Reviewerly/exHarmony huggingface dataset:
The design of biaxial solar tracking with a smart house model

In this study, it is aimed to determine and reveal a viable two-axis solar monitoring system that produces higher voltage output than a fixed panel. After intensive research, a small-scale solar panel, servo motors, arduino processor as controller, and a two-axis solar tracker using a transmitter wirelessly transmitting voltage feedback were carried out. In addition, a 'Smart House' model that makes our current lives safer and more practical has been investigated. Remote control of the smart home system has been analyzed. For remote control of the smart home model, the IoT (Internet of Things) structure uses the MQTT Protocol, NodeMCU (ESP8266) Control Card and sensors of different features to build this structure. Signals from sensors are directed to MQTT Broker with the help of the NodeMCU (ESP8266) Control Card. Phone control is provided through the mobile app, which is written using QML and C++ languages in the QT Creator IDE.
Three musketeers of a quantum channel: Backlog, delay, and throughput

Quantum EngineeringVolume 2, Issue 4 e54 RESEARCH HIGHLIGHT Three musketeers of a quantum channel: Backlog, delay, and throughput Liu-Guo Yin, Corresponding Author yinlg@tsinghua.edu.cn School of Information Science and Technology, Tsinghua University, China Correspondence Liu-Guo Yin, School of Information Science and Technology, Tsinghua University. Email: yinlg@tsinghua.edu.cnSearch for more papers by this author Liu-Guo Yin, Corresponding Author yinlg@tsinghua.edu.cn School of Information Science and Technology, Tsinghua University, China Correspondence Liu-Guo Yin, School of Information Science and Technology, Tsinghua University. Email: yinlg@tsinghua.edu.cnSearch for more papers by this author First published: 30 September 2020 https://doi.org/10.1002/que2.54Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2, Issue4December 2020e54 RelatedInformation
Enhancing Textual Representation for Abstractive Summarization: Leveraging Masked Decoder

For existing models of abstractive summarization, the paradigm of autoregressive decoder inherently prefers relying on former tokens and the prediction error will propagate subsequently. To effectively eliminate the errors, we need a way to remodeling dependency during text generation. In this paper, we introduce MDSumma (as shorthand for Masked Decoder for Summarization), which masks partial tokens in decoder, aiming to alleviate the over-reliance on the antecedent. Moreover, with further facilitating the flexibility and diversity of textual representation, we employ a variational autoencoder model, sampling continuous latent variables from the probability distribution to explicitly model underlying semantics of the target summaries. Our architecture gives good balance between encoder contextual representation and decoder prediction, sidestepping the gap between training and inference. Experimental results on three benchmark datasets validate the effectiveness that our proposed method significantly outperforms the existing state-of-the-art approaches both on ROUGE and diversity scores.
Subsystem Inheritance and Composition in Complex Systems

Inheritance and composition are fundamental features of object-oriented programming. Could it be possible to reuse these concepts at a more complex, more aggregated level? My long-time experience with enterprise scale systems indicate that it is not only possible, but it brings major benefits to architect systems of a given complexity by applying inheritance and composition to applications' larger building blocks: the subsystems. Existing concepts and implementations of dependency injection or SOA leverage such an approach and empower it, even if the most common computer languages do not provide direct support for it. To prove the sustainability of this assumption, I designed and developed a framework to act as a foundation for complex systems, containing composable and inheritable subsystems. The success of applications built on it sets the basis of further refinements and extensions.
Estimation of Greenhouse Gas Emissions in Cement Manufacturing Process Through Blockchain and SSL Based IoT Data Analysis

Recently, the Internet of Things (IoT) system, which supports human activities based on various types of real data, has attracted attention in various fields. However, the behavior of the system is determined based on the actual data, so if an attacker changes the data, a critical problem can occur on the system. Therefore, to ensure the reliability of real data, many researchers have studied data management platforms that use security management technology. In the proposed platform, the pre-manufacturing phase of the cement manufacturing process attempted to ensure the reliability of the generated data by having data produced using blockchain techniques through the distributed ledger of blockchain. In the manufacturing phase, Secure Socket Layer (SSL), also known as digital certificates, is also used to establish encrypted connections between the browser or user’s computer and the server or website. SSL connections protect sensitive data exchanged in each session from interference from unauthorized users. Design a management platform that can verify the integrity of stored data over proposed SSL and block chain. Testers collected in real time from the system provide integrity for the calculation of carbon emissions. You will have the opportunity to drastically reduce the manpower and time funds required for the current first and second stages of verification.
SoftMon

Over the past two decades, a rich ecosystem of open-source software has evolved. For every type of application, there are a wide variety of alternatives. We observed that even if different applications that perform similar tasks and compiled with the same versions of the compiler and the libraries, they perform very differently while running on the same system. Sadly prior work in this area that compares two code bases for similarities does not help us in finding the reasons for the differences in performance.
A Manifold-Based Dimension Reduction Algorithm Framework for Noisy Data Using Graph Sampling and Spectral Graph

This paper proposes a new manifold-based dimension reduction algorithm framework. It can deal with the dimension reduction problem of data with noise and give the dimension reduction results with the deviation values caused by noise interference. Commonly used manifold learning methods are sensitive to noise in the data. Mean computation, a denoising method, is an important step in data preprocessing but leads to a loss of local structural information. In addition, it is difficult to measure the accuracy of the dimension reduction of noisy data. Thus, manifold learning methods often transform the data into an approximately smooth manifold structure; however, practical data from the physical world may not meet the requirements. The proposed framework follows the idea of the localization of manifolds and uses graph sampling to determine some local anchor points from the given data. Subsequently, the specific range of localities is determined using graph spectral analysis, and the density within each local range is estimated to obtain the distribution parameters. Then, manifold-based dimension reduction with distribution parameters is established, and the deviation values in each local range are measured and further extended to all data. Thus, our proposed framework gives a measurement method for deviation caused by noise.
The General Shape Analysis applied for coarse classification of teeth shapes

In the paper we investigate an application of the General Shape Analysis approach to the coarse classification of teeth shapes. The main purpose of the General Shape Analysis is to find one or a few general templates with the highest similarity for an investigated object. This allows to obtain the most basic information about an object and is useful in the case of large amount of data, missing data or when classes are not evident. In this paper various shape description algorithms are applied to the problem of General Shape Analysis, namely the Two-Dimensional Fourier Descriptor, Generic Fourier Descriptor, UNL-Fourier Descriptor, Point Distance Histogram and Zernike Moments. The original description vectors are also investigated after reducing with the use of various approaches, among which are the Principal Component Analysis and Discrete Cosine Transform. We examine some methods using an original database for the General Shape Analysis. Then the selected methods are applied for the recognition of teeth shapes.
Efficient Enumeration of Distinct Factors Using Package Representations

We investigate properties and applications of a new compact representation of string factors: families of packages. In a string T, each package \((i,\ell ,k)\) represents the factors of T of length \(\ell \) that start in the interval \([i,i+k]\). A family \(\mathcal {F}\) of packages represents the set \(\mathsf {Factors}(\mathcal {F})\) defined as the union of the sets of factors represented by individual packages in \(\mathcal {F}\). We show how to efficiently enumerate \(\mathsf {Factors}(\mathcal {F})\) and showcase that this is a generic tool for enumerating important classes of factors of T, such as powers and antipowers. Our approach is conceptually simpler than problem-specific methods and provides a unifying framework for such problems, which we hope can be further exploited.We also consider a special case of the problem in which every occurrence of every factor represented by \(\mathcal {F}\) is captured by some package in \(\mathcal {F}\). For both applications mentioned above, we construct an efficient package representation that satisfies this property.We develop efficient algorithms that, given a family \(\mathcal {F}\) of m packages in a string of length n, report all distinct factors represented by these packages in \(\mathcal {O}(n\log ^2 n+m\log n+|\mathsf {Factors}(\mathcal {F})|)\) time for the general case and in the optimal \(\mathcal {O}(n+m+|\mathsf {Factors}(\mathcal {F})|)\) time for the special case. We can also compute \(|\mathsf {Factors}(\mathcal {F})|\) in \(\mathcal {O}(n \log ^2 n + m \log n)\) time in the general case and in \(\mathcal {O}(n+m)\) time in the special case.In particular, we improve over the state-of-the-art \(\mathcal {O}(nk^4\log k \log n)\)-time algorithm for computing the number of distinct k-antipower factors, by providing an algorithm that runs in \(\mathcal {O}(nk^2)\) time, and we obtain an alternative linear-time algorithm to enumerate distinct squares.
The Collaborative Influence of Multiple Interactions on Successive POI Recommendation

With the rapid development of social networks, users hope to obtain more accurate and personalized services. In general, POI recommendation often uses the historical behaviors of a user to recommend the top N POIs and rarely consider the current state of the user. Unlike POI recommendation, successive POI recommendation is more sensitive to user preferences and changes in time and space. In order to alleviate the data sparsity, we make full use of the interaction of time, space and user interest preferences, and propose a successive POI recommendation model called UTeSp. The UTeSp model uses the collaborative influence of multiple interactions to build a model, which can well adapt to the needs of users at different times and different locations. And it can change dynamically. Furthermore, we associate the user's inherent interest preference with the user's friend's influence on the target user, and propose a user-level interest preference based on attention mechanism, which can obtain more accurate user preference results. In addition, a novel TDP_HC algorithm is designed to segment time dynamically. Based on the partial order relationship, we propose two interpretable methods to enhance the learning ability of the model. The two methods can be used in other similar successive POI recommendation models. Experimental results show that the F1-score of UTeSp model on the two real datasets is better than that of several mainstream successive POI recommendation models, and the two partial order methods also show the effectiveness of our model.
IM-ACS: An Access Control Scheme Supporting Informal Non-malleable Security in Mobile Media Sharing System

Subscribers own their subscription credentials when they accesses mobile media. They cannot extend their subscription credentials without permission. We propose an Access Control Scheme supporting Informal non-Malleable security (IM-ACS) for mobile media. In our scheme, Attribute Based Encryption method is applied to bring de-couple and fine-grain features when publishers share their media data with subscribers. All-or-Nothing transfer method is used to ensure the efficiency when publishers encrypt their media data before upload it onto cloud server. The method of re-encryption on cloud is adopted to ensure that subscribers cannot obtain any acknowledge from the previous data accesses unless publisher allow them to. This scheme is analyzed to be secure.
Generating a Concept Relation Network for Turkish Based on ConceptNet Using Translational Methods

ConceptNet is a large-scale network of concepts and relationships, based on various common sense knowledge bases. Turkish is a language that lacks similar resources for processing texts and extracting meaning. This study discusses various methods to create a Turkish ConceptNet using translational techniques based on English ConceptNet and explains the results herewith obtained. Multiple models were tested, using different knowledge sources and tools including WordNet, Wikipedia, and Google Translate. Results obtained from each model and the approaches to improve these results are discussed, while also explaining details, assumptions, and drawbacks relevant to each relation.
KISS: an EBM-based approach for explaining deep models

The application of deep learning in data mining and knowledge extraction becomes more and more extensive, yet people often criticize deep models for its ”black box” trait. In this paper, we propose a simple algorithm for the model explanation by using the energy-based model theory and subset sampling. This approach can be model-agnostic without knowing the model architecture or be hybrid with the information inside the target model (e.g. the gradients). Through two image classification models, we compared our algorithm with other interpretation methods by testing the effects on the predictions and got an encouraging result.
A Hierarchical-Tree-Based Method for Generative Zero-Shot Learning

It is currently a popular practice to use the class semantic information and the conditional generative adversarial network (CGAN) technique to generate visual features for the unseen classes in zero-shot learning (ZSL). However, there is currently no good ways to ensure that the generated visual features can always be beneficial to the prediction of the unseen classes. To alleviate this problem, we propose a hierarchical-tree-based method for constraining the generation process of CGAN, which can tune the generated visual features based on the multi-level class information. Moreover, to enhance the mapping ability of the model from the visual space to the semantic space, we add a multi-expert module to the traditional single mapping channel, which helps the model to mine the mapping relationship between the visual space and the semantic space. Extensive experimental results on five benchmark data sets show that our method can achieve better generalization ability than other existing generative ZSL algorithms.
Research on Scene Semantic Segmentation Based on Deep Learning

Because of the problem of the low accuracy and slow speed of the traditional semantic segmentation model, making it difficult to actually use. In response to this problem, this paper focuses on the method to improve the precision and speed of the algorithm. According to this theory, based on the convolution neural network, we have designed the PSPNet and ICNet models. Meanwhile, a scene semantic segmentation network based on deep learning was presented. The network effectively improves the accuracy of semantic segmentation of convolutional neural networks by merging multi-level depth and network features. The test results on the LISA traffic sign data set show that the proposed semantic segmentation network has outstanding performance compared with other state of the art semantic segmentation network structures.
Bayesian Estimation of State-Space Models and Particle MCMC

We consider again the problem of estimating the parameter θ of a state-space model, this time from a Bayesian viewpoint. That is, we view θ as the realisation of a random variable Θ, with prior distribution ν(dθ), and we wish to compute the distribution of Θ conditional on data y0:T. This conditional distribution is called the posterior distribution, and represents the remaining uncertainty regarding the value of θ once data has been observed.Posterior distributions are often intractable, and are commonly approximated by MCMC sampling. We discuss the various ways MCMC may be used to sample from the posterior distribution of a state-space model. We give particular emphasis to PMCMC (particle MCMC), a general methodology that involves running a particle filter at each MCMC iteration. Compared to vanilla MCMC (e.g. Gibbs sampling), the PMCMC methodology has the advantages of being more generally applicable, and showing better performance.Notation/Terminology Recall that since Chap. 14 we have reinstated θ in the kernels, emission densities, etc., hence write \(p_t^\theta (x_t|x_{t-1})\), \(f_t^\theta (y_t|x_{t})\), etc., a notation we first introduced in Chap. 2. In this chapter we will also deal with densities marginalised over a prior density ν(dθ), so whereas pθt(y0:t) denotes the density of the observed data conditionally on the parameters with the states integrated out in a state-space model, pt(y0:t) will denote averaged quantity ∫θ ∈ Θpθt(y0:t)ν(dθ).
A Loopless Algorithm for Generating (k, m)-ary Trees in Gray-Code Order

Introduced by Du and Liu in 2007, a (k, m)-ary tree is a generalization of k-ary tree such that the nodes have degree k on even levels and the nodes have degree 0 or m on odd levels. Especially, every node with the degree m on odd levels is called a crucial node. In a (k, m)-ary tree of order n, there are exactly n crucial nodes. A loopless algorithm is an algorithm for generating combinatorial objects using only assignment and comparison statements and does not include loop structure or recursion. In this paper, we propose a loopless algorithm to generate (k, m)-ary trees of order n in Gray-code order using Z-sequence suggested by Zaks in 1980. The required memory space of our algorithm is \(2n+\mathcal {O}(1)\). Moreover, we analyze both amortized costs of assignment statements and comparison statements, which are no more than \(3+\frac{3}{km}\) and \(2.5+\frac{2}{km}\), respectively.
Efficient Updates for Data Association with Mixtures of Gaussian Processes

Gaussian processes (GPs) enable a probabilistic approach to important estimation and classification tasks that arise in robotics applications. Meanwhile, most GP-based methods are often prohibitively slow, thereby posing a substantial barrier to practical applications. Existing "sparse" methods to speed up GPs seek to either make the model more sparse, or find ways to more efficiently manage a large covariance matrix. In this paper, we present an orthogonal approach that memoises (i.e. reuses) previous computations in GP inference. We demonstrate that a substantial speedup can be achieved by incorporating memoisation into applications in which GPs must be updated frequently. Moreover, we derive a novel online update scheme for sparse GPs that can be used in conjunction with our memoisation approach for a synergistic improvement in performance. Across three robotic vision applications, we demonstrate between 40-100% speed-up over the standard method for inference in GP mixtures.
PROGRESSING WITH EXTENDED REALITY

Extended reality is a cutting-edge technology that has revitalized the process people have experienced the real and virtual environment, by combining the three phenomena like virtual reality (VR), augmented reality (AR) and mixed reality (MR), and their escalation and convergence. It has fueled up our imagination beyond our limits. The term parasol used for XR to define entrancing technologies that can combine the real and virtual worlds. HoloLens by Microsoft that overlap digital information onto a users view field is an AR, and Oculus Rift is a headset that allows users to exposure to and communicate with a 3D world that does not really exist by fooling the brain is a type of virtual reality. The adoption of XR technology is slower than it has to be because of all the uncertainty that overshadow this inceptive technology. This study identifies and analyzes few articles through literature search to strengthen and review this emerging field in IS.
Chinese-Russian Shared Task on Multi-domain Translation

We present the results the first shared task on Machine Translation (MT) from Chinese into Russian, which is the only MT competition for this pair of languages to date. The task for participants was to train a general-purpose MT system which performs reasonably well on very diverse text domains and styles without additional fine-tuning. 11 teams participated in the competition, some of the submitted models showed reasonably good performance topping at 19.7 BLEU.
Development Of New Fractal And Non-Fractal Deep Residual Networks For Deblocking Of Jpeg Decompressed Images

The JPEG compression scheme introduces blocking artifacts when the images are decompressed. JPEG image deblocking schemes based on deep neural networks map a JPEG decompressed image to its corresponding deblocked image. Employing a residual block that is capable of generating a rich set of high frequency residual features in a deep JPEG image deblocking network can improve its representational capability, and therefore, enhance the network performance. In this paper, we propose two residual blocks that generate rich high frequency residual features. The first residual block generates features from the high frequency component of its input signal in addition to generating conventional hierarchical residual features using convolutional operations. The second one is a fractal residual block that is developed by replacing the conventional convolutions in the first block by the block itself. The two proposed residual blocks are, respectively, used in recursive (non-fractal) and non-recursive (fractal) neural networks for the task of JPEG deblocking. The results of the experiments performed on the two proposed deblocking networks show their performance superiority over the respective state-of-the-art deblocking networks.