Reviewerly/exHarmony huggingface dataset:
An Automated and Distributed Machine Learning Framework for Telecommunications Risk Management

Automation and scalability are currently two of the main challenges of Machine Learning (ML).This paper proposes an automated and distributed ML framework that automatically trains a supervised learning model and produces predictions independently of the dataset and with minimum human input.The framework was designed for the domain of telecommunications risk management, which often requires supervised learning models that need to be quickly updated by non-ML-experts and trained on vast amounts of data.Thus, the architecture assumes a distributed environment, in order to deal with big data, and Automated Machine Learning (AutoML), to select and tune the ML models.The framework includes several modules: task detection (to detect if classification or regression), data preprocessing, feature selection, model training and deployment.In this paper, we detail the model training module.In order to select the computational technologies to be used in this module, we first analyzed the capabilities of an initial set of five modern AutoML tools: Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, and TransmogrifAI.Then, we performed a benchmarking of the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI).Several comparison experiments were held using three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), allowing to measure the computational effort and predictive capability of the AutoML tools.
Satellite image super-resolution based on progressive residual deep neural network

Satellite remote sensing has wide applications in many fields. However, the quality of the observed images captured from the satellite sensors exhibits significant variances and most images are low resolution. Therefore, they adversely affect the system performance in a variety of real-world applications such as object recognition and analysis. In order to enhance the resolution of remote sensing images, we propose a super-resolution neural network called progressive residual depth neural network (PRDNN). The progressive residual structure used by PRDNN can gradually discover the feature information of satellite images at different levels and different receptive fields, thus providing more detailed features for reconstructing super-resolution satellite images. The experimental results of the DOTA satellite image database demonstrate that the proposed method is superior to the most advanced super-resolution algorithm in recent years.
UnB-AV: An Audio-Visual Database for Multimedia Quality Research

In this paper we present the UnB-AV database, which is a database of audio-visual sequences and quality scores aimed at multimedia quality research. The database contains a total of 140 source content, with a diverse semantic content, both in terms of the video and audio components. It also contains 2,320 test sequences with audio and video degradations, along with the corresponding quality and content subjective scores. The subjective scores were collected by performing 3 different psycho-physical experiments using the Immersive Methodology. The three experiments have been presented individually in previous studies. In the first experiment, only the video component of the audio-visual sequences were degraded with compression (H.264 and H.265) and transmission (packet-loss and frame freezing) distortions. In the second experiment, only the audio component of the audio-visual sequences were degraded with common audio distortions (clip, echo, chop, and background noise). Finally, in the third experiment the audio and video degradations were combined to degrade both audio and video components. The UnB-AV database is available for download from the site of the Laboratory of Digital Signal Processing of the University of Brasilia and The Consumer Digital Video Library (CDVL).
3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.
SARA: A Microservice-Based Architecture for Cross-Platform Collaborative Augmented Reality

Augmented Reality (AR) functionalities may be effectively leveraged in collaborative service scenarios (e.g., remote maintenance, on-site building, street gaming, etc.). Standard development cycles for collaborative AR require to code for each specific visualization platform and implement the necessary control mechanisms over the shared assets. in order to face this challenge, this paper describes SARA, an architecture to support cross-platform collaborative Augmented Reality applications based on microservices. The architecture is designed to work over the concept of collaboration models which regulate the interaction and permissions of each user over the AR assets. Five of these collaboration models were initially integrated in SARA (turn, layer, ownership, hierarchy-based and unconstrained examples) and the platform enables the definition of new ones. Thanks to the reusability of its components, during the development of an application, SARA enables focusing on the application logic while avoiding the implementation of the communication protocol, data model handling and orchestration between the different, possibly heterogeneous, devices involved in the collaboration (i.e., mobile or wearable AR devices using different operating systems). to describe how to build an application based on SARA, a prototype for HoloLens and iOS devices has been implemented. the prototype is a collaborative voxel-based game in which several players work real time together on a piece of land, adding or eliminating cubes in a collaborative manner to create buildings and landscapes. Turn-based and unconstrained collaboration models are applied to regulate the interaction. the development workflow for this case study shows how the architecture serves as a framework to support the deployment of collaborative AR services, enabling the reuse of collaboration model components, agnostically handling client technologies.
Formation of Organizational Competence of Future Engineers By Means of Blended Learning

The article outlines the need of formation of organizational competence of future engineers by means of blended learning. Blended learning contributes to the organizational competence of future engineers using high educational potentials of electronic resources. Blended learning allows to implement modern teaching concepts due to its accessibility, easiness of usage, socialization, possibility of organizing collaborative activities and evaluating methods. The authors conclude that use of blended learning can significantly increase the effectiveness of educational process and quality of student’ training.
Hawkeye: Adaptive Straggler Identification on Heterogeneous Spark Cluster With Reinforcement Learning

It is a common sense that people harbors the belief that stragglers exert huge influence upon the performance conducted by the analysis systems of big data for the reason of poor performance made by some computing nodes, data skew and so on. Accordingly, stragglers have been billed as an indispensable bottleneck in Map-Reduce framework processing. However, existing studies on stragglers identification are targeting coarse-grained detection, schedule level optimization and off-line log based cause analysis. Accuracy identifying the stragglers in time for each job, however, is an extremely tough because (1) Auite a number of root causes for stragglers in data analytics frameworks;(2) The number of key parameters affecting stragglers identification; and (3) The different clusters configurations, and their impact on the stragglers detection, vary among different job types and sizes. Either existing solutions adopt a “tweak-and-pray” manual tuning approach, which is complex, time-consuming and error-prone, or only most of them fix theirs eyes upon coarse-grained straggler detection. In this paper, we systematically conduct the exploration on the fundamental problem of automatic, adaptive straggler identification on big data analytics platform. Under the inspiration of the recent triumphs over implementing Reinforcement Learning (RL) techniques for solving complex online optimal problems, we conducted investigation that Reinforcement learning are reasonably employed to adaptively opt the optimal parameters to identify stragglers free of the intervention of human beings. Specifically, we propose Hawkeye, a general adaptive speculative execution system which identifies stragglers by reinforcement are learning to launch speculative tasks on heterogeneous cluster at runtime. In accordance with the experimental conclusions, Hawkeye manages to cut down the job completion time over the distinct type applications. An instance is that it reveals as many as nearly 37% decrease average job completion time based on an improvement of 23% on the preciseness of the present resolutions to the heterogeneous cluster.
Acquisition of Ownership Illusion with Self-Disownership in Neurological Patients

The multisensory regions in frontoparietal cortices play a crucial role in the sense of body and self. Disrupting this sense may lead to a feeling of disembodiment, or more generally, a sense of disownership. Experimentally, this altered consciousness disappears during illusory own-body perceptions, increasing the intensity of perceived ownership for an external virtual limb. In many clinical conditions, particularly in individuals with a discontinuous or absent sense of bodily awareness, the brain may effortlessly create a convincing feeling of body ownership over a surrogate body or body part. The immediate visual input dominates the current bodily state and induces rapid plastic adaptation that reconfigures the dynamics of bodily representation, allowing the brain to acquire an alternative sense of body and self. Investigating strategies to deconstruct the lack of a normal sense of bodily ownership, especially after a neurological injury, may aid the selection of appropriate clinical treatment.
A Stylometric Approach for Author Attribution System Using Neural Network and Machine Learning Classifiers

The paper discusses few methods of author attribution system in Bengali literature through stylometric approach. Our goal is to examine whether it is possible to identify the actual writers of some unknown Bangla documents by using a machine learning algorithm and artificial neural network. Two voting systems are also generated by the algorithms and how it varies from the result of classification models is discussed here too. We have made a corpus collecting articles of eight political writers. Firstly, we have selected some effective style markers based on statistical analysis of extracted features. Then multilayer feedforward neural network and SVM classification model are used to build an attribution system. Later, two voting systems are created by MLP classifier and SVM classification. By doing experiment and comparison, voting system gives much better result and works in more effective way for our research than the classification models.
Agreement on Target-Bidirectional Recurrent Neural Networks for Sequence-to-Sequence Learning

Recurrent neural networks are extremely appealing for sequence-to-sequence learning tasks. Despite their great success, they typically suffer from a shortcoming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus performance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional RNNs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of either sequence level or non-sequence level metrics. Extensive experiments were performed on three standard sequence-to-sequence transduction tasks: machine transliteration, grapheme-to-phoneme transformation and machine translation. The results show that the proposed approach achieves consistent and substantial improvements, compared to many state-of-the-art systems.
PEMETAAN DAERAH RAWAN KEBAKARAN DI LAHAN GAMBUT BERDASARKAN POLA SEKUENS TITIK PANAS DI KABUPATEN PULANG PISAU KALIMANTAN TENGAH

Land and forest fire is one of the major that caused Indonesia’s deforestation, who has a significant impact to the environment, loss of conservation, air pollution and economic loss. This research makes a spatial modelling along with factor that can affect collerates the forest fire. Spatial model of vulnerability of land and forest fire is built by composite mapping analysis method. Hotspot that is used in this research is the results of data mining processing, with sequential pattern mining technique which to find the relationships between the occurances of sequential event and pattern that often appear. From the six variables that influence land and forest fire there are four variables that impacts on the study area, that is forest zone, depth of peatland, distance of irrigation, and distance of road. The fire in the area of study occurs many times in the peatland area with the depth of 400-800 cm. Land and forest fire occurs frequently in 100-900 meters from irrigation and land and forest fire also occurs frequently in 1-4 km form the road. Land and forest fire occurs frequently in protected forest
An Integrated CNN-based Post Processing Filter For Intra Frame in Versatile Video Coding

Versatile Video Coding (H.266/VVC) standard achieves up to 30% bit-rate reduction while keeping the same quality compared with H.265/HEVC. To eliminate various coding artifacts like blocking, blurring, ringing, and contouring effects, etc., three in-loop filters have been incorporated in H.266/VVC. Recently, convolutional neural network (CNN) has attracted tremendous attention and achieved great success in many image processing tasks. In this paper, we focus on CNN-based filtering in video coding, where a single model solution for post-loop filtering is designed to replace the current in-loop filters. An architecture is proposed to reduce the artifacts of video intra frames, which take advantage of useful information such as partitioning modes and quantization parameters (QP). Different from existing CNN-based approaches, which generally need to train different models for different QP and only suitable for luma component, the proposed filter can well adapt to different QP, i.e. various levels of degradation of frames, and all components (i.e., luma and chroma) are jointly processed. Experiment results show that the proposed CNN post-loop filter not only can replace the de-blocking filter (DBF), sample adaptive offset (SAO) and adaptive loop filter (ALF) in H.266/VVC, and also outperforms them, leading to 6.46%, 10.40%, 12.79% BD-rate savings for Y, Cb and Cr, respectively, under all intra configuration.
Embedding Text in Audio Steganography System using Advanced Encryption Standard, Text Compression and Spread Spectrum Techniques in Mp3 and Mp4 File Formats

Hiding text in a digital audio file format has been a major challenge because of the Human Auditory System (HAS) and how the digital audio will be converted into analog form for text to be embedded into it.Several techniques that include but are not limited to Least Significant Bit, Echo Hiding, Phase Coding, Parity Coding and so on have been proposed in both research communities and the academia.Audio steganography hides data in selected audio files.Several audio steganography works exist, but their major limitations include their inability to embed information in multiple audio file formats, high distortion rate and low level of robustness of their resultant stego files.This research attempts to proffer solution to the obvious challenges of the previous works by developing an efficient and robust audio steganography system for the security of information whether in store or on transit across the Internet.Results of performance evaluation of the developed system shows that it has very low level of distortion as revealed by the Signal to Noise Ratio (SNR).The compression ratio obtained is also equal to one (1), which shows that the cover audio file is identical to the resultant stego file.
Performance-aware deployment of streaming applications in distributed stream computing systems

Performance-aware deployment of streaming applications is one of the key challenging problems in distributed stream computing systems. We proposed a performance-aware deployment framework (Pa-Stream) for distributed stream computing systems. By addressing the important aspects of the framework, this paper makes the following contributions: 1) investigated the performance-aware deployment of a streaming application over distributed and heterogeneous computing nodes, and provided a general application deployment model; 2) demonstrated a streaming applications deployment scheme by proposing an artificial bee colony strategy that deploys application's vertices onto the best set of computing nodes; an incremental online redeployment strategy was used to redeploy the running application; 3) developed and integrated Pa-Stream into Apache Storm platform; 4) evaluated the fulfilment of low latency and high throughput objectives in a distributed stream computing environment. Experimental results demonstrate that the proposed Pa-Stream provided effective performance improvements on latency, throughput and resource utilisation.
Evaluating innovation-driven economic growth: a case of Jordan

The aim of the research was to examine the role of innovation in promoting economic growth in Jordan.Innovation is a key factor for businesses and a significant element of growth.However, in the case of promoting economic growth, its significance differs in terms of region and countries.To explore the innovation-growth link, the study adopted a quantitative approach, using Jordan's macroeconomic data of 18 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017).The data was collected from secondary sources using the World Bank database.It was found in the research that the progress in Jordan in relation to technology and innovation does not contribute to GDP growth.Internet penetration in Jordan increased, but GDP saw a persistent decline.However, patent applications (non-residents) has a significant impact on reducing unemployment in the country.Thus, it is recommended to focus more on innovation in the form of promoting patents to reduce unemployment and propel growth in the country.
Total variance and invariant information in complementary measurements

We investigate the total variance of a quantum state with respect to a complete set of mutually complementary measurements and its relation to the Brukner–Zeilinger invariant information. By summing the variances over any complete set of mutually unbiased measurements and general symmetric informationally complete measurements respectively, we show that the Brukner–Zeilinger invariant information associated with such types of quantum measurements is equal to the difference between the maximal variance and the total variance obtained. These results provide an operational link between the previous interpretations of the Brukner–Zeilinger invariant information.
Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection

The thriving of deep models and generative models provides approaches to model high dimensional distributions. Generative adversarial networks (GANs) can approximate data distributions and generate data samples from the learned data manifolds as well. In this paper, we propose an approach to estimate the implicit likelihoods of GAN models. A stable inverse function of the generator can be learned with the help of a variance network of the generator. The local variance of the sample distribution can be approximated by the normalized distance in the latent space. Simulation studies and likelihood testing on real-world data sets validate the proposed algorithm, which outperforms several baseline methods in these tasks. The proposed method has been further applied to anomaly detection. Experiments show that the method can achieve state-of-the-art anomaly detection performance on real-world data sets.
More than playfulness

Abstract This study aims to uncover the complexity of emoji usage on Chinese social media. We investigate emoji usage in comments on push notifications from the WeChat official account of Guokr , which was chosen as a representative for an open forum for public communication. The data includes 2,552 comments from 90 articles pushed by the account. The analysis adopts a discourse-pragmatic perspective within the framework of intercultural pragmatics ( Kecskes 2014 ), taking into account both the local discourse environment and the cultural context. It is found that Chinese WeChat users show a preference for using emojis that are unique to the WeChat platform. Qualitative analyses were carried out on selected WeChat emojis used in comments fulfilling the speech acts of self-disclosure, self-praise, humor and complaining. Emojis are found to be used to perform and reinforce a sense of playfulness in social media, but underlying this playfulness there is a discursive conformity to social norms in real life. The use of emojis resolves the tension between the openness and freedom in social media and the conservative, constraint-bounded nature of established social norms.