Reviewerly/exHarmony huggingface dataset:
Stochastic Computing: An Introduction

Stochastic Computing (SC) essentially represents numbers as streams of random bits and reconstructs numbers by calculating frequencies. It employs random bits to calculate via simpler circuits and with greater tolerance for errors. As a computing paradigm, SC is currently undergoing a revival. Since stochastic circuits have a small size, SC has regained interest recently due to its potential usage in some emerging nanotechnologies. In this paper, we briefly present stochastic computing and discuss its applications, benefits, and challenges.
Re-targeting User Interfaces on Stand-alone Smart Wearables

In a world of numerous smart devices, people use and interact with fragmented user interfaces daily. Inevitably, users try and adapt to seemingly different options that often require heavy workload to perform same tasks across different devices in various form factors and computation capability. We explore a research theme of user interface re-targeting applied to stand-alone smart wearables. We present working definitions and examples to discuss five factors that affect the quality of re-targeted user interfaces. Also, we discuss recent approaches and challenges surrounding these factors where applicable.
Resolution Booster: Global Structure Preserving Stitching Method for Ultra-High Resolution Image Translation

Current image translation networks (for instance image to image translation, style transfer et al.) have strict limitation on input image resolution due to high spatial complexity, which results in a wide gap to their usage in practice. In this paper we propose a novel patch-based auxiliary architecture, called Resolution Booster, to endow a trained image translation network ability to process ultra high resolution images. Different from previous methods which compute the results with the entire image, our network processes resized global image at low resolution as well as high-resolution local patches to save the memory. To increase the quality of generated image, we exploit the rough global information with global branch and high resolution information with local branch then combine the results with a designed reconstruction network. Then a joint global/local stitching result is produced. Our network is flexible to be deployed on any exiting image translation method to endow the new network to process larger images. Experimental results show the both capability of processing much higher resolution images while not decreasing the generating quality compared with baseline methods and generality of our model for flexible deployment.
Використання сучасних інформаційних технологій у процесі навчання перекладу

The article analyzes the features of the use of modern technologies in the process of translation training. It was determined that modern information technologies have become an important component in the process of preparing modern experts on translation. A specialist in this profession must know the features of modern software in this direction, be able to use the Internet to keep in touch with colleagues from abroad and be knowledgeable in modern translation technologies. However, it was found that sometimes the use of modern information technologies is reduced to their narrow use - traditional teaching materials are available on the Internet, respectively, only a certain environment for students is changing. It was found that the modern educational concept of Blended Learning allows the student to gain knowledge independently - online and in person - with the teacher. The use of blogs makes it possible to make adjustments to the content, to enter into polemics with the author, or the teacher in the form of questions, comments, notes, etc. Wikis and chat can work as auxiliary information tools - they can familiarize students with the peculiarities of the translation process as a whole and their own process. Forums, as well as chats or e-mails can also be used as a tool for learning.Teachers in this case work as motivators to facilitate interaction and create scientific debates. It should be noted that the use of information technologies allows us to activate the educational process, contributes to the formation of the necessary translation competencies, primarily linguistic and communication, to promote the development of activity and independence of our students. The use of ICT allows us to overcome certain problems of the methodical, to develop new models and tools for teaching and evaluation, while maintaining an individual approach to each student. At the same time, it should be noted, the use of new technologies requires some training.
Thomas Eric Marshall McLuhan

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An exploratory research on grammar checking of Bangla sentences using statistical language models

N-gram based language models are very popular and extensively used statistical methods for solving various natural language processing problems including grammar checking. Smoothing is one of the most effective techniques used in building a language model to deal with data sparsity problem. Kneser-Ney is one of the most prominently used and successful smoothing technique for language modelling. In our previous work, we presented a Witten-Bell smoothing based language modelling technique for checking grammatical correctness of Bangla sentences which showed promising results outperforming previous methods. In this work, we proposed an improved method using Kneser-Ney smoothing based n-gram language model for grammar checking and performed a comparative performance analysis between Kneser-Ney and Witten-Bell smoothing techniques for the same purpose. We also provided an improved technique for calculating the optimum threshold which further enhanced the the results. Our experimental results show that, Kneser-Ney outperforms Witten-Bell as a smoothing technique when used with n-gram LMs for checking grammatical correctness of Bangla sentences.
Optimization of a linear controller using dynamic back-propagation

This paper presents the optimization of a linear controller for a DC motor using Dynamic Back-Propagation algorithm. This algorithm is commonly employed for neural networks training; however, it can be used for optimization of a linear controller. The results show a satisfactory controller optimization.
THE PROBLEMS OF PUBLIC REGULATION OF EDITING AND PUBLISHING ACTIVITY IN SCIENTIFIC SPHERE

One of the strategic priorities of Russian science is indexation of not less than 500 Russian magazines in the Web of Science, Scopus and other global systems of citing by 2024. To achieve this goal it is necessary to change the idea of scientific and publishing community about ethical and other norms in the sphere of scientific publications. This problem cannot be solved exclusively by administrative methods, coordinated interaction of public agencies and public organizations is necessary.
Component-Based Face Recognition using CNN for Forensic Application

Face recognition is a breakthrough in human identification. It is a difficult task in a forensic scenario as mostly suspect's face is partially occluded. The major contribution of this paper is partial face recognition i.e. recognition by its components or parts. Component-based recognition is immune to pose and expression changes that add up in the overall accuracy. Proposed work uses Convolutional Neural Network that can identify human, based only on components like eyes, nose, and mouth or by the whole face. The system serves good for both full and partial face recognition, minimizing the training time that would instead be required by two different systems. Obtained results show that the nose gives better accuracy than eyes and mouth. In addition, full face recognition accuracy is improved by using both, full face and its components for recognition. The database used for all the experiments is Faces94, which is a standard face database of Computer Vision Research Projects.
An Investigation of Machine Learning Training Data Padding Method by Shifting Sampling

A large amount of data is necessary to obtain desired results in machine learning in which a robot substitutes humans’ manual labors. In addition, it is an explicit problem that it takes a lot of time and operating costs for it. In this research, we focus on down-sampling and decimation used in system identification and propose a method to reduce teacher data by padding data in machine learning.
Countermeasure Method Against Unauthorized and Anonymous Information System Data Collection

In this paper, we describe the problematics of the network security in corporate networks. We study the vulnerability of existing information system to unauthorized identification and analysis of the object's properties. We consider one of the professional hacking strategies performed during the stage of gathering information about the target system using anonymization technology (protected virtual channels, overlay technology and networks). We propose an original countermeasure method against such unauthorized analysis. The scientific novelty of the work is identifying and profiling the intruders who use anonymization technology for malicious activity by compiling attack vectors based on a number of parameters: anonymization network type, network address space, networks address pool exhaustion level, passive or active scanning type (scanning, probing), progress of the scan, correlation of parameters due to artificial delays and other factors. This approach is combined with the author's operating system and services falsification method that can work with various services for different hackers. This allows to successfully misinform and profile the attacker at the first stage of the cyberattack, then falsificate the object of the attack and redirect the attacker to the created trap-object. The software implementation of the author's method was successfully experimentally investigated and tested. The target area of application are server solutions that operate based on the TCP/IP Protocol stack.
Medium- and Long-Term Predictions for Coastal Economies Based on Support Vector Regression

Wang, Y., 2019. Medium- and long-term predictions for coastal economies based on support vector regression. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 71–74. Coconut Creek (Florida), ISSN 0749-0208.In this article, we analyze the characteristics of coastal economic development as well as the characteristics of mid- to long-term economic forecasts, expound on the existing shortcomings of current economic forecasting methods, and point out that, because of the high degree of nonlinearity, coupling, and time varying in the coastal economic system, the existing economic forecasting method is not competent. In addition, we analyze the support vector regression algorithm, and on that basis, put forward an idea for medium- and long-term predictions about the coastal economy based on support vector regression and establish a corresponding mathematical model. Jiangmen City, Guangdong Province, China, is used as an application object to show the validity of the model.
Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

Hyperspectral images (HSIs) denoising aims at recovering noise-free images from noisy counterparts to improve image visualization. Recently, various prior knowledge has attracted much attention in HSI denoising, e.g., total variation (TV), low-rank, sparse representation, and so on. However, the computational cost of most existing algorithms increases exponentially with increasing spectral bands. In this paper, we fully take advantage of the global spectral correlation of HSI and design a unified framework named subspace-based Moreau-enhanced total variation and sparse factorization (SMTVSF) for multispectral image denoising. Specifically, SMTVSF decomposes an HSI image into the product of a projection matrix and abundance maps, followed by a ‘Moreau-enhanced’ TV (MTV) denoising step, i.e., a nonconvex regularizer involving the Moreau envelope mechnisam, to reconstruct all the abundance maps. Furthermore, the schemes of subspace representation penalizing the low-rank characteristic and ℓ 2 , 1 -norm modelling the structured sparse noise are embedded into our denoising framework to refine the abundance maps and projection matrix. We use the augmented Lagrange multiplier (ALM) algorithm to solve the resulting optimization problem. Extensive results under various noise levels of simulated and real hypspectral images demonstrate our superiority against other competing HSI recovery approaches in terms of quality metrics and visual effects. In addition, our method has a huge advantage in computational efficiency over many competitors, benefiting from its removal of most spectral dimensions during iterations.
RETRACTED: Contourlet transform and adaptive neuro-fuzzy strategy–based color image watermarking

In the today Internet era, protection of digital content during transmission is an indigent. Alphanumeric watermarking is a resolution to the copyright defense than the endorsement of information into the system. In exhibit watermarking calculation, wellbeing of such watermarking process is moderately low. For expanding the soundness, an approach is presented, which is contourlet change with neuro-fuzzy-based watermark inserting process. The conventional approaches having loss during data recovery, this situation will be resolved using proposed watermarking scheme and also increase the security of watermarked image. The proposed color image watermarking scheme binary image is embedded over the shading image which utilizes contourlet and converse contourlet calculation for preprocessing of an image and neuro-fuzzy calculation to implant the bits in the green plane of an image. After completing the watermarking process, the results are analyzing using the quality assessment metrics like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) etc., It is implemented using MATLAB R2013 software. The developed MATLAB code is converted into Hardware Description Language (HDL) and then implemented for Virtex-5 L110T Field Programmable Gate Array (FPGA) kit.
Social Engineering Resistant 2FA

Attackers increasingly, and with high success rates, use social engineering techniques to circumvent second factor authentication (2FA) technologies, compromise user accounts and sidestep fraud detection technologies. We describe an approach, replacing the use of traditional security codes, that thwarts social engineering attacks on 2FA without relying on user risk awareness. Our approach is compatible with existing systems, and offers a similar user experience to that of traditional security code methods. It protects both against user-facing social engineering attacks and attacks, such as SIM card hijacking attacks, where the social engineering is applied to a service provider of the victim.
Text Classification Using Parallel Word-level and Character-level Embeddings in Convolutional Neural Networks

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naive Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character-level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.
Analysis and Design of IoT-Enabled, Low-Cost Distributed Angle Measurement System

A Linear Fresnel Reflector (LFR) is a recent technology with good potential in small-scale solar power applications. It is composed of many long rows of mirrors that focus the sunlight onto a fixed elevated tubular receiver. Mirror segments are aligned horizontally and track the sun such that the receiver does not need to be moved. The efficiency with which the LFR can convert solar to thermal energy depends on the accuracy of the sun tracking system. To maximize the degree of sunlight capture, precise solar tracking is needed so that incident solar rays can be adequately focused to the focal point given by the location of the tubular receiver. The tilt angles of each row are relevant for the tracking controller to achieve correct positioning. Encoders are generally employed in closed-loop tracking systems as feedback signals used to inform the controller with the actual position of collector mirrors. Recently, inclinometers have begun to replace encoders as the most viable and cost-effective sensor technology solution; they offer simpler and more precise feedback, as they measure the angle of tilt with respect to gravity and provide the ability to adjust the system to the optimal angle for maximum output. This paper presents the research results on the development of remote measurements for the precise control of an LFR tracking system, by using distributed angle measurements. The applied methodology enables precision measurement LFR inclination angles through the fusion of data from multiple accelerometers, supported by low-cost wireless transceivers in a wireless sensor network, capable of exchanging information in a cloud infrastructure.
Visual inspection system for trackside communication and signal infrastructure

Trackside communication and signal infrastructure (TCSI) plays an important role in the safe operation of a high-speed railway system. However, it is a challenge to inspect such infrastructure using a vision-based system because of the uneven illumination and various reflection properties of the TCSI surfaces. This paper puts forward a visual inspection system installed on an inspection car for TCSI. The visual inspection system first captures the trackside images using the image acquisition system. Then, an image processing method using a varying-scale Retinex algorithm is implemented so that the visual inspection system can enhance the contrast of these trackside images. The method changes the size of the Gaussian kernel adaptively according to the image details in order to prevent halo artifacts and graying-out effects inherent in many image enhancement algorithms. By comparison with several state of the art methods, varying-scale Retinex is validated to overcome the local inhomogeneous brightness of images and make those images more suitable for browsing and analysis for inspectors. Composed of the image acquisition system and varying-scale Retinex, the visual inspection system is installed on many inspection cars in China for performing visual inspection.
Parallelizable Authenticated Encryption with Small State Size

Authenticated encryption (AE) is a symmetric-key encryption function that provides confidentiality and authenticity of a message. One of the evaluation criteria for AE is state size, which is memory size needed for encryption. State size is especially important when cryptosystem is implemented in constrained devices, while trivial reduction by using a small primitive is not generally acceptable as it leads to a degraded security. In these days, the state size of AE has been very actively studied and a number of small-state AE schemes have been proposed, but they are inherently serial. It would be a natural question if we come up with a parallelizable AE with a smaller state size than the state-of-the-art. In this paper, we study the seminal OCB mode for parallelizable AE and propose a method to reduce its state size without losing the bit security of it. More precisely, while (the most small-state variant of) OCB has 3n-bit state, by carefully treating the checksum that is halved, we can achieve 2.5n-bit state, while keeping the n/2-bit security as original. We also propose an inverse-free variant of it based on OTR. While the original OTR has 4n-bit state, ours has 3.5n-bit state. To our knowledge these numbers are the smallest ones achieved by the blockcipher modes for parallel AE and inverse-free parallel AE.
The Kuroko Paradigm: The Implications of Augmenting Physical Interaction with AR Avatars

We propose a concept in this poster paper, the Kuroko Paradigm, which is able to enhance user engagement during interaction with an augmented reality (AR) avatar by adding a physical object to the interaction with the avatar. With the development of AR and VR, interactions between users and AR avatars have been realized with different approaches. However, most of such interactions and experiences are passive, from which users do not expect a high level of engagement. We hypothesize that by introducing a reality actuator, such as a robot or a drone, to handle a physical object triggered by the user without being noticed, and rendering AR avatars as interacting with the physical object at the same time, user engagement during the experience will be enhanced. To prove this concept, we conducted an experiment emulating a classic game of catch. In the experiment, a user will try to throw a ball to an AR avatar, and the ball will be caught by a reality actuator. From the user's perspective, the ball is caught by the AR avatar. In the future, we plan to extend the experiment by adding control groups with differing conditions.
Monocular depth ordering with occlusion edges extraction and local depth inference

In this paper, a method to infer global depth ordering for monocular images is presented.Firstly a distance metric is defined with color, compactness, entropy and edge features to estimate the difference between pixels and seeds, which can ensure the superpixels to obtain more accurate object contours.To correctly infer local depth relationship, a weighting descriptor is designed that combines edge, T-junction and saliency features to avoid wrong local inference caused by a single feature.Based on the weighting descriptor, a global inference strategy is presented, which not only can promote the performance of global depth ordering, but also can infer the depth relationships correctly between two non-adjacent regions.The simulation results on the BSDS500 dataset, Cornell dataset and NYU 2 dataset demonstrate the effectiveness of the approach.
Philosophy through Machine Learning

In a previous article (2019), I motivated and defended the idea of teaching philosophy through computer science. In this article, I will further develop this idea and discuss how machine learning can be used for pedagogical purposes because of its tight affinity with philosophical issues surrounding induction. To this end, I will discuss three areas of significant overlap: (i) good / bad data and David Hume’s so-called Problem of Induction, (ii) validation and accommodation vs. prediction in scientific theory selection and (iii) feature engineering and Nelson Goodman’s so-called New Riddle of Induction.