous relations at the level of everyday life. Our paper highlights this ground for the emergence of meanings and questions that could not be subsumed by frameworks of control and domination. The research-creation project Machine Ménagerie serves as a case study for these ideas, demonstrating a relational approach in which the designer and the machines co-constitute each other through sustained interactions, becoming attuned to one another through the performance of research. Machine Ménagerie attempts to produce affective and playful—if not unruly—nonhuman entities that invite interaction yet have no intention of serving human social or physical needs. We diverge from other social robotics research by creating machines that do not attempt to mimic human social behaviours.
Direct domain adaptation through reciprocal linear transformations

We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear operations on the input features to the NN model, whether they are from the source or target distributions, as follows: (1) A cross-correlation of the input data (i.e., images) with a randomly picked sample pixel (or pixels) of all images from the input or the mean of all randomly picked sample pixel (or pixels) of all input images. (2) The convolution of the resulting data with the mean of the autocorrelated input images from the other domain. In the training stage, as expected, the input images are from the source distribution, and the mean of auto-correlated images are evaluated from the target distribution. In the inference/application stage, the input images are from the target distribution, and the mean of auto-correlated images are evaluated from the source distribution. The proposed method only manipulates the data from the source and target domains and does not explicitly interfere with the training workflow and network architecture. An application that includes training a convolutional neural network on the MNIST dataset and testing the network on the MNIST-M dataset achieves a 70% accuracy on the test data. A principal component analysis (PCA), as well as t-SNE, shows that the input features from the source and target domains, after the proposed direct transformations, share similar properties along the principal components as compared to the original MNIST and MNIST-M input features.
Road Detection Network Based on Anti-Disturbance and Variable-Scale Spatial Context Detector

Road detection plays a critical role in the application of smart transportation. The performance of the mainstream methods such as PSPNet (Pyramid Scene Parsing Network), DeepLab V3, FCRN (Fully Convolutional Residual Networks) still suffers from uncertain disturbances of surface abrasion buildings, pedestrians, and variation of illumination like tree-shadow. The extracted features are vulnerable to extra-disturbance, and non-local spatial-context information has not been fully utilized. In this paper, a detector based on anti-disturbance and variable-scale spatial context features (AVD) is proposed: the training of the multi-layer features of the detector is always taken under the imposing of fake-feature-disturbance from an independent generator, which is trained to exacerbate the detector errors and the mistakes of feature discriminator. The detector is prepared to be immune from the fake-feature-disturbance, and the discriminator is trained to distinguish the differences between the non-interference features and disturbing features. We also designed a novel variable-scale spatial context module to enhance the richness performance of the extracted features. And a soft connection link is bridged between the low and high feature layers. The detection experiments on the Munich road dataset and urban road dataset show that AVD is better than all the mainstream above methods. Our method increases the accuracy by 3% on the Munich remote sensing dataset and 0.4% on the urban road dataset. Our code and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ding-Q/AVD</uri> for download.
A New Content-Based Image Retrieval System for SARS-CoV-2 Computer-Aided Diagnosis

Medical images are an essential input for the timely diagnosis of pathologies. Despite its wide use in the area, searching for images that can reveal valuable information to support decision-making is difficult and expensive. However, the possibilities that open when making large repositories of images available for search by content are unsuspected. We designed a content-based image retrieval system for medical imaging, which reduces the gap between access to information and the availability of useful repositories to meet these needs. The system operates on the principle of query-by-example, in which users provide medical images, and the system displays a set of related images. Unlike metadata match-driven searches, our system drives content-based search. This allows the system to conduct searches on repositories of medical images that do not necessarily have complete and curated metadata. We explore our system's feasibility in computational tomography (CT) slices for SARS-CoV-2 infection (COVID-19), showing that our proposal obtains promising results, advantageously comparing it with other search methods.
Practitioners’ Views on Cybersecurity Control Adoption and Effectiveness

Cybersecurity practitioners working in organisations implement risk controls aiming to improve the security of their systems. Determining prioritisation of the deployment of controls and understanding their likely impact on overall cybersecurity posture is challenging, yet without this understanding there is a risk of implementing inefficient or even harmful security practices. There is a critical need to comprehend the value of controls in reducing cyber-risk exposure in various organisational contexts, and the factors affecting their usage. Such information is important for research into cybersecurity risk and defences, for supporting cybersecurity decisions within organisations, and for external parties guiding cybersecurity practice such as standards bodies and cyber-insurance companies.
Blind Unmixing of Hyperspectral Images Based on <i>L</i> <sub>1</sub> Norm and Tucker Tensor Decomposition

Most of the traditional hyperspectral unmixing methods are based on the matrix and often ignore the spatial information of hyperspectral images (HSIs). In recent years, tensor-based methods have been gradually used in hyperspectral unmixing, owing to their ability to completely preserve the real spatial structure of HSIs. A blind unmixing method for HSIs based on an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regular term and tucker tensor decomposition (BUTTDL1) is proposed, which describes the low rank of abundance by tucker tensor decomposition in the form of a third-order tensor and increases the sparse characterization of abundance by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> norm. A comparative experiment is performed on two simulation datasets. Compared with the latest method unmixing with low-rank tensor regularization algorithm accounting for endmembers variability (ULTRA-V), in the simulation dataset Data Cube 1 (DC1), the endmember mean square error (MSE) of BUTTDL1 is decreased by 1.1, and the abundance MSE is decreased by 8.6. In the simulation dataset DC2, the endmember MSE is decreased by 2.4, and the abundance MSE is decreased by 6.63.
Elements of Conceptual Thinking in Planning the Behavior of Autonomous Intelligent Agents

The expediency of using the tools of visual-effective, visual-figurative and conceptual thinking for planning the purposeful activity of autonomous intelligent agents in problem environments of various degrees of a priori uncertainty has been substantiated. The content is revealed and the role of each form of thinking is shown in the process of automatic planning of the purposeful behavior of autonomous intelligent agents in the changing conditions of functioning. The special role of conceptual thinking in the performance of complex tasks by autonomous agents and the planning of polyphasic behavior associated with it is indicated. Taking into account the complexity of the problems associated with the formalization of mental acts of conceptual thinking, possible ways of its gradual development from the initial level to the transition to higher levels of development are shown, expanding on this basis the class of tasks solved by autonomous intelligent agents. A model of knowledge representation and tools for deriving solutions of the initial level of conceptual thinking have been developed, which allow intelligent agents to break down the tasks they receive into sub-goals of behavior. Then, on this basis, plan polyphase activity by searching for solutions to the associated subtasks, which ensure the determination of the minimum length routes of movement in a prob lematic environment with obstacles and the purposeful manipulation of objects in it. The tools are synthesized allowing to establish the order of elaboration of complex actions included in the structure of the task formulated by autonomous intelligent agents. It is shown that the further development of the proposed methodological foundations for constructing intelligent problem sol vers is associated with the formalization of a higher level of mental acts of conceptual thinking, which make it possible to solve practical problems of different complexity, formulated both in procedural and declarative form of presentation in the form of various target situations of the problem environment, having a large dimension.
Towards better subtitles: A multilingual approach for punctuation restoration of speech transcripts

This paper proposes a flexible approach for punctuation prediction that can be used to produce state-of-the-art results in a multilingual scenario.We have performed experiments using transcripts of TED Talks from the IWSLT 2017 and IWSLT 2011 evaluation campaigns.Our experiments show that the recognition errors of the ASR output degrade the performance of our models, in line with related literature.Our monolingual models perform consistently in Humanedited transcripts of German, Dutch, Portuguese and Romanian, suggesting that commas may be more difficult to predict than periods, using pre-trained contextual models.We have trained a single multilingual model that predicts punctuation in multiple languages that achieves results comparable with the ones achieved by monolingual models, revealing evidence of the potential of using a single multilingual model to solve the task for multiple languages.Then, we argue that usage of current punctuation systems in the literature are implicitly dependent on correct segmentation of ASR outputs for they rely on positional information to solve the punctuation task.This is too big of a requirement for use in a real life application.Through several experiments, we show that our method to train and test models is more robust to different segmentation.These contributions are of particular importance in our multilingual pipeline, since they avoid training a different model for each of the involved languages, and they guarantee that the model will be more robust to incorrect segmentation of the ASR outputs in comparison with other methods in the literature.To the best of our knowledge, we report the first experiments using a single multilingual model for punctuation restoration in multiple languages.
Action Segmentation on Representations of Skeleton Sequences Using Transformer Networks

We propose an approach for action segmentation by representing motions as images. A transformer object detection network is used to segment the sequences from the representation images. We examine different encoding approaches, normalization strategies and skeleton joint orders in an extensive experiment study. Our approach is evaluated on skeleton sequences from the PKU-MMD dataset. We successfully apply transformer networks for action segmentation on skeleton sequences. Our proposed approach achieves high class accuracies while start and end-time estimation of the action segments are subject to further improvement.
Dimensionality reduction in the context of dynamic social media data streams

Abstract In recent years social media became an important part of everyday life for many people. A big challenge of social media is, to find posts, that are interesting for the user. Many social networks like Twitter handle this problem with so-called hashtags. A user can label his own Tweet (post) with a hashtag, while other users can search for posts containing a specified hashtag. But what about finding posts which are not labeled by the creator? We provide a way of completing hashtags for unlabeled posts using classification on a novel real-world Twitter data stream. New posts will be created every second, thus this context fits perfectly for non-stationary data analysis. Our goal is to show, how labels (hashtags) of social media posts can be predicted by stream classifiers. In particular, we employ random projection (RP) as a preprocessing step in calculating streaming models. Also, we provide a novel real-world data set for streaming analysis called NSDQ with a comprehensive data description. We show that this dataset is a real challenge for state-of-the-art stream classifiers. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper, we provide a use case of RP on real-world streaming data, especially on NSDQ dataset. We discuss why RP can be used in this scenario and how it can handle stream-specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art stream classifiers like adaptive random forest and concept drift detectors. Additionally, we experimentally evaluate an online principal component analysis (PCA) approach in the same fashion as we do for RP. To obtain higher dimensional synthetic streams, we use random Fourier features (RFF) in an online manner which allows us, to increase the number of dimensions of low dimensional streams.
Non-local optimization

In stochastic optimization, particularly in evolutionary computation and reinforcement learning, the optimization of a function f : Ω → R is often addressed through optimizing a so-called relaxation θ ϵ Θ → Eθ(f) of f, where Θ resembles the parameters of a family of probability measures on Ω. We investigate the structure of such relaxations by means of measure theory and Fourier analysis, enabling us to shed light on the success of many associated stochastic optimization methods. The main structural traits we derive and that allow fast and reliable optimization of relaxations are the consistency of optimal values of f, Lipschitzness of gradients, and convexity. We emphasize settings where f itself is not differentiable or convex, e.g., in the presence of (stochastic) disturbance.
Deep truth discovery for pattern-based fact extraction

Fact extraction, which aims to extract (entity, attribute, value)-tuples from massive text corpora, is crucial in the area of text data mining. Recent approaches have focused on extracting facts by mining textual patterns with semantic types, where the quality of a pattern is evaluated based on content-based criteria, such as frequency. However, these approaches overlook the dimension of pattern reliability, which reflects how likely the extracted facts are correct. As a result, a pattern of good content-quality (e.g., high frequency) may still extract incorrect facts. In this study, we consider both pattern reliability and fact trustworthiness in addressing the pattern-based fact extraction problem. To learn the complex relationship between pattern reliability and fact trustworthiness, we propose a novel deep learning model using a hybrid of the CNN and LSTM architecture. For fact embedding, we adopt CNN to extract a fix-sized representation of each component, i.e., entity, attribute, and value, of the fact. For pattern embedding, we represent the pattern as a semantic composition of its extracted fact representations. To de-emphasis the noisy facts, we consider the fact trustworthiness and frequency during the process of pattern embedding, where the features of the tuple trustworthiness information are extracted by a long short-term memory (LSTM) model. To learn the pattern-fact relational dependency, we train the model with both pattern and tuple labels. Extensive experiments involving three real-world datasets demonstrated that the proposed model significantly improves the quality of the patterns and the extracted facts in the pattern-based information extraction.