Reviewerly/exHarmony huggingface dataset:
Reconfigurable and Low-Complexity Accelerator for Convolutional and Generative Networks Over Finite Fields

Convolutional neural networks (CNNs) have gained great success in various fields, such as computer vision and natural language processing. Besides, with the breakthrough in unsupervised learning, generative adversarial network (GAN) is recently utilized to generate virtual data from limited data sets. The generative model of GAN has impressive applications, such as style transfer and image super-resolution. However, the promising performance of CNN and GAN comes at the cost of prohibitive computation complexity. The convolution (CONV) in CNN and the transposed CONV (TCONV) in GAN are the two operations that dominant the overall complexity. The prior works exploit the fast algorithms, Winograd and fast Fourier transform (FFT), to reduce the complexity of spatial CONV. However, Winograd only supports fixed filter size while FFT has high transform overhead. Moreover, very few works apply fast algorithms to accelerate GAN models. In this article, a reconfigurable and low-complexity accelerator on ASIC for both CNN and GAN is proposed to address these problems. First, by exploiting Fermat number transform (FNT), we propose two FNT-based fast algorithms to reduce the complexity of CONV and TCONV computations, respectively. Then the architectures of the FNT-based accelerator are presented to implement the proposed fast algorithms. The methodology to determine the design parameters and optimize the dataflow is also described for obtaining maximum performance and optimal efficiency. Moreover, we implement the proposed accelerator on 65 nm 1P9M technology and evaluate it on various CNN and GAN models. The post-layout results show that our design achieves a throughput of 288.0 GOP/s on VGG-16 with 25.11 GOP/s/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area efficiency, which is superior to the state-of-the-art CNN accelerators. Furthermore, at least $1.7\times $ speedup over the existing accelerators is obtained on GAN. The resulting energy efficiency is $275.3\times $ and $12.5\times $ of CPU and GPU.
Chinese Traditional Handicraft Education Using AR Content

This paper proposes a new Chinese traditional handicraft learning mode through embodied interaction using WebAR technology. It details how emergent technologies for cognitive learning can enhance learning in the teaching of a traditional handicraft. A study is presented where students learn about traditional handicrafts with a whole-body interactive simulation utilizing WebAR devices, and this is compared to traditional methods using a desktop version for the simulation. Results show that the Chinese traditional handicraft learning mode based on WebAR affords embodied interaction and improves learning between real context and virtual devices; thus WebAR devices enhance embodied interaction of traditional handicraft simulations. This contribution is highly relevant for users who aim to learn and be creative in different contexts that deliver new forms of Chinese traditional handicraft learning.
On the design of a framework integrating an optimization engine with streaming technologies

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.
Analysis Accuracy of Random Forest Model for Big Data – A Case Study of Claim Severity Prediction in Car Insurance

Insurance claim is one of the important elements in the field of insurance services. Claim severity refers to the amount of fund that must be spent to repair the damage. The amount of insurance claim is influenced by many factors. This causes the volume of data to be very large. Therefore, a suitable method is required. Random Forest, one of the machine learning methods can be implemented to handle this problem. This thesis applies the Random Forest model to predict the amount of this claim severity on car insurance. Furthermore, analysis of the effect of the number of features used on model accuracy is conducted. The simulation result shows that the Random Forest model can be applied in cases of prediction of claim severity, which is a case of regression in the context of machine learning. Only by using 1/3 of the overall features, the accuracy of the Random Forest model can produce accuracy that is comparable to that obtained when using all features which is around 99%. This result confirms the scalability of Random Forest, especially in terms of the number of features. Hence, the Random Forest model can be used as a solution to Big Data problems related to data volume.
Image Classification Based on Mid-Level Feature Fusion

Image classification and analysis aim to classify the images according to the nature of visual contents. Due to recent increase in the number of multi-media contents, the classification of images is considered as a challenging and complex problem. A list of low-level and mid-level features is available in the literature that aims to represent the images in the form of feature vectors to be used as an input for classification-based problems. The main problem with these features is the domain and application specific nature and the use of features in one domain may not show the same result when applied in a different domain. The feature fusion-based approaches aim to enhance the performance of image classification models as single feature-based approach is not robust to handle image transformations such as translation, rotation, scaling, etc. In this research, we aim to investigate the image classification-based performance of mid-level features. We applied Bag of Visual Words (BoVW) model with image classification framework. This proposed late feature fusion result in a higher classification accuracy. The features are retrieved from the images by using two well-known feature extraction techniques that are Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG). The experiments are performed while using two data sets that are Corel-1k and Corel-1.5k. The result shows that the classification accuracy increases when SIFT and HOG are used in fusion and the proposed results outperforms the standard BoVw model when SIFT and HOG is used separately.
Monte Carlo co-ordinate ascent variational inference

Abstract In variational inference (VI), coordinate-ascent and gradient-based approaches are two major types of algorithms for approximating difficult-to-compute probability densities. In real-world implementations of complex models, Monte Carlo methods are widely used to estimate expectations in coordinate-ascent approaches and gradients in derivative-driven ones. We discuss a Monte Carlo co-ordinate ascent VI (MC-CAVI) algorithm that makes use of Markov chain Monte Carlo (MCMC) methods in the calculation of expectations required within co-ordinate ascent VI (CAVI). We show that, under regularity conditions, an MC-CAVI recursion will get arbitrarily close to a maximiser of the evidence lower bound with any given high probability. In numerical examples, the performance of MC-CAVI algorithm is compared with that of MCMC and—as a representative of derivative-based VI methods—of Black Box VI (BBVI). We discuss and demonstrate MC-CAVI’s suitability for models with hard constraints in simulated and real examples. We compare MC-CAVI’s performance with that of MCMC in an important complex model used in nuclear magnetic resonance spectroscopy data analysis—BBVI is nearly impossible to be employed in this setting due to the hard constraints involved in the model.
Adaptation of Higher Medical Education to the Conditions of Digitalization of Healthcare

The aim of the work is to study the features of digitalization in medicine, healthcare, professional education, and to determine the directions of their further adaptation to the requirements of the time on the example of the medical university. The provision is based on the use of methods such as content analysis of modern publications on the problem, their logical analysis, generalization and systemization of published information, as well as the presentation of the experience of digitalization in the higher medical education. The article shows the relevance of digitalization for health care, with which it is possible to optimize the tasks of managing the quality and accessibility of medical care. They require a quick quantitative assessment using modern information technology, mathematical methods, software, big data analysis, and other technology tools. Content in digitalization in healthcare also includes the creation of innovative services and platforms that enable remote communication with patients, doctors, medical and pharmaceutical services, and the use of monitoring of medical and preventive processes in medical organizations. The digitalization of higher education is one of the areas of innovative policy of universities, implemented in accordance with the requirements and conditions of the external environment and own funds. Training of professionals with the right knowledge and digital technology requires appropriate educational information resources for educational and scientific processes in universities. To do this, the electronic information education environment (EIEE) is formed as a set of information, telecommunications and technology tools. The experience of creating EIEE at Kursk State Medical University over the past decades is presented. Various forms and methods of learning and knowledge control are being computerized. The university has subdivision management systems (dean's offices, departments, library, staff, admissions committees, educational and methodological management and others). Many continuing medical education programs at the Faculty of Postgraduate Education are being implemented remotely. Methods and forms of practice-oriented and project-based training using information technology are being developed.
Are People Sensitive to Problems in Communication?

Abstract Recent research indicates that interpersonal communication is noisy, and that people exhibit considerable insensitivity to problems in communication. Using a dyadic referential communication task, the goal of which is accurate information transfer, this study examined the extent to which interlocutors are sensitive to problems in communication and use other‐initiated repairs (OIRs) to address them. Participants were randomly assigned to dyads ( N = 88 participants, or 44 dyads) and tried to communicate a series of recurring abstract geometric shapes to a partner across a text–chat interface. Participants alternated between directing (describing shapes) and matching (interpreting shape descriptions) roles across 72 trials of the task. Replicating prior research, over repeated social interactions communication success improved and the shape descriptions became increasingly efficient. In addition, confidence in having successfully communicated the different shapes increased over trials. Importantly, matchers were less confident on trials in which communication was unsuccessful, communication success was lower on trials that contained an OIR compared to those that did not contain an OIR, and OIR trials were associated with lower Director Confidence. This pattern of results demonstrates that (a) interlocutors exhibit (a degree of) sensitivity to problems in communication, (b) they appropriately use OIRs to address problems in communication, and (c) OIRs signal problems in communication.
Numerical Modeling of Bifacial PV String Performance: Perimeter Effect and Influence of Uniaxial Solar Trackers

The bifacial photovoltaic (PV) systems have recently met large interest. The performance of such systems heavily depends on the installation conditions and, in particular, on the albedo radiation collected by the module rear side. Therefore, it is of crucial importance to have an accurate performance model. To date, in the scientific literature, numerous models have been proposed and experimental data collected to study and optimize bifacial PV system performance. Currently, 3D and 2D models of bifacial PV devices exist. Though the former are more mathematically complex, they can lead to more accurate results, since they generally allow to fully consider the main aspects influencing a bifacial PV system performance. Recently, we have proposed and validated through experimental data a 3D model tested as a function of module height, tilt angle, and ground albedo. In this work, through such a model, we studied the role played by the perimeter zones surrounding the PV string, by considering PV strings of 30 or 60 modules. We considered the cases of fixed installation with optimal PV module tilt and of installation with uniaxial horizontal solar tracker. We evaluated the PV energy yield as a function of the size of the perimeter zones for the two cases, i.e., both with and without the solar tracker. In optimal perimeter conditions, we then studied the behavior of bifacial and mono-facial PV strings by varying the geographical location in a large latitude range.
Hierarchical multi-task learning with CRF for implicit discourse relation recognition

Implicit discourse relation recognition (IDRR) remains an ongoing challenge. Recently, various neural network models have been proposed for this task, and have achieved promising results. However, almost all of them predict multi-level discourse senses separately, which not only ignores the semantic hierarchy of and mapping relationships between senses, but also may result in inconsistent predictions at different levels. In this paper, we propose a hierarchical multi-task neural network with a conditional random field layer (HierMTN-CRF) for multi-level IDRR. Specifically, a HierMTN component is designed to jointly model multi-level sense classifications, with these senses as supervision signals at different feature layers. Consequently, the hierarchical semantics of senses are explicitly encoded into features at different layers. To further exploit the mapping relationships between adjacent-level senses, a CRF layer is introduced to perform collective sense predictions. In this way, our model infers a sequence of multi-level senses rather than separate sense predictions in previous models. In addition, our model can be easily constructed based on existing IDRR models. Experimental results and in-depth analyses on the benchmark PDTB data set show that our model achieves significantly better and more consistent results over several competitive baselines on multi-level IDRR, without additional time overhead.
Granular Aggregation of Fuzzy Rule-Based Models in Distributed Data Environment

Quite often, complex systems or phenomena are observed from various points of view yielding the particular subsets of data usually being composed of locally available attributes. Such datasets give rise to individual models. As is reflective of the local behavior of the system (global data), each model can produce different, albeit similar results. A critical issue is to aggregate the results coming from the individual models. In virtue of the diversity of the produced results, the aggregation process has to be reflective of this variety. Equally important is a way of quantifying the diversity of the individual results. In this article, we provide an efficient and original way of aggregation of the results by engaging a principle of justifiable granularity and in this manner leading to interval-valued results summarizing the results produced by a collection of models. We develop an overall design process and discuss the associated optimization mechanism leading to a granular fuzzy model of a global nature. The detailed scheme of the principle of justifiable granularity is discussed along with the related performance indexes; in particular, two modes of design of information granules are investigated. The quality of the granular model is quantified with the aid of the criteria of coverage and specificity.
Reliable Knowledge Graph Path Representation Learning

Knowledge graphs, which have been widely utilized in various intelligent applications, are highly incomplete.Many valid facts can be inferred from existing facts in knowledge graphs.A promising approach for this task is a knowledge graph representation learning, which aims to represent entities and relations into low-dimensional vector spaces.Most of the existing methods mainly focus on direct relationships between entities and do not reflect the semantics of multi-hop relation paths.Although a few methods have studied the problem of multi-hop path-based representation learning, they fail to distinguish reliable relation paths among a majority of meaningless relation paths.In this paper, we propose a reliable path-based knowledge graph representation learning method, called RKRL.Specifically, we combine the representations of intermediate entities and relations on relation paths to learn more meaningful knowledge representations.Also, we present a reliable knowledge graph path ranking method to avoid the unnecessary computation of unreliable paths and find semantically valid relation paths.Experimental results on benchmark datasets show that our method achieves consistent improvement on typical evaluation tasks for knowledge representations, compared with the classical and state-of-the-art representation learning baselines.
Variance-Reduced Stochastic Learning Under Random Reshuffling

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform data sampling with replacement. However, it has been observed in related works that random reshuffling can deliver superior performance over uniform sampling and, yet, no formal proofs or guarantees of exact convergence exist for variance-reduced algorithms under random reshuffling. This paper makes two contributions. First, it provides a theoretical guarantee of linear convergence under random reshuffling for SAGA in the mean-square sense; the argument is also adaptable to other variance-reduced algorithms. Second, under random reshuffling, the article proposes a new amortized variance-reduced gradient (AVRG) algorithm with constant storage requirements compared to SAGA and with balanced gradient computations compared to SVRG. AVRG is also shown analytically to converge linearly.
Composite optimization for the resource allocation problem

In this paper, we consider resource allocation problem stated as a convex minimization problem with linear constraints. To solve this problem, we use gradient and accelerated gradient descent applied to the dual problem and prove the convergence rate both for the primal iterates and the dual iterates. We obtain faster convergence rates than the ones known in the literature. We also provide economic interpretation for these two methods. This means that iterations of the algorithms naturally correspond to the process of price and production adjustment in order to obtain the desired production volume in the economy. Overall, we show how these actions of the economic agents lead the whole system to the equilibrium.
Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering

Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio (SNR) enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering (ASMMF) is proposed to reconstruct chaotic signals. In the process of noise reduction for contaminated chaotic signals, firstly, a multiscale morphological filter is constructed adaptively according to the multiscale permutation entropy (MPE) of morphological filter residuals, and the contaminated signals are filtered. Secondly, the weight coefficients of scale structural element are calculated by the residual sum of squares operation, and the chaotic signals are reconstructed. Finally, the resampled filter signals are smoothed by cubic B-spline interpolation operation. In the experiment, the Lorenz signal with white Gaussian noise, the measured sunspot, and the chaotic vibration signal are reconstructed by four comparison methods. The test results show that the proposed ASMMF method has obvious advantages in noise suppression and topological trajectory restoration.
A Novel Convolutional Neural Network Accelerator That Enables Fully-Pipelined Execution of Layers

In this paper, we propose a novel CNN accelerator, called MIDAP, aiming to maximize the utilization of MAC units by enabling fully-pipelined execution of layers. To this end, MIDAP adopts two-level pipelining, macro pipelining and micro pipelining, and large on-chip SRAMs. The macro pipeline consists of three modules for convolution, activation, and pooling layer and each module accesses separate memory units without access conflict. For micro-pipelining inside the convolution module, the datapath is designed to be free from dynamic resource contention. Also, the inter-layer feature map reuse is maximized by compile-time analysis. From the simulation results on 1GHz frequency, MIDAP shows the remarkable end-to-end performance of several well-known CNNs: for instance, 144 fps for Inception V3 model and 892 fps for Mobilenet V1 model with 1024 MACs. A high-level synthesis result reveals that our accelerator is able to achieve about 2.0 TOPs/W with a small area less than 2mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with 8nm CMOS technology, thanks to its simple datapath.