Reviewerly/exHarmony huggingface dataset:
A Survey on Named Entity Recognition Solutions Applied for Cybersecurity-Related Text Processing

Named entity recognition (NER) is one of the most common Natural Language Processing (NLP) tasks. As nowadays large quantities of unstructured data are produced, the organizations have begun to be more interested in NER solutions. In the first part of this article, we describe the evolution of NER, and we discuss the most common NER approaches. Later, we address the state-of-the-art NER machine learning solutions. We focus both on open-source and commercial solutions. The most important solutions are identified and compared based on a methodology proposed by the authors. Since the authors are involved in using NER on cybersecurity-related text, the study focuses mainly on NER aspects related to cybersecurity domain. Nevertheless, this survey has a general nature, and therefore, our conclusions can be useful as well for those interested in using NER solutions in other domains.
Multi-Mode Intra Prediction for Learning-Based Image Compression

In recent years image compression techniques based on deep learning have achieved great success and their performances are gradually reaching the methods crafted by experts, such as JPEG, WebP, and Better Portable Graphics (BPG). A technique that is fundamental for modern image and video codecs is intra prediction, which takes advantage of local redundancy to predict the pixels from previously encoded neighbors. In this paper, we use Convolutional Neural Networks (CNN) to develop a new intra-picture prediction mode. More specifically, we propose a multi-mode intra prediction approach that uses two CNN-based prediction modes and all intra modes previously implemented in the High Efficiency Video Coding (HEVC) standard. We also propose a bit allocation technique that increases the bitstream only if the reconstruction error is significantly reduced. Experimental results evince a significant and consistent performance increase compared to other approaches that use a similar backbone architecture, with 28% bitrate reduction compared to the baseline codec.
Multiscale Adaptation Fusion Networks for Depth Completion

Depth completion is becoming a particularly important yet challenging problem with the growingly rapid progress of depth sensing technologies. Depth completion aims to complete sparse and noisy depth images to generate dense depth images. In this paper, we propose a multiscale adaptation fusion network (MAFN) for depth completion. The depth features are fused with RGB features at multiple scales with adaptation modules, where a neighbour attention mechanism is designed to adapt the local structures of the RGB image and the depth image. The fusion and completion process are unified under the encoder-decoder framework which is learned in an end-to-end way. By exploiting the detailed structural relationships of RGB images and depth images, our MAFN model can accurately complete and restore the invalid depth values on the sparse depth images. We test the proposed method on the challenging KITTI depth completion benchmark. The experimental results prove the effectiveness and strength of the proposed method.
Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields.
AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers.

While significant advances in deep learning has resulted in state-of-the-art performance across a large number of complex visual perception tasks, the widespread deployment of deep neural networks for TinyML applications involving on-device, low-power image recognition remains a big challenge given the complexity of deep neural networks. In this study, we introduce AttendNets, low-precision, highly compact deep neural networks tailored for on-device image recognition. More specifically, AttendNets possess deep self-attention architectures based on visual attention condensers, which extends on the recently introduced stand-alone attention condensers to improve spatial-channel selective attention. Furthermore, AttendNets have unique machine-designed macroarchitecture and microarchitecture designs achieved via a machine-driven design exploration strategy. Experimental results on ImageNet$_{50}$ benchmark dataset for the task of on-device image recognition showed that AttendNets have significantly lower architectural and computational complexity when compared to several deep neural networks in research literature designed for efficiency while achieving highest accuracies (with the smallest AttendNet achieving $\sim$7.2% higher accuracy, while requiring $\sim$3$\times$ fewer multiply-add operations, $\sim$4.17$\times$ fewer parameters, and $\sim$16.7$\times$ lower weight memory requirements than MobileNet-V1). Based on these promising results, AttendNets illustrate the effectiveness of visual attention condensers as building blocks for enabling various on-device visual perception tasks for TinyML applications.
A Gated Recurrent Unit based Echo State Network

Echo State Network (ESN) is a fast and efficient recurrent neural network with a sparsely connected reservoir and a simple linear output layer, which has been widely used for real-world prediction problems. However, the capability of the ESN of handling complex nonlinear problems is limited by the relatively simple neuronal dynamics in the reservoir. Although the gated recurrent unit (GRU) model with multiple nonlinear operators has achieved an excellent performance, gradient-based training algorithms usually require intensive computational resources. In this paper, we present a novel ESN model based on GRUs to tackle complex real-world tasks while reducing the computational costs, taking advantage of the characteristics of both the ESN and the GRU models. In the proposed model, the reservoir unit is replaced by the sparsely connected GRU neurons. Experimental results on three regression problems demonstrate that the proposed method performs better than the original ESN and GRU models.
Compressive Sensed Video Coding Having Jpeg Compatibility

This paper studies the problem of video coding based on compressive sensing (CS) framework. We propose a new approach based on global sensing model applied to a residual frame computed as a difference between an input frame and its downsampled and upsampled version. The downsampled version of the frame is compressed via JPEG baseline making the codec compatible with JPEG. The sensed measurements are quantized, subsampled using a predefined look-up table, and encoded via context-adaptive binary range coder. The resulting bit stream is divided into network abstract layer packets which are embedded into an application part of the JPEG header. Experimental results show that the proposed approach outperforms existing algorithms based on CS in rate-distortion performance and generates a bit stream, which is significantly more robust to packet losses compared to conventional codecs.
Measurement Dependence Inducing Latent Causal Models

We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theoretic problem of finding edge clique covers,resulting in an algorithm for returning minimal MeDIL causal models (minMCMs). This algorithm is non-parametric, requiring no assumptions about linearity or Gaussianity. Furthermore, despite rather weak assumptions aboutthe class of MeDIL causal models, we show that minimality in minMCMs implies some rather specific and interesting properties. By establishing MeDIL causal models as a semantics for edge clique covers, we also provide a starting point for future work further connecting causal structure learning to developments in graph theory and network science.
Self-sovereign Identity as Trusted Root in Knowledge Based Systems

In this paper we continue to speak about the concept of Self-Sovereign Identity (SSI), but not in the cases of IoT devices as it was in previous works []. The main purpose of this research is the usage of digital identity in two cases: a) SSI of a single person in Knowledge based system “Experts Ledger” and b) SSI of a company and candidate in HR matching systems. Though these two systems are developed for different issues, the idea of SSI in both is similar. The overview of these systems is done, and the pros and cons of using SSI with the relation of Zero-Knowledge Proof (ZKP) in each of them is made.
Semi-supervised learning, causality, and the conditional cluster assumption

While the success of semi-supervised learning (SSL) is still not fully understood, Scholkopf et al. (2012) have established a link to the principle of independent causal mechanisms. They conclude that SSL should be impossible when predicting a target variable from its causes, but possible when predicting it from its effects. Since both these cases are somewhat restrictive, we extend their work by considering classification using cause and effect features at the same time, such as predicting disease from both risk factors and symptoms. While standard SSL exploits information contained in the marginal distribution of all inputs (to improve the estimate of the conditional distribution of the target given inputs), we argue that in our more general setting we should use information in the conditional distribution of effect features given causal features. We explore how this insight generalises the previous understanding, and how it relates to and can be exploited algorithmically for SSL.
Mask R-CNN for Instance Segmentation of Water Bodies from Satellite Image

To acquire information about the land, data from the remote sensing satellites is used. Object detection which is the task of detecting objects in an image is used here. The challenges faced in object detection include non-trivial noisiness, blurring, and lower resolutions compared to aerial images. The objects may overlap and have different textures, or alternately, adjacent objects may have similar texture, which can prevent identification of contours separating them. This task can either be carried out by a domain expert (manually) or automatically by employing machine learning. Machine learning techniques if implemented properly may give results better than a human and can also reduce the expenditure to a very large extent. A polygon maybe used instead of a rectangle to obtain more accurate information about the object. But that’s still not the best way. Therefore, the best way would be assigning the pixels belonging to the object in a bounding box. This task is called as instance segmentation, where the object instances are segmented. This project implements instance segmentation using Mask R-CNN, a deep learning model to extract geographical features (water bodies) from satellite maps.
XR-Cockpit: a comparison of VR and AR solutions on an interactive training station

One of the most challenging aspects of the implementation of Virtual/Mixed reality training systems is the effective simulation of real-world manipulation of the physical devices included in control interfaces like buttons, sliders, levers, knobs, etc. In this paper we describe a mockup airplane cockpit (XR-Cockpit), featuring interactive components of this kind that demonstrate the feasibility of effective simulations of device manipulation using low cost hand tracking technology and gesture recognition. Based on this system, we performed a user study to compare the effectiveness of the interaction with virtual tools using different visualization solutions: immersive VR, optical and video see-through based MR. In our study, we also checked how well it is possible to perform manipulation of real objects wearing the two video see-through solutions. The analysis of the experimental results provides useful guidelines for the design of Virtual and Mixed Reality training systems involving virtual and physical actions on manipulation devices.
The Development of Leisure Sports Consumption of Urban Residents under the View of Marine Ecological Environment

Lan, Y., 2020. The development of leisure sports consumption of urban residents under the view of marine ecological environment. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 863–866. Coconut Creek (Florida), ISSN 0749-0208.With the enhancement of urban residents' health consciousness, the consumption of leisure sports is increasing year by year, and has become an important consumption of life, but we should always firmly interact with the development of ecological environment and leisure experience, and strive to promote each other. Based on the relationship between the construction of urban ecological civilization and the development of leisure sports, this paper explores the current situation and existing problems of leisure sports consumption of urban residents in China, and puts forward corresponding improvement measures in the light of the perspective of marine ecological environment, hoping to enrich the research in related fields.
Three Lessons from Accelerating Scientific Insight Discovery via Visual Querying

Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools.
The Water Living Inheritance and Management Path of Ecological Resources with Folk Sports Characteristics

Hong, X. and Pan, T.L., 2020. The water living inheritance and management path of ecological resources with folk sports characteristics. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 867–871. Coconut Creek (Florida), ISSN 0749-0208.The construction of water living state is an important strategic choice for water resources management in China. The management mode of water living resource management in China has the phenomenon of water pollution, which leads to the distortion of ecological resources with folk sports characteristics in water state inheritance. The main significance of water living heritage is to show the integrity and purity of ecological resources with sports characteristics, not just to meet the needs of modern life. The problems facing the country are water shortage and water pollution, which are becoming more and more serious and appearing at a sustained growth rate. Under the influence of water pollution, some aquatic organisms have a large number of deaths, which also affect the use of groundwater. In the daily life and industrial water use, the phenomenon of waste is more common, resulting in water shortage. In view of this phenomenon, the state and relevant departments have taken corresponding management efforts to make the water living state of inheritance and management smoother, so as to ensure that people's lives more stable.
Mixture-based estimation of entropy

The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs to be obtained from the data sample itself. A semi-parametric estimate is proposed based on a mixture model approximation of the distribution of interest. A Gaussian mixture model is used to illustrate the accuracy and versatility of the proposal, although the estimate can rely on any type of mixture. Performance of the proposed approach is assessed through a series of simulation studies. Two real-life data examples are also provided to illustrate its use.
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification

Deep neural network-based medical image classifications often use “hard” labels for training, where the probability of the correct category is 1 and those of others are 0. However, these hard targets can drive the networks over-confident about their predictions and prone to overfit the training data, affecting model generalization and adaption. Studies have shown that label smoothing and softening can improve classification performance. Nevertheless, existing approaches are either non-data-driven or limited in applicability. In this paper, we present the Class-Correlation Learning Network (CCL-Net) to learn interclass visual correlations from given training data, and produce soft labels to help with classification tasks. Instead of letting the network directly learn the desired correlations, we propose to learn them implicitly via distance metric learning of class-specific embeddings with a lightweight plugin CCL block. An intuitive loss based on a geometrical explanation of correlation is designed for bolstering learning of the interclass correlations. We further present end-to-end training of the proposed CCL block as a plugin head together with the classification backbone while generating soft labels on the fly. Our experimental results on the International Skin Imaging Collaboration 2018 dataset demonstrate effective learning of the interclass correlations from training data, as well as consistent improvements in performance upon several widely used modern network structures with the CCL block.
A Smooth Transition Algorithm for Adjacent Panoramic Viewpoints Using Matched Delaunay Triangular Patches

The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent view images are extracted, a robust matching algorithm is used to establish adjacent point pairs, and the matching triangles are formed by using the homonymous points. Then, a dynamic transition model is formed by the simultaneous linear transitions of shape and texture for each control triangle. Finally, the smooth transition between adjacent viewpoints is implemented by overlaying the dynamic transition model with the 360° panoramic walkthrough scene. Experimental results show that this method has obvious advantages in visual representation with distinct visual movement. It can realize the smooth transition between two indoor panoramic stations with arbitrary station spacing, and its execution efficiency is up to 50 frames per second. It effectively enhances the interactivity and immersion of 360° panoramic walkthrough systems.
Forensic phonetics and automatic speaker recognition

Speech technologists are making rapid advances in terms of the performance of Automatic Speaker Recognition (ASR) systems, which are designed to carry out spectral comparisons of speech recordings that are of evidential significance in criminal investigations. ASR systems yield quick, inexpensive and scientifically valid results, which the authorities in some jurisdictions see as superior to those produced by human speech experts. Many governments are therefore investing heavily in training police officers and forensic laboratory staff in the use of commercially available ASR products. However, there are aspects of human psychology and behaviour which influence the acoustic-phonetic characteristics of speech but which have hitherto attracted relatively little forensically oriented empirical research. Since forensic casework recordings so often contain speech produced by individuals under high levels of emotional arousal and/or psychological stress, it is vital – not least if we wish to keep improving ASR system performance – that we better understand the effects that these non-linguistic factors can have on the speech signal. We argue that the introduction of ASR will not necessarily replace human expert speech analysts. Instead, it is opening up new kinds of challenge for forensic speech scientists, forensic linguists and forensic psychologists, and encouraging collaborations based on their complementary expertise.
Cryptocurrency: Threat or Opportunity

Though a weak currency is a sign of a weak economy and a weak economy leads to a weak nation, here we gave an attempt to study about the cryptocurrency. Cryptocurrency is also known as the digital currency of the 21st Century which moves in the form of cryptographic codes in between people or institutions those who are connected to peer to peer networks (P2P). Blockchain Technology plays a major role in the flow of cryptographic codes among various nodes in the P2P networks to convert into Cryptocurrency in the decentralized ledger environment. This paper investigates the working of cryptocurrency and its impact on economies, especially to list the threats and opportunities to the Indian Economy. Also gave an attempt to differentiate the cryptocurrency and fiat or real currencies on various aspects in the world economy. We used descriptive cum exploratory research methodology to get the desired results in the present research work.
Improved marker coordinate recognition algorithm incorporating temporal continuity of deformation

Marker coordinate recognition (MCR) is widely used in fields such as medical character positioning, artificial intelligence control, and motion and deformation monitoring. Registration algorithms are often applied to improve the accuracy of MCR. However, external conditions such as light and temperature induce a temporal variation in the grey level during image acquisition, thereby reducing measurement accuracy. To overcome this problem, a mechanical constraint is introduced into marker point image processing, and an algorithm that incorporates the temporal continuity of deformation is proposed. A detailed derivation of this algorithm is generated, and the algorithm's performance is systematically validated through numerical and experimental tests. The results show that the proposed method yields accurate measurements accuracy but with a slight increase in computational cost. The proposed method can be used to improve deformation measurement and position accuracy.
Image and Video Restoration and Compression Artefact Removal Using a NoGAN Approach

Lossy image and video compression algorithms introduce several types of visual artefacts that reduce the visual quality of the compressed media. In this work, we report results obtained using the NoGAN training approach and adapting the popular DeOldify architecture used for colorization, for image and video compression artefact removal and restoration.