Reviewerly/exHarmony huggingface dataset:
MrPC: Causal Structure Learning in Distributed Systems

PC algorithm (PC) – named after its authors, Peter and Clark – is an advanced constraint based method for learning causal structures. However, it is a time-consuming algorithm since the number of independence tests is exponential to the number of considered variables. Attempts to parallelise PC have been studied intensively, for example, by distributing the tests to all computing cores in a single computer. However, no effort has been made to speed up PC through parallelising the conditional independence tests into a cluster of computers. In this work, we propose MrPC, a robust and efficient PC algorithm, to accelerate PC to serve causal discovery in distributed systems. Alongside with MrPC, we also propose a novel manner to model non-linear causal relationships in gene regulatory data using kernel functions. We evaluate our method and its variants in the task of building gene regulatory networks. Experimental results on benchmark datasets show that the proposed MrPCgains up to seven times faster than sequential PC implementation. In addition, kernel functions outperform conventional linear causal modelling approach across different datasets.
Augmented Reality as an Educational Resource Applied to the Teaching of Pre-Columbian Cultures Settled in the Pumapungo Archaeological Park

The objective of the project focuses on the use of Augmented Reality and free software applied to a playful, puzzle-like game that serves as didactic support in the teaching and diffusion of archaeological patrimony in museums. For the study case, we choose The Pumapungo Museum and The Archaeological Park located in the city of Cuenca, Ecuador, which have an important archaeological and patrimony reserve and are also located on one of the most important Inkas settlements in the country. According to its statistics, around 164,000 thousand tourists visit it annually, and 14.64% of them are children; therefore, based on these data, the defined audience of users for the study were primary school children between 6 and 8 years of age. As a pedagogical methodology, the learning theories in education were used, by Seymour Papert (Constructivism) and John Dewey (Learning by doing), tools that have improved the learning process through exploration and experience. Likewise, we worked with formal and non-formal learning schemes that allow improving the interaction of children with the museum environments, reinforcing the knowledge from the guided visits. The results show that the use of Augmented Reality in the learning process arouses the interest and curiosity of children in a given topic because it allows them to get involved in real time in historical contexts and therefore learn about the material and intangible heritage or patrimony of their country. The research and development of the project was carried out in 2015, so the technological and bibliographic references correspond to that date.
Auto-scaling Compute and Network Resources in a Data-center

This paper focusses on auto-scaling network and compute resources using an intermediary enterprise that uses resources from a public cloud to provide a virtual private cloud to a single client enterprise. The primary goal of the auto-scaling technique is to achieve a profit for the intermediary enterprise while satisfying service level agreements associated with client requests as well as maintaining the desired grade of service for the client enterprise. A secondary goal is to minimize the east-west traffic in the Data Center by reducing the number of network links utilized. Network bandwidth and compute resources are reserved to service the requests from a broker. This paper presents an autoscaling algorithm and includes a discussion of system design. A simulation-based performance analysis is presented to demonstrate the effectiveness of the proposed technique. auto-scaling, resource allocation, dynamic resource provisioning, scheduling with SLAs, resource management on clouds.
Recognition of Named Entities and Categories in Text using Stacked Embeddings

Named entities enable the identification of key elements in text while sentence classification provides for a summary of the same. Sequential labeling and sentence classification tasks together enable deeper extraction of information from text. Embeddings trained over a corpus pertaining to a specific domain, tend to generate strong vector representations thereby providing for the creation of better classification models. We propose custom fastText embeddings trained on a large Indian English news corpus. These embeddings are stacked with state-of-the-art Pooled Flair embeddings to generate an f1-score of 79 on a custom FIRE English NER dataset and 93.05 f1-score on a subset of the OntoNotes 5.0 dataset. The embeddings were also used for sentence classification on 20 news categories, to generate the best multi-class accuracy of 88.1%. We also propose two Indian news datasets, one based on the FIRE NER dataset and a custom multi-class sentence classification dataset.
Simultaneous Inpainting and Colorization via Tensor Completion

Both image inpainting and colorization can be considered as estimating certain missing pixel values from a given image, which is still a challenging problem in image processing. In fact, it is a more challenging problem to make image inpainting and colorization simultaneously, that is, given a corrupted gray-scale image and a few color pixels, we try to restore the original color image. In this paper, we propose a novel tensor completion model to solve the problem of simultaneous inpainting and colorization. Moreover, it can be applied to each of inpainting and colorization tasks separately as a special case. Experimental results on test images demonstrate that our proposed model is effective and efficient for simultaneous inpainting and colorization task and outperforms the state-of-the-art inpainting methods.
MYTHOLOGICAL MEANING OF TV SERIES AS AN IMPLEMENTATION OF THE FUNCTIONALITY OF MODERN MEDIA

The article is devoted to the problem of ambiguity in the assessment of the mythological characteristics of the TV series. The purpose of the article is to establish functions inherent in the TV series, which reveal its mythological potential. Methodological approach of post-structuralism was applied to various cultural forms as texts to be interpreted, therefore, TV series were considered as signifiers representing mythological significance. The audience usually perceives TV series as the traditional myth, emotionally experiencing it as a holistic semantic system in which meaning is inextricably linked to the form, therefore it is perceived as a reality. It is established that the regular repetition of the series in the broadcast TV networks demonstrates its paradigmatic level of signification and gives it a normative function. The novelty of the study is to identify the functional strength of the mythology of TV series, which consists not only in creating an atmosphere of aesthetic exchange of values in the communicative process but also in the norming of living space (symbolic-normative function), creating a virtual space for fantasy experience of unconscious meanings and desires (social-compensatory function) and socialization of individuals (integration function). We can conclude that the mythology of the series can be enhanced in two ways – due to the normative-ritual function of series, which is achieved by regular repetition, and, in turn, affects the integrative function and due to the fantasy content of the TV series. This feature largely reveals the socio-compensatory function of the TV series, which is most realized in fantasy series, and given that the spectator is experiencing their own desires, we can talk about the experience of individualized myth.
Network marks of montage in audio recordings

Abstract Very often forensic domain processes required an approval of authenticity in audio recordings presented as admissible evidence. Standard techniques to search for editing in audio materials are rather long and wearisome. The paper proposes a network platform as an effective instrument for solving the above mentioned problem. A specialized software tool was developed to transform given audio data into set of nodes and links according to the algorithms of natural visibility graph and horizontal visibility. A comparative analysis of the derived network structures was performed with the use of popular Gephi software product. The results demonstrate the first advances of network paradigm for detection of audio montage, in addition the examples of trivial signals of those point on possible existence of a marker - the metric that responds to sound recordings tampering.
Real-time 4-way Intersection Smart Traffic Control System

Since traffic congestion is becoming a regular part of commuters' life, there is a pressing need for better traffic management. Most current traffic control systems are not sensitive to the current state of the roads being controlled, instead they are fixed, timed traffic signals that do not respond to unpredicted congestion. Solutions have been proposed to solve this problem including creating a large database for each traffic stop and determining the optimal traffic signals for the best vehicle flow based on the statistics collected, which does not react to data outliers. Other solutions suggest installing weight sensors under roads to detect the number of vehicles waiting then setting the duration of the next green light accordingly. This paper proposes an image analysis work flow to analyze the number of waiting vehicles as well as moving vehicles in each arm of a 4-way intersection. Then the collected data is utilized to control the state of the entire intersection to ensure the best traffic flow for all waiting and moving vehicles. Results from this approach yielded an absolute mean error of 0.559 detected representative vehicles with standard deviation of 0.93 on the first dataset and mean absolute error of 0.554 with 1.20 standard deviation for the second dataset. This level of accuracy conformed with the finite state machine control logic of the intersection, moving from one state to the other according to the analyzed images in real-time without causing starvation to any of the intersection arms.
Direct Conversion: Accelerating Convolutional Neural Networks Utilizing Sparse Input Activation

The amount of computation and the number of parameters of neural networks are increasing rapidly as the depth of convolutional neural networks (CNNs) is increasing. Therefore, it is very crucial to reduce both the amount of computation and that of memory usage. The pruning method, which compresses a neural network, has been actively studied. Depending on the layer characteristics, the sparsity level of each layer varies significantly after the pruning is conducted. If weights are sparse, most results of convolution operations will be zeroes. Although several studies have proposed methods to utilize the weight sparsity to avoid carrying out meaningless operations, those studies lack consideration that input activations may also have a high sparsity level. The Rectified Linear Unit (ReLU) function is one of the most popular activation functions because it is simple and yet pretty effective. Due to properties of the ReLU function, it is often observed that the input activation sparsity level is high (up to 85%). Therefore, it is important to consider both the input activation sparsity and the weight one to accelerate CNN to minimize carrying out meaningless computation. In this paper, we propose a new acceleration method called Direct Conversion that considers the weight sparsity under the sparse input activation condition. The Direct Conversion method converts a 3D input tensor directly into a compressed format. This method selectively applies one of two different methods: a method called image to Compressed Sparse Row (im2CSR) when input activations are sparse and weights are dense; the other method called image to Compressed Sparse Overlapped Activations (im2CSOA) when both input activations and weights are sparse. Our experimental results show that Direct Conversion improves the inference speed up to 2.82× compared to the conventional method.
When representation learning meets software analysis

In recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). Especially, representation learning, which can learn vectors from the syntactic and semantics of the code, offers much convenience and promotion for the downstream tasks such as code search and vulnerability detection. In this work, we introduce our two applications of leveraging representation learning for software analysis, including defect prediction and vulnerability detection.
On Randomized Searching for Multi-robot Coordination

In this chapter, we propose a novel approach for solving the coordination of a fleet of mobile robots, which consists of finding a set of collision-free trajectories for individual robots in the fleet. This problem is studied for several decades, and many approaches have been introduced. However, only a small minority is applicable in practice because of their properties - small computational requirement, producing solutions near-optimum, and completeness. The approach we present is based on a multi-robot variant of Rapidly Exploring Random Tree algorithm (RRT) for discrete environments and significantly improves its performance. Although the solutions generated by the approach are slightly worse than one of the best state-of-the-art algorithms presented in [23], it solves problems where ter Morses algorithm fails.
A Gaussian Process-Based Incremental Neural Network for Online Regression

This paper proposes a Gaussian process-based incremental neural network algorithm to handle the online regression problem. It can extract prototypes by an incremental neural network, where 1) Gaussian process approximations are adopted to update the threshold regions and the posterior distribution of the dependent variable at the weight vectors of nodes and 2) the optimal bandwidth matrix is derived for adapting to network structure. Besides, we discuss some properties of the proposed approach, and the experimental results show that our approach achieves remarkable accuracy improvement in extracting prototypes for online regression on noisy data.
Service-based Semiconductor Manufacturing using the Digital Reference Ontology for Global Service Discovery

The challenges of the Industry 4.0 era require the effective combination of emerging automation and information technologies. Two important representatives of these core technologies are service oriented architectures that provide the technical basis for middleware as well as semantic web technologies that provide unique vocabulary. In this paper, we illustrate the combination of these two technologies with regard to a concrete scenario within the complex semiconductor supply chain environment. Providing the scenario, problem description and solution at utmost concreteness, this approach aims at overcoming the complexity within this domain. This paper illustrates the challenges and solutions during ontology development, and on a conceptual level the application scenario, decision-making support for human planners as well as the IT infrastructure based on the Arrowhead Framework.
Camera Intrinsic Invariance of Image Jacobian in 4 DOF Image Based Visual Servo

In the image based visual servo, image Jacobian is vital to the system performance because it is the bridge that warps the velocity in feature space to camera velocity in Cartesian space. However, image Jacobian is sensitive to the camera intrinsic parameters, while the camera intrinsic parameter calibration error is almost unavoidable, which heavily affects the image Jacobian and visual servo process. In this paper, we find the camera intrinsic parameter invariance of the image Jacobian in our 4 DOF visual servo system. The image Jacobian of some geometry features is invariant to the camera intrinsic parameters, indicating that the disturbance in camera intrinsic parameters will not affect the convergence trajectory of those features. To further analyse camera intrinsic invariance, we proposed camera intrinsic parameter Jacobian of Image Jacobian, which can fully describe the camera intrinsic parameter invariance of the image Jacobian. The work in this paper can be used to analyse the system sensitivity to the camera intrinsic parameters. The camera intrinsic invariance is also significant for choosing the visual servo features when designing the visual servo system.
Optimization by Context Refinement for Development of Incremental Granular Models

Optimization by refinement of linguistic contexts produced from an output variable in the construction of an incremental granular model (IGM) is presented herein. In contrast to the conventional learning method using the backpropagation algorithm, we use a novel method to learn both the cluster centers of Gaussian fuzzy sets representing the symmetry in the premise part and the contexts of the consequent part in the if–then fuzzy rules. Hence, we use the fundamental concept of context-based fuzzy clustering and design with an integration of linear regression (LR) and granular fuzzy models (GFMs). This GFM is constructed based on the association between the triangular membership function produced both in the input–output variables. The context can be established by the system user or using an optimization method. Hence, we can obtain superior performances based on the combination of simple linear regression and local GFMs optimized by context refinement. Experimental results pertaining to coagulant dosing in a water purification plant and automobile miles per gallon prediction revealed that the presented method performed better than linear regression, multilinear perceptron, radial basis function networks, linguistic model, and the IGM.
Research Of Text Data Processing Algorithms In Social Networks

In this paper, we investigate various clustering algorithms for a large amount of text data. An analysis of the existing implementation methods was carried out and the algorithms Word2Vec and GloVe were selected. The initial textual data for testing the algorithms were obtained by collecting records from open VKontakte communities. The results showed that the use of these algorithms allows us to assess the frequency of use and the significance of individual words relative to the context of the studied community. The results of the algorithms applications were compared and the conclusion about their efficiency was made in the work as well.
Modelling elastic scaling of cloud with energy-efficiency: Application to smart-university

Cloud computing represents one of major innovations in Information Technology (IT). It is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. Elasticity is one of the most important characteristic in the cloud. Due to the enormous amounts of energy consumed by a cloud, it becomes difficult to build an elasticity system that satisfies all the requirements that might arise during its lifetime. Different approaches are proposed to address the energy-efficiency in the modelling of elasticity system at multiple layers of cloud services. Most of the existing approaches measure the overall hardware energy consumption, instead of the energy consumption of the software. Understanding how energy is consumed by cloud with elastic scaling mechanism is a key for managing better energy efficient software. This work proposes an architecture for modelling elasticity and energy-efficiency in the application layer. This architecture combines both the characteristics of adaptation (with Autonomic Computing) and variability (with Feature Model) into a single solution. The feature model considers the variability in the structural modelling and the behaviour one. We show the feasibility of the proposed approach by analysing the smart university applications associated with the Znn.com scenario.
Crowd Sourcing as an Improvement of N-Grams Text Document Classification Algorithm

A common task in a world of natural language processing is text classification useful for e.g. spam filters, documents sorting, science articles classification or plagiarism detection. This can still be done best and most accurately by human, on the other hand, we can of ten accept certain error in the classification in exchange for its speed. Here, natural language processing mechanism transforms the text in natural language to a form understandable by a classifier such as K-Nearest Neighbour, Decision Trees, Artificial Neural Network or Support Vector Machines. We can also use this human element to help automated classification to improve its accuracy by means of crowdsourcing. This work deals with classification of text documents and its improvement through crowdsourcing. Its goal is to design and implement text documents classifier prototype based on documents similarity and to design evaluation and crowdsourcing-based classification improvement mechanism. For classification the N-grams algorithm has been chosen, which was implemented in Java. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful. We have tested our approach on two data sets with promising preliminary results even across different languages. This led to a real-world implementation started at the beginning of 2019 in cooperation of two universities: VSB-TUO and OSU.
Channel Parameters Extraction Based on Back Propagation Neural Network

Accurately mastering the information of wireless channel characteristics is of great significance for improving the spectrum utilization rate and channel capacity. This paper studies the application of back propagation neural network (BPNN) in channel parameters extraction based on QuaDriGa platform. In this paper, the QuaDriGa platform is used to generate the Channel Impulse Response (CIR) in urban scenes, and SAGE algorithm is used to extract channel parameters such as delay spread, azimuth angle (AOA, AOD) in horizontal dimension and elevation angle (EOA, EOD) in vertical dimension. Then BPNN is trained with sample data to extract different channel parameters. The results show that there is little difference between the prediction results of BPNN model and SAGE algorithm, so BPNN model can replace SAGE algorithm to extract channel parameters for MIMO channel simulation. In addition, the time complexity of the two methods is also compared. The results show that BPNN has higher time complexity than SAGE algorithm. Besides, the simulation results of three common error back propagation algorithms are compared. The results show that the L-M algorithm has the lowest mean square error and the best effect in training BPNN model.
Scheme for Flux-Qubit-Based Microwave Single-Photon Counter with Weak Continuous Measurement

Single-photon detectors are crucial elements of prospective quantum devices and techniques such as quantum communication systems, quantum computers, quantum radars, astrophysical detectors, etc. The detection of individual photons in microwave range is a much larger challenge as compared to that of in optics due to the quanta energy difference of 4 to 5 orders of magnitude which requires a quite different approach. So "artificial atoms" with discrete energy spectrum such as superconducting qubits are used here exploiting resonance transitions between their quantum states. A scheme incorporating a flux qubit with controllable spectrum as a single photon counter for 3-12 GHz range and weak continuous readout circuit based on rf superconducting quantum interferometer (SQUID) with UHF pumping is proposed and analyzed. High SQUID pumping frequency (about 500 MHz) along with using cooled microwatt HEMT preamplifier provides the measurement frequency band of up to 30 MHz while substantially diminishing the back-action effect.