Reviewerly/exHarmony huggingface dataset:
Yoneda Hacking: The Algebra of Attacker Actions

Our work focuses on modeling the security of systems from their component-level designs. Towards this goal, we develop a categorical formalism to model attacker actions. Equipping the categorical formalism with algebras produces two interesting results for security modeling. First, using the Yoneda lemma, we can model attacker reconnaissance missions. In this context, the Yoneda lemma shows us that if two system representations, one being complete and the other being the attacker’s incomplete view, agree at every possible test, they behave the same. The implication is that attackers can still successfully exploit the system even with incomplete information. Second, we model the potential changes to the system via an exploit. An exploit either manipulate the interactions between system components, such as providing the wrong values to a sensor, or changes the components themselves, such as controlling a global positioning system (GPS). One additional benefit of using category theory is that mathematical operations can be represented as formal diagrams, helpful in applying this analysis in a model-based design setting. We illustrate this modeling framework using an unmanned aerial vehicle (UAV) cyber-physical system model. We demonstrate and model two types of attacks (1) a rewiring attack, which violates data integrity, and (2) a rewriting attack, which violates availability.
Fuzzy sentiment analysis using convolutional neural network

Sentiment analysis is one part of natural language processing. Sentiment analysis can be done by lexicon based, or machine learning based. Sentiment analysis based on machine learning has advantage of dynamism to meet with new language datasets or new vocabulary. Sentiment analysis seeks to understand the sentiments contained in a sentence. A sentence can be positive, neutral or negative, based on its sentiments. A sentence can have positive, neutral or negative sentiments. However, the fact is each sentence does not always have positive, negative or neutral sentiment clearly. We try to develop a sentiment analysis method that can show the sentiment degree of a sentence. Fuzzy sentiment analysis using convolutional neural network are introduced in this paper to produce more accurate sentiment analysis results. Convolutional neural networks are a popular machine learning method for sentiment analysis. The concept of fuzzy sets is used to express the sentiment degree of a sentence. Euclidean distance analysis to determine the proximity of two vectors is used to show that this method is better than the standard method. The method we propose successfully produces a value that indicates the degree of sentiment of a sentence. Comparison of the euclid distance between the results of the standard sentiment analysis and our method shows that the results of the fuzzy sentiment analysis using convolutional neural network have a distance that is relatively close to the true sentiment value. Fuzzy convolutional neural network analysis sentiment is proven to be able to produce better and smoother sentiment analysis results than standard methods.
HGAN: Hybrid generative adversarial network

In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood. However, GANs overlook the explicit data density characteristics which leads to undesirable quantitative evaluations and mode collapse. To bridge this gap, we propose a hybrid generative adversarial network (HGAN) for which we can enforce data density estimation via an autoregressive model and support both adversarial and likelihood framework in a joint training manner which diversify the estimated density in order to cover different modes. We propose to use an adversarial network to transfer knowledge from an autoregressive model (teacher) to the generator (student) of a GAN model. A novel deep architecture within the GAN formulation is developed to adversarially distill the autoregressive model information in addition to simple GAN training approach. We conduct extensive experiments on real-world datasets (i.e., MNIST, CIFAR-10, STL-10) to demonstrate the effectiveness of the proposed HGAN under qualitative and quantitative evaluations. The experimental results show the superiority and competitiveness of our method compared to the baselines.
Integrative potential of educational resources in a modern metropolis

The intensity of globalization and internationalization stimulates activity of population in the world. As a consequence, the number of Russian citizens living and getting education beyond the boundaries of the Russian Federation increases. Special load in this case is on the municipal structures rendering educational services in the Russian language. Quality improvement of teaching Russian and status of educational services in the Russian language are the social priorities and assume serious rearrangement of additional training of Russian language teachers. In this regard, it would be relevant to evaluate professional competences of Russian language teachers in schools abroad; to detect competence lacunas of additional professional education; to correct the detected professional deficits by additional educational programs of various specializations and degrees of complexity. This work is aimed at detection of development strategies of the Center as a tool of distance support for Russian schools abroad. The research methods in this work are theoretical analysis of psychologic and pedagogical, sociological, economic publications devoted to the considered problem; analysis of practices of distance support courses in Russia and abroad; surveying, testing users of existing courses; analysis of expert opinions and quality appraisals of online training; analysis of statistic data. The activity of such Center would promote implementation of the Russian Schools Abroad concept, providing opportunity of annual systematic diagnostics of state of educational and methodological resources of foreign schools, supplying Russianists with modern methodological tools, allowing foreign schools to systematically raise the level of professional competences of teaching staff.
An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset

Abstract Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform: - Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.
Attention-Based Context Aware Network for Semantic Comprehension of Aerial Scenery

It is essential for researchers to have a proper interpretation of remote sensing images (RSIs) and precise semantic labeling of their component parts. Although FCN (Fully Convolutional Networks)-like deep convolutional network architectures have been widely applied in the perception of autonomous cars, there are still two challenges in the semantic segmentation of RSIs. The first is to identify details in high-resolution images with complex scenes and to solve the class-mismatch issues; the second is to capture the edge of objects finely without being confused by the surroundings. HRNET has the characteristics of maintaining high-resolution representation by fusing feature information with parallel multi-resolution convolution branches. We adopt HRNET as a backbone and propose to incorporate the Class-Oriented Region Attention Module (CRAM) and Class-Oriented Context Fusion Module (CCFM) to analyze the relationships between classes and patch regions and between classes and local or global pixels, respectively. Thus, the perception capability of the model for the detailed part in the aerial image can be enhanced. We leverage these modules to develop an end-to-end semantic segmentation model for aerial images and validate it on the ISPRS Potsdam and Vaihingen datasets. The experimental results show that our model improves the baseline accuracy and outperforms some commonly used CNN architectures.
How the COVID-19 made universities switch to distance education

The topic covers distance education at universities with a focus on how the COVID-19 influences the transition from face-to-face teaching to online courses. The aim is to identify non-contact teaching in education and which educational processes can be useful.
Blockchain‐based privacy‐preserving valet parking for self‐driving vehicles

Abstract The valet parking provides convenience for users and thus is becoming increasingly popular nowadays. However, this valet parking service incurs the risks of location information leakage and vehicle theft. To address these issues, in this paper, we propose a blockchain‐based privacy‐preserving valet parking protocol (B‐park) for self‐driving vehicles. The protocol guarantees the privacy of users while providing transparency and auditability of the valet parking service. We define the system model of privacy‐preserving valet parking for self‐driving vehicles in blockchain setting and formalize its security properties including anonymity, conversion blindness, nonframeability, and traceability. We provide a concrete construction of B‐park as well based on a new variant of Pointcheval‐Sanders group signature. Then, we demonstrate that our protocol is secure in the random oracle model. Finally, we evaluate the performance of B‐park to demonstrate its effectiveness and practicability.
EGGAN: Learning Latent Space for Fine-Grained Expression Manipulation

Fine-grained facial expression aims at changing the expression of an image without altering facial identity. Most current expression manipulation methods are based on a discrete expression label, which mainly manipulates holistic expression with details neglected. To handle the above mentioned problems, we propose an end-to-end expression-guided generative adversarial network (EGGAN), which synthesizes an image with expected expression given continuous expression label and structured latent code. In particular, an adversarial autoencoder is used to translate a source image into a structured latent space. The encoded latent code and the target expression label are input to a conditional GAN to synthesize an image with the target expression. Moreover, a perceptual loss and a multiscale structural similarity loss are introduced to preserve facial identity and global shape during expression manipulation. Extensive experiments demonstrate that our approach can edit fine-grained expressions, and synthesize continuous intermediate expressions between source and target expressions.
SACN: A Novel Rotating Face Detector Based on Architecture Search

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.
Cloud Task Scheduling Based on Policy Gradient Algorithm in Heterogeneous Cloud Data Center for Energy Consumption Optimization

The appearance of cloud computing has provided an efficient and economical way for users to receive computing resources with a pay-as-you-go model from cloud service provider. Cloud computing has attracted wide attention in both academia and industrial application. However, with the widely deploying of large-scale data center, high energy consumption becomes one of the key challenges and difficulties that restricting the development of cloud computing. Due to the limitations and dynamics of resources, how to map the cloud tasks to the resources aiming at decreasing the energy consumption has remained an essential issue. In this paper, we have made some improvements to the DeepJS and present DeepEnergyJS to minimize energy consumption for data center that receives enormous numbers of tasks arrive dynamically. We firstly introduce a power model to estimate the power of data center of the moment, then design a reward function that reflects the goal of optimizing power consumption and implement the simulation experiment on independent workloads to verity its availability of reducing the energy consumption. Finally, DeepJS considered only the independent workloads, we model inter-task dependencies in jobs and then conduct experiments in a similar way with independent workloads to prove that DeepEnergyJS can be extended for workloads that with dependencies which make it more functional. What's more, our experiments are based on different configured servers while the severs used in DeepJS are all the same, which make our study closer to the real cloud computing environment. The experiments results show that DeepEnergyJS can significantly outperforms three baselines in both independent workloads and workloads with dependencies.
Matroidal Entropy Functions: A Quartet of Theories of Information, Matroid, Design, and Coding

In this paper, we study the entropy functions on extreme rays of the polymatroidal region which contain a matroid, i.e., matroidal entropy functions. We introduce variable strength orthogonal arrays indexed by a connected matroid M and positive integer v which can be regarded as expanding the classic combinatorial structure orthogonal arrays. It is interesting that they are equivalent to the partition-representations of the matroid M with degree v and the (M,v) almost affine codes. Thus, a synergy among four fields, i.e., information theory, matroid theory, combinatorial design, and coding theory is developed, which may lead to potential applications in information problems such as network coding and secret-sharing. Leveraging the construction of variable strength orthogonal arrays, we characterize all matroidal entropy functions of order n≤5 with the exception of log10·U2,5 and logv·U3,5 for some v.
Procedure via cross-Kerr nonlinearities for encoding single logical qubit information onto four-photon decoherence-free states

Abstract We propose a photonic procedure using cross-Kerr nonlinearities (XKNLs) to encode single logical qubit information onto four-photon decoherence-free states. In quantum information processing, a decoherence-free subspace can secure quantum information against collective decoherence. Therefore, we design a procedure employing nonlinear optical gates, which are composed of XKNLs, quantum bus beams, and photon-number-resolving measurements with linear optical devices, to conserve quantum information by encoding quantum information onto four-photon decoherence-free states (single logical qubit information). Based on our analysis in quantifying the affection (photon loss and dephasing) of the decoherence effect, we demonstrate the experimental condition to acquire the reliable procedure of single logical qubit information having the robustness against the decoherence effect.
Affectual Dramaturgy for Augmented Reality Immersive Heritage Performance

This paper introduces the concept of Affectual Dramaturgy as a lens for creating digitally mediated immersive performances in the context of cultural heritage. In doing so we bring together two disciplines, smartglass Augmented Reality design (AR) and immersive heritage performance, with the aim to innovate experiences built for heritage sites.For both disciplines, immersive experience building uses interactive methods to engage the public with the tangible and intangible heritage of a site. Immersive heritage performance incorporates narrative-led, affective, and ludic techniques found in virtual and live immersive and participatory theatre practice. Smartglass AR experiences communicate to the viewer how a place was in the past by means of superimposing on the physical space virtual material accompanied with audio.However, often, the orchestration of the virtual material does not take the context into account and the superimposition of digital information misses an opportunity to connect into the existing narrative of the site tapping into its rich dramatic potential. This article explores how AR design and live performance can blend together through embodied storytelling techniques designed to draw the public’s attention to their sensorial experience of the site, and offer them a deeper understanding of the place’s history, which we refer to as affectual dramaturgy. By fusing embodied, affectual, and sensorial experience into the public’s engagement with the site, this article explores how interdisciplinary collaborations between theatre and AR design may create innovative tools and methods to tell stories of the past for the 21st century museums.
Text Classification of Arabic Text: Deep Learning in ANLP

Recently Natural Language Processing (NLP) has excessive attention due to increased data available online and needs processing. Nevertheless, the huge development in the NLP but still Arabic Natural Language Processing (ANLP) faces many challenges and grief from researchers' leakage compared with English NLP. Mainly this study has three divisions. It aims to find out the challenges facing ANLP, especially text classification. Furthermore, it aims to examine the effect of using deep learning in ANLP text classification from both sides' advantages and disadvantages. Moreover, it aims to find the most efficient deep learning algorithm that sufficiently classifies and categorizes an Arabic text. To fulfill the objectives of this study, three different methods have been used. The first method, Survey / Systematic Review, to find out the challenges. The second method, Literature review; to examine the effect of using deep learning algorithms to classify Arabic text. The third method, experiment to find an efficient algorithm that reverts better accuracy. In this study, we have determined the data collection methods. Where searching for good quality research papers is the key to accomplish the first two divisions. But the last division needs the full implementation to deploy Arabic text classification models. The Data Analysis explained the sub-steps extensively in each division, how it will be implemented, and each division's expected outcome. Also, this study briefly gives the resources needed and the required timeline. Deploying the models will be the longest process; hence it needs preprocessing and building models covering all types of neural networks to determine the most efficient one.
Advances in applying cloud computing techniques for air traffic systems

The rapid growth of air traffic flow has put tremendous pressure on the air traffic management (ATM) industry. The contradiction between flight flow, air traffic control service and airspace capability has become increasingly prominent, and the support capability of civil aviation transportation has attracted the social attention. In order to meet the increasing flow management requirements of ATM stakeholders, a cloud computing-based ATM flow management architecture is designed and implemented within the project of Regional Air Traffic Flow Management (ATFM) system in North China Air Traffic Management Bureau (N-ATMB). By adopting the techniques of Virtualization, distributed computing and data-driven services, the system benefits from a scalable and flexible platform architecture that ensures the ability to handle the growing volume of air traffic data. The characteristics and advantages of cloud computing techniques are compared and analyzed. This implementation has proved the high availability of the cloud-based system, the security of data storage, and the convenience of information sharing between operational systems.
Automatic Speaker Recognition using Deep Neural Network Classifiers

The advances in modern computing technologies have achieved a breakthrough in the fields of artificial intelligence (AI) and the Internet of Things (IoT). One of the major achievements in the recent history is the ability of the computer software to classify and recognize some of the objects or sounds by learning data. In this paper, we have trained the software to recognize people using their voice utterances using TIMIT Acoustic Phonetic Continuous Speech Corpus. The speaker identity is enrolled by acquiring voice samples of the speaker. Relevant features are extracted, and a model is built using the extracted feature vectors. A pattern matching classification is applied to the model using artificial neural network techniques. Speaker verification system is built using Kaldi libraries to analyze acoustic features, while x-vector training is implemented using Tensor Flow. To achieve better performance, we have implemented a combination of multiple layers of TDNN (Time Delay Neural Networks) and LSTM (Long Short-Term Memory) deep neural networks.
Word Embeddings and Neural Network Architectures for Arabic Sentiment Analysis

With the development of the new era of technology and social networks, forums, blogs, and online sales, combined with the increase of Arabs expressing their opinions, it became an urgent matter to do this research. This paper investigates different deep neural network architectures to perform Arabic sentiment classification combined with word embedding approaches. The models used are Recurrent Neural network (RNN), Bidirectional multi-layer long short-term memory (LSTM), and FastText. Different hyperparameters are used to train each model. In addition, a neural network of Multi-Layer Bidirectional Long Short-Term Memory trained on top of Glove Arabic word embedding with 1.75 billion tokens and 1.5 million words beat the existing methods.
An Effective Design to Improve the Efficiency of DPUs on FPGA

Convolutional neural networks (CNNs) have been widely used in various complicated problems, such as image classification, objection detection, semantic segmentation. To meet diversified CNN structures, the deep learning processing unit (DPU) is designed as a general accelerator on field programmable gate array (FPGA) to support various CNN layers, such as convolution, pooling, activation, etc. However, low DPU utilization and schedule efficiency appear when DPU used to multitask application completed by CNN models. In this paper, an effective design including multi-core with different size (MCDS) and DPU Plus is proposed to improve the efficiency of DPUs usage from the two dimensions of time and space. Through increasing the number of DPU cores on an FPGA and the utilization of single DPU core, the design of MCDS can effectively improve the overall throughput with restricted on-chip resources. Furthermore, the design of DPU Plus is proposed to improve the schedule efficiency of DPUs through simultaneously implementing DPU with other significant auxiliary modules of the application system on the same FPGA. Finally, a color space conversion module is implemented cooperate to the DPU cores to testify its performance, and the experimen shows that compared with running on the the CPU completely, it achieves16.2x acceleration, and increases the throughput of the entire system by 3.0x.