Reviewerly/exHarmony huggingface dataset:
Synthesizing human-like sketches from natural images using a conditional convolutional decoder

Humans are able to precisely communicate diverse concepts by employing sketches, a highly reduced and abstract shape based representation of visual content. We propose, for the first time, a fully convolutional end-to-end architecture that is able to synthesize human-like sketches of objects in natural images with potentially cluttered background. To enable an architecture to learn this highly abstract mapping, we employ the following key components: (1) a fully convolutional encoder-decoder structure, (2) a perceptual similarity loss function operating in an abstract feature space and (3) conditioning of the decoder on the label of the object that shall be sketched. Given the combination of these architectural concepts, we can train our structure in an end-to-end supervised fashion on a collection of sketch-image pairs. The generated sketches of our architecture can be classified with 85.6% Top-5 accuracy and we verify their visual quality via a user study. We find that deep features as a perceptual similarity metric enable image translation with large domain gaps and our findings further show that convolutional neural networks trained on image classification tasks implicitly learn to encode shape information.
Control of vehicles and robots: creating of knowledge bases for mivar decision making systems robots and vehicles

Abstract An important element in the control of machines and robots is the Mivar decision-making system based on production rules and cause-effect relationships. For example, unmanned vehicles must know the traffic laws rules in a real environment. It is shown how young scientists and students of the department “Information Processing and Control Systems” of the Bauman Moscow State Technical University successfully create production knowledge bases for mivar decision-making systems for unmanned vehicles and robotic systems. It is proposed to use actively our experience in all educational and scientific institutions where they solve the problems of controlling machines and robots.
Saliency Map-Aided Generative Adversarial Network for RAW to RGB Mapping

RAW files are widely applied in cameras and scanners as storage because they contain original optical data. Different cameras usually process the RAW files using diverse algorithms that are incompatible. To address the issue, we propose a general transformation method for cross-camera RAW to RGB mapping based on Generative Adversarial Network (GAN). Moreover, we propose a saliency map-aided data augmentation technique and the saliency maps are produced by Saliency GAN (SalGAN). Given RAW file as an input, it jointly predicts the RGB image and corresponding saliency map to enhance perceptual quality in the generated image. The proposed architecture is trained on the Zurich RAW2RGB (ZRR) dataset. Experimental results show that our method can generate more clear and visually plausible images than state-of-the-art networks.
SoK: Demystifying Stablecoins

This papers aims to provide an understandable survey of 'stablecoins'---cryptocurrencies designed to have lower volatility than Bitcoin or Ether. We distil the stability mechanisms into a set of primitives and precisely analyze why they are thought to adjust exchange rates, what assumptions they are based on, and what risks still exist. Stablecoins are interesting to the cryptocurrency community because volatility has hampered spending, increased speculation, and hindered mature lending/credit markets from forming. Our comparison includes pre-Bitcoin digital currencies like Liberty Reserve and e-gold, Ethereum's gas, and novel visualizations.
Comparison of SARSA algorithm and Temporal Difference Learning Algorithm for Robotic Path Planning for Static Obstacles

For any mobile device, the ability to navigate by its own in its environment is a very important requirement. Avoiding dangerous situations such as accidents and conditions that are not safe comes primarily and if the robot is designed to find certain places according to its application then the robot should find those places. Path planning is an important requirement for such mobile robots. For doing this the robots needs to be aware of their environment. This is possible only if robots are provided with information about the environment. In this paper, the different obstacle occurring cases are studied and simulated for SARSA and temporal difference algorithm for a 6×6 grid in MATLAB and it is found that the success rate of SARSA is much better than that of TD. It is proposed to implement the SARSA algorithm in iRobot with a mobile interface for interaction with the user.
Methods increasing inherent resistance of ECC designs against horizontal attacks

Due to the nature of applications such as critical infrastructure and the Internet of Things etc. side channel analysis attacks are becoming a serious threat. Side channel analysis attacks take advantage from the fact that the behaviour of crypto implementations can be observed and provides hints that simplify revealing keys. A new type of SCA is the so called horizontal differential SCA. In this paper we investigate two different approaches to increase the inherent resistance of our hardware accelerator for the kP operation. The first approach aims at reducing the impact of the addressing in our design by realizing a regular schedule of the addressing. In the second approach, we investigated how the formula used to implement the multiplication of GF(2n)-elements influences the results of horizontal DPA attacks against a Montgomery kP-implementation. We implemented 5 designs with different partial multipliers, i.e. based on different multiplication formulae. We used two different technologies, i.e. a 130 and a 250 nm technology, to simulate power traces for our analysis. We show that the implemented multiplication formula influences the success of horizontal attacks significantly. The combination of these two approaches leads to the most resistant design. For the 250 nm technology only 2 key candidates could be revealed with a correctness of about 70% which is a huge improvement given the fact that for the original design 7 key candidates achieved a correctness of more than 90%. For our 130 nm technology no key candidate was revealed with a correctness of more than 60%.
Loopy belief propagation based data association for extended target tracking

The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements. Recently, the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus. Different from the belief propagation based Extended Target tracking based on Belief Propagation (ET-BP) algorithm proposed in our previous work, a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper. The proposed formulation can be solved by the Loopy Belief Propagation (LBP) algorithm. Furthermore, the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy. Finally, experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency. Additionally, the convergence of the proposed algorithm is verified in the simulations.
Trends in Video Object Tracking in Surveillance: A Survey

With the passage of time, detection and tracking of the object has gained popularity in various applications. Video surveillance is among the most evolved applications of object tracking. It can be defined as process of monitoring the area with the help of camera network employed such that entire area is covered. Any unethical activity can be observed inside the control room and accordingly action can be taken. This paper expounds an extensive study of algorithms and techniques for single and multiple objects tracking specifically in the field of surveillance in recent years. A comparative study has been done and the results have been discussed. Further it has been concluded that a perfect trade-off between robustness, accuracy and computational requirements is required inside the system for real time operation.
Information behaviour of prison inmates in Malawi

The study examined the information behaviour of Mzimba prison inmates focusing on the research themes which are information needs, sources of information, and the barriers to seeking and using information of Mzimba prison inmates. A qualitative approach was used coupled with a case study design. Data were collected through focus group discussions and interviews from 12 inmates and two prison teaching staff respectively. The study found that health information, education information and spiritual information are the major information needs of Mzimba prison inmates. The study also found that the majority of respondents agreed that the six popular sources of information are their friends, teachers, radio, television, books and newspapers. However, the study concludes that Mzimba prison (library) fails to fulfil its role as a source of information and in meeting the information needs of inmates due to challenges of lack of information resources, limited time available for inmates to search for information, poor services and lack of funding for the school and library. The study recommends that the Malawi Prison Service should engage some stakeholders such as the Malawi National Library Services, National Initiative for Civic Education and Mzuzu University Library and Learning Resources Centre to support prison libraries with the provision of information resources to meet the information needs of inmates. The study further recommends that the Malawi Prison Service Command should lobby for financial support in order to hire qualified and capable librarians and teachers to manage prison libraries and schools.
A multi-branch separable convolution neural network for pedestrian attribute recognition

Video surveillance applications have made great strides in making the world a safer place. Extracting visual attributes from a scene, such as the type of shoes, the type of clothing, carrying any object or not, or wearing any accessory etc., is a challenging problem and an efficient solution holds the key to a great number of applications. In this paper, we present a multi-branch convolutional neural network that uses depthwise separable convolution (DSC) layers to solve the pedestrian attribute recognition problem. Researchers have proposed various solutions over the years making use of convolutional neural networks (CNN), however, we introduce DSC layers to the CNN for the problem of pedestrian attribute recognition. In addition, we make a novel use of the different color spaces and create a 3-branch CNN, denoted as 3bCNN, that is efficient, especially with smaller datasets. We experiment on two benchmark datasets and show results with improvement over the state of the art.
Weighted LIC-Based Structure Tensor With Application to Image Content Perception and Processing

As a famous visual content perception and processing tool, structure tensor has been widely studied in the past decades. Among them, the anisotropic nonlocal structure tensor (ANLST) has received much attention, recently. However, the existing ANLST calculation methods fail to fully utilize the anisotropic characteristic of the tensor field, thus resulting in limited performance. For this problem, in this article, we present a novel ANLST construction method, by means of combining tensor decomposition with weighted line integral convolution (LIC) with the aim at deeply discovering and exploiting the spatial direction relevancy of the tensors for their regularization. At first, the tensors decomposition, computed by direction projection, yields multiple atomic vector fields, from which, for each point in the tensor field we obtain a family of integral curves that are associated with spatial direction related tensors. Then, LIC is employed with the nonlocal means filtering to smooth the tensors relevant to each integral curve, giving rise to curve-level structure tensor (CLST). At last, a weighted average scheme is carried out on the multiple CLSTs, leading to our proposed weighted anisotropic nonlocal structure tensor (WANST). Experimental results demonstrate that the proposed WANST is superior to the current representative nonlinear structure tensors. The proposed WANST can be applied to industrial surveillance system to enable it perceive image contents, such as flat regions, corners, textures, and edges. In addition, WANST can also help monitoring system improve its image quality.
Machine Learning in IoT Security Performance Analysis of Outage Probability of link selection for Cognitive Networks

The future web of things architecture will have an inexpensive, industrial and social impact on our exist. The IoT network unit nodes usually resource in an unnatural way by making them more prone to cyber attacks. Throughout this regard, network standards were advanced to augment all outturn of 2 coinciding underlay optional systems that utilize the range of a dispatched essential system. Optimal systems are required either to transmit simultaneously by distributing the Interference Temperature Limit (ITL), or in separation. The in-depth efforts are created to switch the protection and privacy issues in IoT networks through the ancient science approach. This research paper has consistently reviewed the protection needs, attack vectors, and also the current security solutions incorporated in the IoT networks. This research paper will additionally discuss the prevailing cc and metric capacity unit solutions for addressing the totally different security problems in IoT networks. Finally, it derives a method to maintain the elaborated examination of the established solutions within the literature and additionally it has a facility to discuss the longer term analysis instructions used for ML- and DL-based IoT security.
A Context-Sensitive Real-Time Spell Checker with Language Adaptability

We present a novel language adaptable spell checking system that detects spelling errors and suggests context-sensitive corrections in real-time. We show that our system can be extended to new languages with minimal language-specific processing. Available literature majorly discusses spell checkers for English but there are no publicly available systems that can be extended to work for other languages out of the box. Most of the systems do not work in real-time. We explain the process of generating a language's word dictionary and n-gram probability dictionaries using Wikipedia-articles data and manually curated video subtitles. We present the results of generating a list of suggestions for a misspelled word. We also propose three approaches to create noisy channel datasets of real-world typographic errors. Finally, we show the effectiveness of language adaptability of our proposed system by extending it to 24 languages.
Cyber Attack and Defense Emulation Agents

As the scale of the system and network grows, IT infrastructure becomes more complex and hard to be managed. Many organizations have a serious problem to manage their system and network security. In addition, vulnerabilities of hardware and software are increasing in number rapidly. In such a complex IT environment, security administrators need more practical and automated threat assessment methods to reduce their manual tasks. Adversary emulation based automated assessment is one of the solutions to solve the aforementioned problems because it helps to discover the attack paths and vulnerabilities to be exploited. However, it is still inefficient to perform the adversary emulation because adversary emulation requires well-designed attack scenarios created by security experts. Besides, a manual-based penetration test cannot be frequently performed. To overcome this limitation, we propose an adversary emulation framework composed of the red team and blue team agent. The red team agent carries out automated attacks based on the automatically generated scenarios by the proposed framework. The blue team agent deploys defense measures to react to the red team agent’s attack patterns. To test our framework, we test multiple attack scenarios on remote servers that have various vulnerable software. In the experiment, we show the red team agent can gain an administrator’s privilege from the remote side when the blue team agent’s intervention is not enabled. The blue team agent can successfully block the red team’s incoming attack when enabled. As a result, we show our proposed framework is beneficial to support routine threat assessment from the adversary’s perspective. It will be useful for security administrators to make security defense strategy based on the test results.
Exploiting Ethereum Smart Contracts for Clinical Trial Management

Blockchain is a distributed ledger that ensures the authenticity of business transactions without the involvement of intermediaries, brokers or trusted third parties. The main features of blockchain technology include transparency, immutability and data provenance offers an important role in satisfying the requirements of a more secure, visible, efficient, and trusted clinical trial (CT) management. This is because the traditional CTs face significant challenges such as protocol compliance, transparency and patient enrollment. In this paper we discuss how blockchain technology can be leveraged to tackle data management challenges in clinical trials. We propose a blockchain-based framework using Ethereum based smart contract. In the framework, three stages of a CT process were captured: new drug application, CT initiation and patient enrollment stages. The framework captures key interactions among CT stakeholders including the regulatory agency, drug sponsor, principal investigator, physician and patients. The smart contract was written in Remix IDE and was successfully compiled and tested for different scenarios. The proposed approach and results can be highly beneficial to various stakeholders in CT data management.
D-ID-Net: Two-Stage Domain and Identity Learning for Identity-Preserving Image Generation From Semantic Segmentation

Training functionality-demanding AR/VR systems require accurate and robust gaze estimation and tracking solutions. Achieving such a performance requires the availability of diverse eye image data that might only be acquired by the means of image generation. Works addressing the generation of such images did not target realistic and identity-specific images, nor did they address the practical-relevant case of generation from semantic labels. Therefore, this work proposes a solution to generate realistic and identity-specific images that correspond to semantic labels, given samples of a specific identity. Our proposed solution consists of two stages. In the first stage, a network is trained to transform the semantic label into a corresponding eye image of a generic identity. The second stage is an identity-specific network that induces identity details on the generic eye image. The results of our D-ID-Net solutions shows a high degree of identity-preservation and similarity to the ground-truth images, with an RMSE of 7.235.
Image Retrieval Based on Self-Organizing Feature Map and Multilayer Perceptron Neural Networks Classifier

Content-based image retrieval (CBIR), is a type of computer vision application. It searches for an image by image content not text in large database images. According to the huge existence of image databases, the searching time and high accuracy retrieved images become a great challenge. This paper aims to find a solution to retrieve images with high accuracy results. Neural network became a hot topic in the image processing field for the past few years. This paper presents two approaches to Content-based image retrieval. The first approach used the Self-Organized Feature Map (SOFM) as a clustering method to image retrieval. The second approach consists of two phases. The first one used the SOFM as a feature extraction method. The second phase used the Multilayer Perceptron (MLP) as a classifier method. The paper studied the impact of changing some parameters values on recognition accuracy. The experiments carried out using the Wang Corel 1000 database. The results show that the SOFM+MLP improved the recognition accuracy compared to SOFM. The SOFM+MLP achieved approximately 99% average recognition accuracy.
Clinical applications of virtual reality in patient-centered care

Clinical virtual reality (VR) interventions can spur perceptual, behavioral, and attitudinal changes in patients. Long-standing patient-centered applications include pain management, physical therapy, psychotherapy, treating neurological conditions, and promoting healthy behaviors. Recent improvements in tracking, hardware fit, and increased mobility in consumer VR have increased the range of potential applications. System requirements are a key consideration for the design and use of such VR experiences. This chapter will present current patient-centered VR interventions through the lens of system requirements and conclude with a discussion on the future directions of clinical VR based on what equipment is now, and is becoming, available.
English vs Arabic Sentiment Analysis: A Survey Presenting 100 Work Studies, Resources and Tools

To determine whether a document or a sentence expresses a positive or negative sentiment, three main approach types are commonly used: lexicon based approaches, machine learning (ML) based approaches and hybrid approaches. English has the greatest number of sentiment analysis studies, unlike other languages including Arabic and its dialects. More specifically, ML based sentiment analysis requires annotated data. In the case of lexicon based approaches, they typically require the availability of lexicons annotated by valence and/or intensity. One of the majors problems related to the treatment of Arabic and its dialect is the lack of the above resources. This survey is aimed to highlight the most recent resources that have been constructed and most recent advances in the context of sentiment analysis (related to English and Arabic language). It refers to about one hundred recent papers, most of which published between 2015 and 2018. These works are classified by category (as survey work vs. solution work). In the case of solution works, we focus on the construction of sentiment lexicon and corpus. We also propose new trends in Arabic sentiment analysis, mainly employing deep learning techniques.
Image Dehazing Based on Luminance Stretching

In this work, a method is proposed to restore the visual effects of hazy images with sky using dark channel prior (DCP) and luminance stretching (LS). The transmissions for the non-sky and sky regions of a hazy image is calculated by DCP and LS respectively. The transmission of the hazy image is computed by combining the transmissions obtained using DCP and LS based on soft segmentation. The proposed method along with two state-of-the-art methods is evaluated on 125 randomly selected images from BSDS500 database. The qualitative and quantitative results depict that the proposed method outperforms two state-of-the-art methods.