Reviewerly/exHarmony huggingface dataset:
Degree Validation Application Using Solidity and Ethereum Blockchain

One of the most common actions for students post-graduation is requesting a degree validation or an official transcript of grades. Requests for degree validations happen for various reasons such as an employment requirement, a pursuit of a higher degree education, foreign degree accreditation, or even for a personal reason. Those who are requesting the degree validation are usually on a time constraint. Traditional methods of degree validation are often complicated, time consuming, require manpower to maintain, and potentially have security or privacy issues. We present a software application implementing an automatic degree validation system that uses the Ethereum, an open-source, public, blockchain-based distributed computing platform, and that features smart contract functionality. The application is developed using Solidity, a contract-oriented programming language, and is run by the Ethereum Virtual Machine (EVM). This application allows students and universities to automatically verify each other's identity while maintaining transaction validity and referential integrity through a blockchain ledger and maintaining security through PGP key authentication. The university checks the student's request against its own secure mongoDB database. Our application also generates the student's Ehereum wallet using the Truffle development Ethereum network. Truffle generates the student's private and public keys. Our Application pulls the student public key, hashes the last 20 bytes, generates Keccak256 Hash, and stores it on the MongoDB as the student Ethereum Address. To ensure the information's privacy, the blocks of the blockchain are mainly formed of two encrypted fields using the Elliptic Curve Integrated Encryption Scheme (ECIES) library. The first field is encrypted using the student's public key and the second field is encrypted using the third party public field. Third parties, such as employers or other higher education institutions, are able to check its encrypted field of the block using its own private key, once the degree is validated or the transcript is generated. Results of testing show our application to be scalable, secure, have efficient gas costs on the Ethereum network, and minimize human interaction.
Consistency measure based simultaneous feature selection and instance purification for multimedia traffic classification

With the increase of multimedia traffic, the implementation of fast and accurate classification has become an important issue. Besides, a manually captured dataset contains certain noise and mislabeled instances, which influences the accuracy of classifier to some extent. Motivated by these observations, a novel feature selection and instance purification (FS&IP) method based on consistency measure is proposed. It utilizes a linear consistency-constrained algorithm for feature selection. In each round of iteration, it removes the instance with the minor labels in every pattern subset. Our method has three desirable properties: 1) It can simultaneously achieve feature selection and data purification. 2) when purifying instance, it doesn’t need to annotate the noisy instance with learned labels; that is because it is an unsupervised method in terms of data purification. 3) through data purification, it is able to obtain a minimal feature subset on condition of maintaining accuracy. In addition, the proposed method can be used to discover a new discriminative feature based on linking behaviors called the flow fragment (F−Frag), which can reflect important information among the complex and multitudinous packet communication behaviors. The experimental results over six different datasets demonstrate the advantages of the proposed technique compared to six existing methods, and the discriminative power of the new flow fragment feature.
Anticontrol of a Fractional-Order Chaotic System and Its Application in Color Image Encryption

This paper investigates the anticontrol of the fractional-order chaotic system. The necessary condition of the anticontrol of the fractional-order chaotic system is proposed, and based on this necessary condition, a 3D fractional-order chaotic system is driven to two new 4D fractional-order hyperchaotic systems, respectively, without changing the parameters and fractional order. Hyperchaotic properties of these new fractional dynamic systems are confirmed by Lyapunov exponents and bifurcation diagrams. Furthermore, a color image encryption algorithm is designed based on these fractional hyperchaotic systems. The effectiveness of their application in image encryption is verified.
Chat with Bots Intelligently: A Critical Review &amp; Analysis

Conversation Agents also known as Chatbots are considered the next big thing in the modern technological era. Chatbots aim to emulate humans, by mimicking human conversation in the most realistic way possible. There are several methods to generate such intelligent agents. Thus to evaluate the effectiveness of the various approaches, a systematic review of the approaches is required. This paper traces the evolution of chatbots and also elaborates on the impact chatbots have in different industries. Additionally, it also presents a survey of the different modern chatbot methodologies that different researchers have proposed. When analyzing the different approached, this paper has taken into consideration the dataset, input-output, design, development, performance metric, and limitations into consideration. In the end, we've elaborated on the key insights that were obtained pertaining to the crucial factors that influence chatbot design, generation, and development.
A high-precision online monitoring system for surface settlement imaging of railway subgrade

In order to improve the measurement accuracy of subgrade settlement deformation system, this study proposes a high-precision, online monitoring system for subgrade settlement imaging. Firstly, the working principle and key technologies of this system are introduced. Secondly, a pose measurement system is designed to solve the problem that the relative position of target and camera in the monitoring system is affected by train vibration, and attitude estimation method p4p(Perspective 4 Points) for rectangular four-feature points target is proposed. Finally, experimental results indicate that when the relative deflection angle is less than 2°, the rotation angle error of pose measurement method in this study is less than 0.09°. The measurement accuracy of this method meets the requirement of this online monitoring system. And on-site testing results illustrate that the monitoring system corrected by the pose measurement system avoids the detection of non-real subgrade settlement and improves the measurement accuracy.
Fuzzy Style K-Plane Clustering

As the first attempt, this article considers how to provide a design methodology for style clustering on stylistic data, where each cluster depends on both the similarities between data samples and its latently or apparently distinguishable style. By taking our previous fuzzy k plane clustering algorithm as the basic framework, a fuzzy style k-plane clustering (S-KPC) algorithm is proposed to have its distinctive merits: First, the nuances between styles of clusters can be well identified by using the proposed twofold data representation. That is to say, style matrices are used to express the structure, hence style information of each cluster, whereas the augmentation of the original features of data with enhanced nodes is taken as an abstract representation so as to move the manifold structure of data apart. Such a twofold data representation can make us realize S-KPC readily in an incremental way. Second, by means of alternating optimization strategy, the objective function of S-KPC can be optimized such that each discriminant function of each cluster shares the advantages of both simple regression models and functional-link neural networks. Extensive experiments on synthetic and real-world datasets demonstrate that S-KPC has comparable clustering performance with several compared methods on the adopted ordinary datasets, and yet it obviously outperforms them on stylistic datasets.
A 0.22–0.89 mW Low-Power and Highly-Secure Always-On Face Recognition Processor With Adversarial Attack Prevention

A low-power, highly secure, always-on face recognition (FR) processor is required for security applications. In this brief, a branch net-based early stopping FR (BESF) processor is proposed to prevent adversarial attacks for high security and consume low power for always-on operation. It shows a recognition accuracy of 83.10% under the fast gradient signed method (FGSM), and 71.97% under the projected gradient descent (PGD) attack. The clock-gating of the BESF processor reduces the average power consumption by 30.85%. The unified pointwise and depthwise convolution processing element adopts layer-fusion to reduce the external memory access by 88.0%. Furthermore, noise injection layers are inserted between every bottleneck layer to further reduce the FGSM and PGD attack success rate by 9.29% and 20.0%, respectively. Implemented with a 65 nm CMOS process with a 3.0 mm × 3.0 mm area, the processor consumes 0.22-0.89 mW power at 1 fps and shows 95.5% FR accuracy in the Labeled Faces in the Wild dataset.
Neural Nonnegative Matrix Factorization for Hierarchical Multilayer Topic Modeling

We introduce a method for detecting latent hierarchical structure in data based on nonnegative matrix factorization. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. The proposed method, Neural NMF, recursively applies topic modeling in layers to discover overarching topics encompassing the lower-level features. We derive a backpropagation scheme that allows us to frame our method as a neural network. Numerical results on a synthetic dataset demonstrate that Neural NMF outperforms similar algorithms on a hierarchical classification task.
A Black-Box Fork-Join Latency Prediction Model for Data-Intensive Applications

The workflows of the predominant datacenter services are underlaid by various Fork-Join structures. Due to the lack of good understanding of the performance of Fork-Join structures in general, today's datacenters often operate under low resource utilization to meet stringent service level objectives (SLOs), e.g., in terms of tail and/or mean latency, for such services. Hence, to achieve high resource utilization, while meeting stringent SLOs, it is of paramount importance to be able to accurately predict the tail and/or mean latency for a broad range of Fork-Join structures of practical interests. In this article, we propose a black-box Fork-Join model that covers a wide range of Fork-Join structures for the prediction of tail and mean latency, called ForkTail and ForkMean, respectively. We derive highly computational effective, empirical expressions for tail and mean latency as functions of means and variances of task response times. Our extensive testing results based on model-based and trace-driven simulations, as well as a real-world case study in a cloud environment demonstrate that the models can consistently predict the tail and mean latency within 20 and 15 percent prediction errors at 80 and 90 percent load levels, respectively, for heavy-tailed workloads, and at any load levels for light-tailed workloads. Moreover, our sensitivity analysis demonstrates that such errors can be well compensated for with no more than 7 percent resource overprovisioning. Consequently, the proposed prediction model can be used as a powerful tool to aid the design of tail-and-mean-latency guaranteed job scheduling and resource provisioning, especially at high load, for datacenter applications.
Adaptive Densely Connected Single Image Super-Resolution

For a better performance in single image super-resolution(SISR), we present an image super-resolution algorithm based on adaptive dense connection (ADCSR). The algorithm is divided into two parts: BODY and SKIP. BODY improves the utilization of convolution features through adaptive dense connections. Also, we develop an adaptive sub-pixel reconstruction layer (AFSL) to reconstruct the features of the BODY output. We pre-trained SKIP to make BODY focus on high-frequency feature learning. The comparison of PSNR, SSIM, and visual effects verify the superiority of our method to the state-of-the-art algorithms.
Development of Soft Skills by Doctoral Students

This paper has been prepared as a part of the capacity building project “Modernization of Doctoral Education in Science and Improvement of Teaching Methodologies” made possible by the programs of Erasmus+. The main aim of this project is to improve the quality of doctoral education by implementing new teaching methodologies based on interdisciplinary approaches and international cooperation. The author presents Russian system of Doctoral education, its structure, content and main competences required for training highly qualified researchers. The paper focuses on developing communicative skills by PhD students in the framework of Foreign Language Course which is included Doctoral degree programs.
Syntax-aware Transformer Encoder for Neural Machine Translation

Syntax has been shown a helpful clue in various natural language processing tasks including previous statistical machine translation and recurrent neural network based machine translation. However, since the state-of-the-art neural machine translation (NMT) has to be built on the Transformer based encoder, few attempts are found on such a syntax enhancement. Thus in this paper, we explore effective ways to introduce syntax into Transformer for better machine translation. We empirically compare two ways, positional encoding and input embedding, to exploit syntactic clues from dependency tree over source sentence. Our proposed methods have a merit keeping the architecture of Transformer unchanged, thus the efficiency of Transformer can be kept. The experimental results on IWSLT' 14 German-to-English and WMT14 English-to-German show that our method can yield advanced results over strong Transformer baselines.
Occlusions for Effective Data Augmentation in Image Classification

Deep networks for visual recognition are known to leverage ""easy to recognise"" portions of objects such as faces and distinctive texture patterns. The lack of a holistic understanding of objects may increase fragility and overfitting. In recent years, several papers have proposed to address this issue by means of occlusions as a form of data augmentation. However, successes have been limited to tasks such as weak localization and model interpretation, but no benefit was demonstrated on image classification on large-scale datasets. In this paper, we show that, by using a simple technique based on batch augmentation, occlusions as data augmentation can result in better performance on ImageNet for high-capacity models (e.g., ResNet50). We also show that varying amounts of occlusions used during training can be used to study the robustness of different neural network architectures.
Enhancing Optimized Personalized Therapy in Clinical Decision Support System using Natural Language Processing

Sentiment analysis is the process of identifying and categorising the opinions expressed by human utterances through computational techniques using natural language processing. The present work focuses on a case study to develop a clinical decision support system for personalized therapy process using aspect-based sentiment analysis. The process is carried out on a drug review data in order to determine whether the patient's behaviour towards a medicine, product, treatment etc is positive, negative or neutral using NLP techniques. The polarities obtained are compared for further analysis of the patient reviews for the better clinical decision system. Machine learning methods are also used for classification of the drug review data to compare the sentiment scores. The prominent statistical sklearn models used are support vector machines (SVM), Random Forest Classification, LinearSVC, MultinomialNB. SVM algorithm is found to perform better compared to other in terms of accuracy.
Full-Reference Stereoscopic Video Quality Assessment Using a Motion Sensitive HVS Model

Stereoscopic video quality assessment has become a major research topic in recent years. Existing stereoscopic video quality metrics are predominantly based on stereoscopic image quality metrics extended to the time domain via for example temporal pooling. These approaches do not explicitly consider the motion sensitivity of the Human Visual System (HVS). To address this limitation, this paper introduces a novel HVS model inspired by physiological findings characterising the motion sensitive response of complex cells in the primary visual cortex (V1 area). The proposed HVS model generalises previous HVS models, which characterised the behaviour of simple and complex cells but ignored motion sensitivity, by estimating optical flow to measure scene velocity at different scales and orientations. The local motion characteristics (direction and amplitude) are used to modulate the output of complex cells. The model is applied to develop a new type of full-reference stereoscopic video quality metrics which uniquely combine non-motion sensitive and motion sensitive energy terms to mimic the response of the HVS. A tailored two-stage multi-variate stepwise regression algorithm is introduced to determine the optimal contribution of each energy term. The two proposed stereoscopic video quality metrics are evaluated on three stereoscopic video datasets. Results indicate that they achieve average correlations with subjective scores of 0.9257 (PLCC), 0.9338 and 0.9120 (SRCC), 0.8622 and 0.8306 (KRCC), and outperform previous stereoscopic video quality metrics including other recent HVS-based metrics.
Facial Age and Expression Synthesis Using Ordinal Ranking Adversarial Networks

Facial image synthesis has been extensively studied, for a long time, in both computer graphics and computer vision. Particularly, the synthesis of face images with varying ages, expressions and poses has received an increasing attention owing to several real-world applications. In this paper, facial age and expression synthesis are addressed. While previous and current research papers on facial age synthesis mostly adopt an age span of 10 years, this paper investigates face aging with a shorter time span. For expression synthesis, given a neutral face, we work on synthesizing faces with varying expression intensities (e.g., from zero to high). Note that both human ages and expression intensities are inherently ordinal. To fully exploit this ordinal nature, we devise ordinal ranking generative adversarial networks (ranking GAN). For each face, a one-hot label is assigned to define its age range/expression intensity. By exploiting the relative order information among age ranges/expression intensities, a binary ranking vector is further computed for each face. In ranking GAN, one-hot labels are used as the condition of the generator for synthesizing faces with target age groups/expression intensities. Moreover, we add a sequence of cost-sensitive ordinal rankers on top of several multi-scale discriminators, with the aim of minimizing age/intensity rank estimation loss when optimizing both the generator and discriminators. In order to evaluate the proposed ranking GAN, extensive experiments are carried out on several public face databases. As demonstrated by the experimental testing, this ranking scheme performs well even when the amount of available labeled training data is limited. The reported experimental results well demonstrate the effectiveness of ranking GAN on synthesizing face aging sequences and faces with varying expression intensities.
RMNv2: Reduced Mobilenet V2 for CIFAR10

In this paper, we developed a new architecture called Reduced Mobilenet V2 (RMNv2) for CIFAR10 dataset. The baseline architecture of our network is Mobilenet V2. RMNv2 is architecturally modified version of Mobilenet V2. The proposed model has a total number of parameters of 1.06M which is 52.2% lesser than the baseline model. The overall accuracy of RMNv2 for CIFAR10 dataset is 92.4% which is 1.9% lesser than the baseline model. The architectural modifications involve heterogeneous kernel-based convolutions, mish activation, etc. Also, we include a data augmentation technique called AutoAugment that contributes to increasing accuracy of our model. This architectural modification makes the model suitable for resource-constrained devices like embedded devices, mobile devices deployment for real-time applications like autonomous vehicles, object recognition, etc.
A survey on distributed filtering, estimation and fusion for nonlinear systems with communication constraints: new advances and prospects

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An Unrestricted Learning Procedure

We study learning problems involving arbitrary classes of functions F , underlying measures μ, and targets Y . Because proper learning procedures, i.e., procedures that are only allowed to select functions in F , tend to perform poorly unless the problem satisfies some additional structural property (e.g., that F is convex), we consider unrestricted learning procedures that are free to choose functions outside the given class. We present a new unrestricted procedure whose sample complexity is almost the best that one can hope for and holds for (almost) any problem, including heavy-tailed situations. Moreover, the sample complexity coincides with what one could expect if F were convex, even when F is not. And if F is convex, then the unrestricted procedure turns out to be proper.
Combining Deep Learning and Super-Resolution Algorithms for Deep Fake Detection

Deep Fake is a technique for human image synthesis based on artificial intelligence. In this article is explored the problem of Deep Fake Video content and its detection. Has been gathered information about previous attempts, analyzed methods used by different researches and considered their actuality right now. Basing on results of the discovery was designed strategy to expose Deep Fake videos that combines previous detection methods with super-resolution algorithms. Results of the research were compared with expected, so recommendations and possible way of continuing developments were given.
Hate Speech and Abusive Language Classification using fastText

Hate speeches are defined as utterances, writings, actions, or performances that are intended to incite violence or prejudice against a person on the basis of the characteristics of a particular group that he or she is representing, such as race, ethnicity. In this study, we built a hate speech classification model using word representation with continous bag of words (CBOW) and fastText algorithm. This algorithms was chosen, because it is able to achieve a good performance, specially in the case of rare words by making use of character level information. Based on this result, we can see that there is no single, universal variations that outperform other. But in general, models that use pre-trained vectors from Wiki outperform models that do not use pre-trained vectors.