Reviewerly/exHarmony huggingface dataset:
A Semiotic Model Mapping and Reduction Properties

In this paper, we investigate semiotic models used in the computer industry, by means of linguistic approach, considering a computer modeling notation as an artificial modeling language, which, compared to a natural language of human communication, sacrifices its beauty and imagery for the sake of accuracy and unambiguity. The research is carried out in the context of Frege’s triangle, its vertexes represent three sets of different nature: material objects, ideal concepts, and signs. We suggest separating relations inter elements of one set, and a mapping describing the correspondence between the elements of different sets. Three sides of Frege’s triangle can be interpreted as mappings: conceptualization - assigns an object to a concept, semantic - link a concept to a sign, representation - connects a sign of a model to an object of a subject area. An analysis of mappings inter three sets of different nature allows making a judgment on model quality. In the case of computer modeling notations all mappings should be morphisms that carry the relations between the elements of one set to the relations between the elements of the other. Finally, we make a proposition that syntax and semantics of a computer modeling notation can be formalized by use of Bunge-Wand-Weber upper-level ontology.
QoE and Video Quality Evaluation for HTTP Based Adaptive Streaming

Video quality demanded by the end user must result in the best possible Quality of Experience (QoE). QoE assesment as well as its integration into video streaming algorithms is a complex problem that demands a comprehensive design of subjective databases and video quality metrics. This paper presents an analysis of different video quality metrics and their usability for QoE measurement for HTTP based adaptive streaming (HAS). To compare the metrics, the LIVE-NFLX-II database is used.
ANN based Measurement for No-Reference Video Quality of Experience Metric

In recent years, there has been a tremendous development in information technology, which has led to the possibility of fast access to different data types over the Internet, one of the data type is video and the ability for the user to watch video directly online. This paper provides a video streaming QoE evaluation metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that are used to train the neural network and finally evaluate the QoE value. Verify training models prediction using 10-fold cross-validation. The proposed system had the best correlation result 0.95 in SRCC metric.
Educational software for the initiation of the Kichwa language

This research presents an educational application for the recovery of the Kichwa language as a technological proposal that seeks to promote culture and mitigate the problem of the disappearance of native languages in the country indicated by UNESCO in 2017, for it the Álvaro Galvis is adapt Panqueva's educational methodology with an agile software development methodology Scrum, in order to obtain an agile concept applied to the development of educational software. As a result, a didactic system designed for children was obtained; it has six modules with the basic concepts of communication (greetings, colors, animals, vowels, etc.) in the indigenous language. The proposal was evaluated at first by expert pedagogues in the teaching of foreign languages and then validated in an academic environment with students from a rural area, who used the educational software demonstrating ownership of the Kichwa language.
News Recommendation using Social Network Data

Many social networks exist today- for example, Facebook and twitter. They have played a major role in our day to day news consumption. These sources have a huge potential to provide important news related information. These include user-generated data as well as numerous links to online articles. However, these are not completely useful for all types of users without a proper filtering process. The news is filled with noise as well as irrelevant media that may not be considered valuable. In addition, it is vital to prioritize this data post the filtering process due to the possibility of information overload. This can be attained by ranking the information by making use of three major factors. The first factor is the media focus (MF) of any topic in the news which describes its temporal prevalence in the news. The second factor is the user attention (UA) which indicates its temporal prevalence in social network media. The last factor is known as user interaction (UI) which depicts the various interactions amongst all the users who are currently discussing this topic. This paper presents a solution that uses an unsupervised framework to extract and rank news topics from social media and news sources according the above mentioned factors.
A Novel Throughput Based Temporal Violation Handling Strategy for Instance-Intensive Cloud Business Workflows

Temporal violations take place during the batch-mode execution of instance-intensive business workflows running in the cloud environments which may significantly affect the QoS (Quality of Service) of cloud workflow system. However, currently most research in the area of workflow temporal QoS focuses on single scientific workflow rather than business workflow with a batch of parallel workflow instances. Therefore, how to handle temporal violations of instance-intensive cloud business workflows is a new challenge. To address such a problem, in this paper, we propose a novel throughput based temporal violation handling strategy. Specifically, firstly we present a definition of throughput based temporal violation handling point to determine where temporal violation handling should be conducted, and secondly we design a new method for adding necessary cloud computing resources for recovering detected temporal violations. Experimental results show that our temporal violation handling strategy can effectively handle temporal violations in cloud business workflow and thus guarantee satisfactory on-time completion rate.
Reconstruction of Satellite Remote Sensing Images using Multifractal Analysis

Remote Sensing Satellites are imaging the earth and providing different types of imagery in terms of volume, variety, velocity and variability. The spatial resolution is an important quality parameter and critical bottleneck in various remote sensing applications. In this work, we used multi-fractal based reconstruction to increase the spatial resolution of the remote sensing image. In multifractal analysis, image is treated as a nontrivial combination of a number of fractals. Multifractal characteristics of the low resolution image are extracted to compute the information transfer function and noise parameters. We generated an enhanced resolution image using low resolution image by a fractal based denoising and downscaling method. The reconstructed super resolution image is validated with original high resolution image through quality parameters like correlation coefficient and Structural Similarity Index (SSIM).How to cite this article: Sneha C, Venkateswarlu E, Nair T. Reconstruction of Satellite Remote Sensing Images using Multifractal Analysis. J Adv Res Geo Sci Rem Sens 2019; 6(3&4): 36-38.
Feature Selection and Recognition of Multivariate Physiological Signals Using Binary Firefly Algorithm

Interest in pattern recognition of physiological signals is growing in recent years due to the rapid development of telemedicine and the Internet of Things. However, on account of high dimensionality, multivariate, and the large amount of data, feature extraction and selection of multiple physiological signals is still a hard nut to crack in pattern recognition. Therefore, a measuring experiment of multivariate physiological signals before and after physical exercises was firstly carried out. Signals of electrocardiogram (ECG), blood pressure and oxygen saturation are simultaneously measured for 30 subjects. Moreover, multivariate physiological signals are in turn processed by filtering, segmentation and morphological feature extraction based on heartbeat samples. A novel binary firefly algorithm (NBFA) is proposed in this paper to realize the automatic feature selection for multiple physiological signals. The NBFA evaluates each binary firefly according to both classification accuracy and the number of features. The movement of a firefly is updated by a bit conversion factor and a random mounting flying step. High classification results of 30 subjects show that morphological features can depict main characteristics of 12-lead ECG signals. The NBFA performs superior to the PCA, PSO in the feature selection of multiple physiological signals. It can quickly find the small number of features as well as obtain high classification, which further reduces the computation load and data storage capacity in pattern recognition of multiple physiological signals.
RNA: Reconfigurable LSTM Accelerator with Near Data Approximate Processing

Near Data Processing(NDP) techniques are introduced into deep learning accelerators as they can greatly relieve the pressure on memory bandwidth. Besides, approximate computing is also adopted in accelerating neural networks for the network fault-tolerance to reduce energy consumption. In this paper, an NDP accelerator with approximate computing features for LSTM is proposed to explore the data parallelism with reconfigurable features. Firstly, a hybrid-grained network partitioning model with scheduling strategy of LSTM is put forward to achieve high processing parallelism. Secondly, the approximate computing units are designed for LSTM with adaptive precision. Then the heterogeneous architecture, RNA, with reconfigurable computing arrays and approximate NDP units is proposed and implemented regarding the configuration code. The gates and cells in LSTM are modeled into fine-grained operations, organized in coarse-grained tasks, and then mapped onto RNA. In addition, approximate computing units are integrated into the NDP units with the adaptive precision, which is also controlled by the configuration codes. The proposed RNA architecture achieved 544 GOPS/W energy efficiency while processing LSTM, and further can be extended for larger and more complex recurrent neural networks. Comparing with the state-of-the-art accelerator for LSTM, it is 2.14 times better in efficiency.
Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation

Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non-uniform temporal scalings is a common issue in skeleton-based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. We empirically evaluate our framework on real-world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state-of-the-art while benefiting from a less complicated yet more interpretable model.
Recurrent Neural Detection of Time–Frequency Overlapped Interference Signals

For interfering signals overlap with normal signals in both time and frequency domain, it is difficult to detect them. Therefore, this paper proposes a novel bidirectional recurrent neural network-based interference detection method. By utilizing the ability of recurrent neural network of extracting the nonlinear features of the time series context, the model can get a prediction of following signal samples and calculate the difference between prediction signal and original signal to do interference detection. The proposed method can achieve a better sensitivity and determine the exact location of the complete interfering signal. In the experiment part, we demonstrate the efficacy of this method in multiple typical scenarios of time–frequency overlapped wireless signals.
Uncovering Significance of Feature-Selection in Recommender System Models

Recently, recommendation systems have shown significant improvement in terms of return on investment (ROI). The fundamental idea of a recommender system is to assist user to shortlist item(s) based on his/her choices and preferences. Recommender system model takes in to consideration set of item features to generate relevant recommendations. Recommender system is applicable on items from multiple business domains. For each item, different feature-set may turn out to be useful to obtain accurate recommendation results. This paper discusses the compatibility of textual and visual movie features through various scenarios. We analyze recommendation results obtained by using distinct movie features. This study suggests possible extensions and also helps in taking decision with respect to the selection of appropriate item features.
New Complex Hybrid Security Algorithm (CHSA) for Network Applications

The raising of Cyberwarfare level require to have the maximum protection levels in the networks and its applications. Therefore, these systems must be protected from unauthorized usage of their data using the most powerful security algorithms. Many and growing challenges facing cybersecurity require cryptographers and data security engineers to develop stronger and more powerful algorithms to resist this type of attack. There are many characteristics that measure the strength of the electronic security system, including the speed of the algorithm and the strength of the system against the cryptanalysis. This paper discusses the design, building, and implementation of a new hybrid algorithm that combines a number of security algorithms in order to obtain the best results in terms of resistance to multiple cyber-attacks. This paper also contains an analytical study of the algorithms used and their effectiveness in resistance to attacks represented by statistical results and graphs.
Space-Efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs). The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors. Thus, we present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.
Decoding of Polar Code by Machine Learning

In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> bit polar code into many small sub-blocks according to the encoding rules of polar code, then put these sub-blocks into the neural network of the same structure for processing. This decoding algorithm is non-iterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.
Towards Dynamically Adaptable Immersive Spaces for Learning

This paper describes the difficulties prepositions pose for students learning English as a second language and proposes novel solutions for teaching based on Virtual Reality (VR), and environments using Artificial Intelligence (AI). It identifies the synergy between Cognitive Linguistics (CL) and VR and describes how VR may be applied to Second Language Acquisition (SLA). This is followed by a proposed integration of AI into a VR environment. The paper then goes on to describe a novel method for creating an intelligent teaching environment for SLA, based on cloning student behaviour, and gives details of the process and a simplified functional example.
A Dependency-Extended Method of Multi-pattern Matching for RDF Graph

The problem of multi-pattern matching for RDF graph is an extended problem of subgraph isomorphism, where Resource Description Framework (RDF) is a graph-based data model for information sharing on Web. In real world, concurrent execution of multi-queries is more realistic than single query. However, the problem about re-computations of common subgraphs always limits the time efficiency of matching processing. To solve this problem, an algorithm of multi-pattern matching for RDF graph is proposed, which can response to multiple queries through one traversal of RDF graph. The experimental results show that our algorithm can avoid the re-computations of common subgraphs and improve up to 70$$\%$$ of time efficiency than basic line algorithm.
Flow Disruptions and Mitigation in Virtualized Water-Cooled Data Centers

Recent availability of warm water cooling systems that can be easily retrofitted to stock server by replacing the heatsinks with coldplates have made it possible to use such cooling for non-HPC cloud/data center servers. These cooling systems use internal pumps in rack-level heat exchangers as well as external pumps that can fail. We present a systematic study of the pump failures that disrupt flow in the cooling system, propose and experimentally evaluate techniques for reducing service disruptions during failures while avoiding damage to the servers where water cooling has failed.
On Convex Stochastic Variance Reduced Gradient for Adversarial Machine Learning

We study the finite-sum problem in an adversarial setting using stochastic variance reduced gradient (SVRG) optimization in a distributed setting. Here, a fraction of the workers are assumed to be Byzantine that exhibit adversarial behavior by providing arbitrary data. We propose a robust scheme to combat the actions of Byzantine adversaries in this setting, and provide rates of convergence for the convex case. This is the first study of SVRG in an adversarial setting.
The use of open source microcontrollers as an enabler of learning in mandatory education in Portugal

Microcontrollers are one of the resources that are present in more and more schools and that are part of the educational context in many classrooms. These technologies have been evolving and bring with them tools that help teachers and researchers in the development of their activities. This document aims to reflect and assist educators in the contextualization of these technologies and present them with a framework when making decisions about the use of microcontrollers that are available. This paper reports a research work related to classroom use of tangible interfaces; or in the development of projects or other dynamics, within the context of mandatory education in Portugal, using microcontrollers.
Sensor Fusion Based Path Following Control of a Car-Like Mobile Vehicle in the Presence of Input Saturations and a Pulse Disturbance

This study aims to achieve the sensor fusion based path following control of a car-like mobile vehicle (CLMV) in the presence of a pulse disturbance and input saturations. The pose and orientation information of the CLMV are respectively obtained from both an encoder and a laser range finder (LRF). The Extended Kalman Filter (EKF) is used to fuse these two sensors to obtain more correct states for feedback in the control loop. Path planning of motion is generated by a virtual CLMV. Subsequently, the position error model between the virtual and actual CLMVs is given. According to the derived position error model, the proposed path following controller (PFC) is used to attain its path following based on the sensor fusion. The system stability is testified via Lyapunov stability criteria. Finally, simulations further validate the effectiveness and robustness of the proposed control scheme.
Contrasting cyber-dependent and traditional offenders

This chapter will focus on cyber-dependent crimes (i.e., malicious hacking). Of all crimes that could be called cybercrimes, these cyber-dependent crimes are theoretically most different from traditional crimes. They strongly rely on the anonymous digital context of IT-systems. Therefore, when comparing cyber-dependent offending to traditional offending, differences may be expected in: situations that provide opportunities for offending, personal characteristics and skills that increase the likelihood of offending, expected consequences of offending, etcetera. There is some empirical research that applies traditional criminological explanations for offending to cyber-dependent offending. However, almost no research provides an empirical comparison between cyber-dependent offenders and traditional offenders. This chapter will first provide arguments for possible differences between cyber-dependent offenders and traditional offenders on important domains in criminology: offending over the life-course, personal and situational risk factors for offending, and cyber-deviant behavior in social networks. Subsequently, it will focus on existing empirical research in these domains and a recent empirical comparison between cyber-dependent offenders and traditional offenders. After discussing motives for offending, implications for the prevention of cyber-offending will be reviewed. The chapter concludes with a discussion of future research directions in this area.
Deep Learning for Next-Generation Inventive Wireless Networks

Recent years have witnessed an exponential rise in the number of connected wireless devices. This number is expected to rise at an unprecedented rate leading to severe challenges in user demands and network infrastructure. With the fusion of artificial intelligence (AI) technologies, these devices have become more smart and intelligent. Using machine learning, efforts are being carried out to artificially train these networks to determine the optimum system requirements automatically. With these intelligent networks generating an enormous volume of data along with the demand for high data rate, machine learning has found its few limitations. Deep learning techniques have emerged as one of the most impressive tools to solve many of these problems. The primary objective of this chapter is to provide insights into deep learning and the applications of deep learning in next-generation inventive wireless networks. The chapter also presents the issues, challenges, and future directions in this research area.