Reviewerly/exHarmony huggingface dataset:
ReLoca: Optimize Resource Allocation for Data-parallel Jobs using Deep Learning

Since under-allocating computation resource (e.g., CPU cores) causes suboptimal JCTs of data-parallel jobs, users are inclined to request excessive computation resource to decrease JCTs. However, over-allocating computation resource for data-parallel jobs incurs considerable system overheads (e.g., network communication and disk I/O overhead), which prolong the job completion time. In this paper, we propose ReLoca towards the optimal allocation of computation resource with the objective of minimizing the job completion time. ReLoca employs a deep neural network to guide the allocation of computation resource, by learning the impact of the operations in data-parallel jobs on the system overhead and computation time. Since training samples are time-consuming to collect, we develop an adaptive sampling method to preferably collect high-quality samples and thus overcome the issue of data scarcity. We apply ReLoca to improve Spark and conduct real experiments with five typical applications in big data analytics. Results show that ReLoca significantly reduces the average job completion time. Compared with the state-of-the-art method, ReLoca has higher prediction accuracy, needs fewer training samples and decreases the sampling overhead. With the prediction by ReLoca, the JCT decreases by 29.85%.
Data Processing of a Local Seismological Network for West Texas Seismicity Characterization

Abstract We propose a template-matching workflow capable of improving detection sensitivity of a seismic network and demonstrate its performance on the local seismic network comprising Texas Seismological Network installations in West Texas. We use three earthquakes from three clusters as our templates. Template matching is applied to each station independently. Then, SeisComP3 scanloc associator groups the obtained picks into seismic events following moveouts between stations consistent with a velocity model. In comparison to short-term over long-term average detection workflow, the number of “new,” previously undetected events more than doubles. The events detected by the template-matching workflow are registered on a set of stations, thus allowing for their absolute location. Template matching improves local network sensitivity. Among network parameters, station noise conditions appear to have the highest influence on the effectiveness of the workflow.
Extraction of Trend Keywords from Thai Twitters using N-Gram Word Combination

Extracting keywords from text on social media facilitates people to update news and trends. It reduces time spent for identifying main content from huge amount of data, and it can be used to identify situations or events that most of people mention in each period of time. This paper proposes a method for extracting keywords from Thai text on social media. A N-gram-based word-combination technique is presented to segment words that are not in dictionaries and increase the precision of word segmentation. Posts on Twitter concerning universities in Thailand are used as a case study for extracting keywords and analyzing trends. The experimental results show that the proposed method yield the highest precision of 70%.
Domain Specific Ontology Enhancing Communication Accuracy in Airport Operation

Improved communication between pilots and ground control operator in aviation industry is one of the highest priority tasks in order to maintain safety at takeoff and landing. It has been observed that many accidents happen on these two critical events. One major cause of mistake is human related error or in other words un-recognized conversation between them. To improve the communication process we propose domain specific ontology based text support system. Although general purpose ontology is available but pilot and ground control operator communication is mission critical that requires high accuracy of understanding of conversation by text based guidance. In this paper we propose designing domain specific ontology to improve the accuracy of understanding. The domain specific ontology is designed using three different input sources and Natural Language Processing techniques. Our proposed solution not only improves accuracy but is economical also as it does not require any major hardware investment.
Method Simultaneous Using GAN and RNN for Generating Web Page Program Code from Input Image

The article presents an analysis of neural networks that can be used to generating web page program code from input image. As part of the research of the possibilities of modification of algorithms for generating code, the method of generating code simultaneous using the generative adversarial and recurrent neural networks based on the input image is proposed. Methods for creating source code using generative adversarial network (GAN) and recurrent neural networks (RNN) are analyzed. Using domain-specific language (DSL) for tree generation code method, it was possible to use GAN and RNN together, achieving on the average 10% productivity increase. Using GRID’s mesh, the generated code is adaptable. To implement the proposed method, software with a graphical user interface is created. Classes of GAN and RNN were considered, among which the feasibility of using a neural network with long-term short-term memory (LSTM) for analysis and forecasting of tags, blocks and HTML elements of the language in the input image was selected and substantiated. The stages of the research are presented. For the experiment datasets made on the basis of existing sites from open sources for 2018–2019 are used. The estimation of the effectiveness of the proposed method is described.
Prospecting Fe-Skarn mineralization using ASTER satellite data: case study from Ravanj village, Markazi Province, Iran

Abstract The study area is located in Iran central zone and Urumieh-Dokhtar volcanoplutonic belt. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data was used to identify alteration zones associated with Fe-Skarn mineralization in the Ravanj village, Markazi Province, Iran. Argillic, phyllic and propylitic alteration zones are typically associated with Fe-Skarn mineralization in the study area. In this research, the Selective Principal Component Analysis (SPCA) method was applied to VNIR + SWIR bands of ASTER remote sensing data. Bands 1, 4, 6 and 8 were designated for identification clay minerals. Bands 4, 5, and 6 were selected for argillic alteration mapping. Bands 1, 2, and 4 were used to identification iron oxides/hydroxide minerals. Bands 5, 6, and 7 were chosen to map phyllic alteration zones. Bands 7, 8, and 9 were nominated to specify propylitic alteration mapping. According to the eigenvector statistics calculated using SPCA for ASTER, inverse SPC4 image identified clay minerals and SPC2 images detected argillic alteration, oxides/hydroxide minerals, phyllic alteration and propylitic alteration. In this paper, SPCA technique is an appropriate method because of the distinction between alteartion minerals, vegetation for Fe-Skarn mineralization exploration.
A novel hierarchically-structured factor mixture model for cluster discovery from multi-modality data

Advances in sensing technology have generated multi-modality datasets with complementary information in various domains. In health care, it is common to acquire images of different types/modalities for the same patient to facilitate clinical decision making. We propose a clustering method called hierarchically-structured Factor Mixture Model (hierFMM) that enables cluster discovery from multi-modality datasets to exploit their joint strength. HierFMM employs a novel double-L21-penalized likelihood formulation to achieve hierarchical selection of modalities and features that are nested within the modalities. This formulation is proven to satisfy a Quadratic Majorization condition that allows for an efficient Group-wise Majorization Descent algorithm to be developed for model estimation. Simulation studies show significantly better performance of hierFMM than competing methods. HierFMM is applied to an application of identifying clusters/subgroups of migraine patients based on brain cortical area, thickness, and volume datasets extracted from Magnetic Resonance Imaging. Two subgroups are found, whose patients significantly differ in clinical characteristics. This finding shows the promise of using multi-modality imaging data to help patient stratification and develop optimal treatment for different subgroups with migraine.
Optimal weighted fusion Kalman estimator for the incremental system with correlated noises

Summary When the observation equation of system has not been verified or corrected under certain environmental conditions, applying it will yield to unknown system error and filtering error. The unknown system error can be effectively eliminated by introducing the incremental equation. In this article, the local Kalman estimator for the incremental system with correlated noises is first presented. It solves the state estimation problem for the system with unknown measurement error, which does not meet the requirements of classical Kalman filter. Furthermore, under the linear minimum variance optimal fusion criterion, the optimal weighted measurement fusion Kalman estimator is proposed for the multi‐sensor incremental system with correlated noises. It effectively solves the state estimation problem for the multi‐sensor system under poor observation condition. The proposed algorithms are simple in form and small in computational burden so as to be easily applied in engineering practice. A simulation example shows their effectiveness and feasibility.
A Zeroth-Order ADMM Algorithm for Stochastic Optimization over Distributed Processing Networks

In this paper, we address the problem of stochastic optimization over distributed processing networks, which is motivated by machine learning applications performed in data centers. In this problem, each of a total n nodes in a network receives stochastic realizations of a private function f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (x) and aims to reach a common value that minimizes Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> (x) via local updates and communication with its neighbors. We focus on zeroth-order methods where only function values of stochastic realizations can be used. Such kind of methods, which are also called derivative-free, are especially important in solving realworld problems where either the (sub)gradients of loss functions are inaccessible or inefficient to be evaluated. To this end, we propose a method called Distributed Stochastic Alternating Direction Method of Multipliers (DS-ADMM) which can choose to use two kinds of gradient estimators for different assumptions. The convergence rates of DS-ADMM are O(n√k log (2k)/T) for general convex loss functions and O(n k log (2kT)/T) for strongly convex functions in terms of optimality gap, where k is the dimension of domain and T is the time horizon of the algorithm. The rates can be improved to O(n/√T )and O(n log T/T) if objective functions have Lipschitz gradients. All these results are better than previous distributed zerothorder methods. Lastly, we demonstrate the performance of DSADMM via experiments of two examples called distributed online least square and distributed support vector machine arising in estimation and classification tasks.
How to Measure UX and Usability in Today’s Connected Vehicles

Besides hard factors like horsepower or consumption, today’s connected cars need soft factors being available as criteria for purchasing decisions. User experience (UX), as one of these soft factors, is essentially influenced by services available inside the vehicle. Almost every new vehicle offers a variety of mostly Internet-enabled services to improve the driving experience and to make customers happier. One challenge is to make UX measurable regarding experiences and knowledge inside the vehicle. It has been researched what influencing factors significantly characterize the evaluation of in-car UX. A combination of assessment of relevance, usability testing of pre-defined use cases as well as gathering of quantitative parameters helped create a characteristic variable as a measure of UX inside vehicles. The final evaluation tool is to allow the formation of a holistic opinion about experiences and knowledge inside vehicles and to enable a comparability of UX of complete vehicles as well as of developmental states. Ultimately this not only supports customers in their purchasing decision process, but also manufacturers developing and optimizing new and existing solutions.
On the Suitability of Using SGX for Secure Key Storage in the Cloud

This paper addresses the need for secure storage in virtualized services in the cloud. To this purpose, we evaluate the security properties of Intel’s Software Guard Extensions (SGX) technology, which provides hardware protection for general applications, for securing virtual Hardware Security Modules (vHSM). In order for the analysis to be comparable with analyses of physical HSMs, the evaluation proceeds from the FIPS 140–3 standard, the successor to FIPS 140–2, which is commonly used to assess security properties of HSMs. Our contribution is twofold. First, we provide a detailed security evaluation of vHSMs using the FIPS 140–3 standard. Second, after concluding that the standard is designed for stand-alone rather than virtual systems, we propose a supplementary threat model, which considers threats from different actors separately. This model allows for different levels of trust in actors with different capabilities and can thus be used to assess which parts of FIPS 140–3 that should be considered for a specific attacker. Using FIPS 140–3 in combination with the threat model, we find that SGX enclaves provide sufficient protection against a large part of the potential actors in the cloud. Thus, depending on the threat model, SGX can be a helpful tool for providing secure storage for virtualized services.
Mediating Art and Science: Media beyond the Two Cultures

By Alenda Chang, Adrian Ivakhiv. What is media’s place in the supposedly long-standing discontinuity between the arts and the sciences? Might media studies’ conventional expertise in technology, audience, communication, and design help reconcile them?
Towards Adaptive Sensor Data Quality Improvement based on Context Models

Pervasive applications use context information for decision making and to adapt to new circumstances at run time. We can derive this context from sensors (dynamic) or from the deployment information (static). Some applications rely on a combination of context information sources to evaluate the quality of the data received. Besides, different context sources enable the applications to adjust to changes in the configuration as soon as they happen. The possible adaptation can include a change in the data processing to incorporate data quality improvement approach to provide a better context to the application. In this paper, we offer an adaptive data quality improvement based on a combination of context sources that we model using a domain-specific ontology.
ThermoBench: A thermal efficiency benchmark for clusters in data centers

The energy efficiency of a data center depends on the cooling cost of clusters in the data center. Enhancing thermal efficiency of clusters is a practical approach to reducing energy consumption cost, optimizing scalability, and improving reliability. In this paper, we propose ThermoBench to evaluate the thermal efficiency of computing and storage clusters deployed in data centers. We shed light on the criteria, metrics and challenges of developing a thermal efficiency benchmark. We pay particular attention to clusters running scalable client-server enterprise applications in data centers. Because these applications are quite popular in modern data centers, thermal efficiency benchmarks for clusters providing services to these applications become critical. We characterize workload conditions in such a cluster computing environment in forms of client sessions of multiple transactional requests. To resemble real-world applications, ThermoBench makes use of the TPC-W benchmark to change transactional requests mix and load over time. We apply ThermoBench to evaluate the thermal efficiency of a real-world cluster. Experimental results show that ThermalBench provides a simple yet powerful benchmark solution for assessing thermal behaviours of computing clusters in data centers as well as offers thermal-aware scheduling strategies during the course of requests dispatching.
Bicycles for the mind have to be see-through

This paper describes ongoing research on building software to be comprehensible to its users so that they can tailor it to their needs in the field. Our test-bed is a computing stack called Mu that deemphasizes a clean interface in favor of a few global implementation properties: small implementation size, few distinct notations, parsimonious dependencies, a simple dependency graph that avoids cycles, and early warning on breaking changes. Assuming a 32-bit x86 processor and (for now) a basic third-party Unix-like kernel, Mu builds up from raw machine code to a memory-safe but less expressive language than C.
Designing situated learning experiences for smart cities: the Inf@nziaDigiTales3.6 experience

This paper reports the results of the Inf@nziaDigiTales3.6 project on the design, development and evaluation of an augmented reality application to support situated learning experiences in a smart city. The users of the application are primary school children and the case study refers to recognition of road signs as geometric shapes. The objective is to allow children to comprehend the meaning of road signs of a city and to guide them in the recognition of the geometric shapes and colors connected to these signs. The mobile application is based on the paradigm of games and uses a virtual character with the role of intelligent tutor. The results of the evaluation confirm the expected benefits related to the adoption of augmented reality and mobile technologies, mainly in terms of involvement, pleasantness and willingness to repeat the experience. The results show that the re-mediation of augmented reality and the intelligent tutoring system have influenced the memorization processes of figures and colors, as well as of the contents of most of the signals presented. The application has been also deployed on smart glasses and shown in the context of an international event for children and teenagers, the Giffoni Film Festival 2018.
Mixed or Virtual: Does Device Type Matter in Human-ECA Interactions

In this paper, we take a first step in exploring the effect of xR devices on the experiences of users in the context of human-agent interactions. We report on the design and heuristic evaluation of an embodied conversational agent integrated into either a Virtual or Mixed Reality environment. Our evaluation with four experts show that Virtual Reality may elicit a better experience from users, while Mixed Reality may evoke a better sense of social presence due to natural user embodiment and spatial references to physical objects in the real environment.
Are Charts Going Digital? The Case of Data Visualization on Portuguese Media

Sales and circulation of print publications, either newspapers or magazines, are dropping. In Portugal alone, from 2014 to 2018, there was a drop in printed periodic publications in circulation (INE, 2019a) and a fall of millions of units in the sales of newspapers and magazines (INE, 2019b). At the same time, digital media remains on the rise as the main source of information for the Portuguese population, since 81% of people between the ages of 16 to 74 use the internet to read or download news (INE, 2018). In the process of duplication or migration of content from print to digital, with the rise of multimedia stories, interactivity and mobility, it would be natural that Data Visualization would follow the tendency. Taking the changes in the media landscape into account, a question arose: how are Portuguese media outlets publishing charts, diagrams and infographics? Is Data Visualization rising on digital as other forms of news, like video and audio, or is it maintaining a foot in each platform? Are there any visible differences between the publication of infographics in digital and print editions? The main objective of the present research was to trace patterns of frequency, authorship, design, composition, relevance and themes in order to create a detailed overview of contemporary Data Visualization in Portuguese news outlets. To do this, the research team chose to analyze publications made in 2017, since this was a fruitful year for news in Portugal and the world. The analysis focused on March, August and October, which were the months with the biggest prevalence of newsworthy events of the year. During this period, the team collected, categorized and analyzed all charts, diagrams and infographics published by the 12 media outlets with the biggest readership in Portugal, both in print and digital editions, in March, August and October 2017. These were: Público, Jornal de Notícias, Diário de Notícias, Expresso, Observador, Correio da Manhã, Jornal Económico, Jornal de Negócios, I, Sol, Visão e Sábado, which were divided in daily newspapers, weekly newspapers, economic newspapers, news magazines and one news website. The first step was the design of an analysis sheet, based on a large base of literary influences and previous similar analysis, which included information such as title, date, author, editorial section, theme, type and size of graphics, importance, format, graphical elements, interactivity and appearance on the cover. The results were unexpected, and the differences were outstanding. While some variances were to be anticipated, only one of the 12 news outlets published a similar quantity of infographics or the same infographic on both platforms. All the rest published almost ten times more charts, diagrams and infographics in the print editions than on the websites. This research will enable the team to evaluate how the production of charts, diagrams and infographics in news outlets depends on the daily routines and workflows of data visualization professionals.
The Possibilistic Gustafson-Kessel Fuzzy Clustering Procedure for Online Data Processing

The script is engaged in the intricacy of clustering data streams. The advised plan considers the possibilistic fuzzy clustering on the grounds of the Gustafson and Kessel techniques for the case when data should be grouped in the following manner. Another aspect should also be considered, which has to do with the fact that classes possess a random configuration (which cannot typically be classified linearly) and are mutually intersecting.