Reviewerly/exHarmony huggingface dataset:
Job-Aware File-Storage Optimization for Improved Hadoop I/O Performance

Hadoop is a popular data-analytics platform based on Google's MapReduce programming model. Hard-disk drives (HDDs) are generally used in big-data analysis, and the effectiveness of the Hadoop platform can be optimized by enhancing its I/O performance. HDD performance varies depending on whether the data are stored in the inner or outer disk zones. This paper proposes a method that utilizes the knowledge of job characteristics to realize efficient data storage in HDDs, which in turn, helps improve Hadoop performance. Per the proposed method, job files that need to be frequently accessed are stored in outer disk tracks which are capable of facilitating sequential-access speeds that are higher than those provided by inner tracks. Thus, the proposed method stores temporary and permanent files in the outer and inner zones, respectively, thereby facilitating fast access to frequently required data. Results of performance evaluation demonstrate that the proposed method improves Hadoop performance by 15.4% when compared to normal cases when file placement is not used. Additionally, the proposed method outperforms a previously proposed placement approach by 11.1%.
Digital educational technologies in the context of increasing the international competitiveness of universities

The article assesses the impact of the pandemic on the development of distance learning and digital competencies of university teachers who are part of the International Association of Financial and Economic Education. The analysis of the dynamics of the use of digital technologies, services and tools by teachers from November 2019 to June 2020 is carried out. Particular attention was paid to the study of changes in the attitude of teachers towards distance learning in connection with the current situation caused by the pandemic.
Role of facies changes in shifting trends of anticlines in the Zagros Fold-Thrust belt: Examples from Sulaimani area, Kurdistan Region, Northeastern Iraq

The Zagros Fold Thrust Belt extends for more than 1500 kms from Oman to Turkey and passes through Kurdistan Region, northern Iraq where the studied area is located. The belt is about 200 kilometers wide and the main anticlines axes have the trends of 300°-325° in the studied area. The present study focused on a local disturbance of the above trends in the Chwarta–Mawat area. The disturbance is expressed by 20-30° of clockwise rotation (toward north) of axes of the local anticlines relative to trend of the non-rotated main anticlines of the Zagros belt. The rotation occurs in the lateral boundary between the Early Cretaceous Balambo Formation (thin bedded limestone and marly limestone) and Qamchuqa Formation (massive limestone and dolomite). These two formations are consisting of incompetent and competent rocks whereas shifting is occurred at their lateral boundary. The reasons for this tectonic disturbance is discussed and analyzed according to the direction of stress in the area and boundary conditions that are associated with the axes rotation. The study attributes the disturbance to lateral facies changes from competent, in the northwest, to incompetent rocks in the southeast. Furthermore, the facies change is associated with presence of massif block (Mawat Massif) which is responsible for transferring of the northeast Zagros tectonic stress toward southwest direction into areas of anticlines disturbance. Due to this transfer , the rate of the stress propagation, being directed by the massif, is faster in the incompetent units in the southeast which rotates the massif clockwise and forcing the axis of the neighboring anticlines to shift northward about 25 degrees. The massif is a large block (26 km × 8 km) and according to previous studied consists of metamorphic and igneous rocks in addition to conglomerates. The block is located between rotated folds (the local disturbance) and the Main Zagros Thrust. Due to southwest pushing of the massif by the Main Zagros Thrust (or reverse Fault), the surrounded rocks are folded and aligned parallel to the massif. The rotated folds are localized at the interfaces between the massif and incompetent rocks. The record of this local disturbance is important for analysis of the dynamic evolution of Zagros since the local events can be used for interpretation of regional ones.
Hopfield Neural Network and Anisotropic Ising Model

The probabilistic Hopfield model known also as the Boltzman machine is a basic example in the zoo of artificial neural networks. Initially it was designed as a model of associative memory, but played a fundamental role in understanding the statistical nature of the realm of neural networks. The close relation between the Boltzman machine and the Ising model was a challenging observation in [1]. In this note we go further, we establish another type of structural similarity between these models sharing the methods of the Bethe ansatz family of integrable statistical mechanics. We examine the asymmetric model on the triangular lattice with arbitrary weights. We show that the probability of passing a trajectory in time dynamics obeys the Gibbs distribution with a partition function of the Ising model on the cubic lattice with additional weights on diagonals.
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-of-the-art performance when combined with pre-trained deep denoisers. However, current RED algorithms are inadequate for parallel processing on multicore systems. We address this issue by proposing a new asynchronous RED (ASYNC-RED) algorithm that enables asynchronous parallel processing of data, making it significantly faster than its serial counterparts for large-scale inverse problems. The computational complexity of ASYNC-RED is further reduced by using a random subset of measurements at every iteration. We present complete theoretical analysis of the algorithm by establishing its convergence under explicit assumptions on the data-fidelity and the denoiser. We validate ASYNC-RED on image recovery using pre-trained deep denoisers as priors.
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

In the last few years, there has been a surge of interest in learning representations of entitiesand relations in knowledge graph (KG). However, the recent availability of temporal knowledgegraphs (TKGs) that contain time information for each fact created the need for reasoning overtime in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the currenttime in the complex vector space. Specially, for facts involving time intervals, each relation isrepresented as a pair of dual complex embeddings to handle the beginning and the end of therelation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time. Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know hasnot been investigated in previous literature.
Machine Learning Techniques for Credit Card Fraud Detection

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they can, because the fraud costs millions of dollar business loss and it is rising over time, and that affects greatly the worldwide economy. . In this paper we introduce 14 different techniques of how data mining techniques can be successfully combined to obtain a high fraud coverage with a high or low false rate, the Advantage and The Disadvantages of every technique, and The Data Sets used in the researches by researcher
Asymmetric Learning for Stereo Matching Cost Computation

Current stereo matching methods benefit a lot from the precise stereo estimation with Convolutional Neural Networks (CNNs). Nevertheless, patch-based siamese networks rely on the implicit assumption of constant depth within a window, which does not hold for slanted surfaces. Existing methods for handling slanted patches focus on post-processing. In contrast, we propose a novel module for matching cost networks to overcome this bias. Slanted objects appear horizontally stretched between stereo pairs, suggesting that the feature extraction in the horizontal direction should be different from that in the vertical direction. To tackle this distortion, we utilize asymmetric convolutions in our proposed module. Experimental results show that the proposed module in matching cost networks can achieve higher accuracy with fewer parameters compared to conventional methods.
A User-Centric Evaluation to Generate Case-Based Explanations Using Formal Concept Analysis

Recommender systems are useful to find relevant products for a certain user. Some recommender techniques based on models, for example, Matrix Factorization, act as a black box for users. Explanations for recommender systems are useful to make recommendations more effective and help the users to trust the system and understand why certain items have been recommended. In this paper, we propose a post-hoc model-agnostic explanation system for MF recommendations based on Case-Based Reasoning and Formal Concept Analysis. We have conducted an experimental evaluation with real users to define what are the most useful explanation features that allow users a better understanding of the system recommendation.
Domanial and dimensional adversarial learning for emotion regression

In this paper, we address cross-domain multi-dimensional emotion regression through adversarial learning. This is done via a proper conduction of both dimensional and domanial adversarial learning. On the one hand, we conduct adversarial learning between emotion dimensions via a dimensional discriminator to achieve dimension-specific features through the attention mechanism for better determining dimensional emotion scores. On the other hand, we conduct adversarial learning between the target domain and multiple source domains via a domanial discriminator for better leveraging texts from source domains for regression model training in the target domain. Empirical evaluation on the EMOBANK corpus shows that our proposed approach achieves notable improvements in r-values in the cross-domain multi-dimensional emotion regression task over the state-of-the-art baselines.
Explainable Deep Reinforcement Learning for UAV Autonomous Navigation

Autonomous navigation in unknown complex environment is still a hard problem, especially for small Unmanned Aerial Vehicles (UAVs) with limited computation resources. In this paper, a neural network-based reactive controller is proposed for a quadrotor to fly autonomously in unknown outdoor environment. The navigation controller makes use of only current sensor data to generate the control signal without any optimization or configuration space searching, which reduces both memory and computation requirement. The navigation problem is modelled as a Markov Decision Process (MDP) and solved using deep reinforcement learning (DRL) method. Specifically, to get better understanding of the trained network, some model explanation methods are proposed. Based on the feature attribution, each decision making result during flight is explained using both visual and texture explanation. Moreover, some global analysis are also provided for experts to evaluate and improve the trained neural network. The simulation results illustrated the proposed method can make useful and reasonable explanation for the trained model, which is beneficial for both non-expert users and controller designer. Finally, the real world tests shown the proposed controller can navigate the quadrotor to goal position successfully and the reactive controller performs much faster than some conventional approach under the same computation resource.
On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods

Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning. Such methods are appealing due to the capability on large-scale sparse datasets with high computational efficiency. In this paper, we present a new framework for Adam-type methods with the trend information when updating the parameters with the adaptive step size and gradients. The additional terms in the algorithm promise an efficient movement on the complex cost surface, and thus the loss would converge more rapidly. We show empirically the importance of adding the trend component, where our framework outperforms the conventional Adam and AMSGrad methods constantly on the classical models with several real-world datasets.
Personalized Multifaceted Visualization of Scholars Profiles

When we consider our CV, it is full of entities - where we studied, where we worked, who we collaborated with on a project or on a paper. Entities we are linked to are part of our profile and as such they help to understand who we are and what are we interested in. Hence, we adapt the typed entity-relation graph (profile) concept and based on this presentation we propose a personalized multifaceted graph visualization for the entity profile. In the context of an academic conference, we allow scholars to explore a graph of related entities and a word cloud representing the links, providing the user a comprehensive, compact and structured overview about the explored scholar. We evaluated our proposed personalized multifaceted visualization in a user study with encouraging results which showed that this visualization is engaging, easy to use and helpful.
Automated identification and quantification of rock types from drill cuttings

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Automated identification and quantification of rock types from drill cuttingsAuthors: Youssef TamaazoustiMatthias FrançoisJosselin KherroubiYoussef TamaazoustiSchlumbergerSearch for more papers by this author, Matthias FrançoisGeoservicesSearch for more papers by this author, and Josselin KherroubiSchlumbergerSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3426273.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIdentifying and quantifying rock types from drill cuttings is a key step in characterizing the reservoir. Today, because it is manually done, this step is subjective and time-consuming. Automating this task is crucial to gain efficiency and objectivity. Some work has been proposed in the literature, but all consider a classification approach that does not allow for quantification. Here, we propose formalizing the problem as a segmentation task and solve it with a convolutional neural network in a transfer-learning scenario. Extensive experiments have been conducted and very promising results were obtained on single lithology samples, mixed ones and even wet cuttings.Presentation Date: Wednesday, October 14, 2020Session Start Time: 1:50 PMPresentation Time: 4:45 PMLocation: 351FPresentation Type: OralKeywords: rock physics, well-log interpretation, reservoir characterizationPermalink: https://doi.org/10.1190/segam2020-3426273.1FiguresReferencesRelatedDetailsCited byAutomation in Cuttings Analysis: Futuristic Preview of Digital Enablement for Geology 10118 March 2022 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Youssef Tamaazousti, Matthias François, and Josselin Kherroubi, (2020), "Automated identification and quantification of rock types from drill cuttings," SEG Technical Program Expanded Abstracts : 1591-1595. https://doi.org/10.1190/segam2020-3426273.1 Plain-Language Summary Keywordsrock physicswell-log interpretationreservoir characterizationPDF DownloadLoading ...
Topology of the Thesaurus of Russian Adjectives Revealed by Measurements of the Spoken Word Recognition Time

We measured spoken word recognition time for different lists of Russian adjectives. Thesaurus of adjectives is smaller than thesauri of nouns and verbs, making it more convenient for experimentation. 40 words were presented to a listener in a single experiment lasting about 90 s. In hundreds of trials we invariably observed robust ordering from the most quickly recognized word to the “slowest” one. The spoken word recognition process looks like a sweep in a space of words. We conjecture that this reflects cortical representation of the thesaurus of words, which are arranged into semantically close groups. Inside the group the words are ordered so that their meaning gradually changes along the list of words. Our experiments with words are in line with experimental studies of travelling waves in the cortex and their theoretical description. We believe that the retrieval of words from the cortical memory is mediated by the cortical travelling waves.
Prediction in Chaotic Environments Based on Weak Quadratic Classifiers

The problem of prediction in chaotic environments based on identifying analog situations in arrays of retrospective data are considered. Traditional recognition schemes are ineffective and form weak classifiers in cases where the system component of the observed process is represented by a non-periodic oscillatory time series (realization of chaotic dynamics). The objective is to develop a system of such classifiers, which allows for improvements in the quality of forecasts for non-stationary dynamics in flow processes. The introduced technique can be applied for the prediction of oscillatory non-periodic processes with non-stationary noise, i.e., dependence of different relay frequencies, external electric potential and microchannel width in an electrokinetic micromixer.
SQ-Framework for Improving Sustainability and Quality into Software Product and Process

Sustainability is one of the most important quality factors and it integrates some other quality factors in the product too.Moreover, it makes an effective workflow and improves user satisfaction.A manager can meet the target by controlling a project but sustainability is more versatile.Quality factors are the measuring criteria of a product while sustainability drives to make the quality product, efficient project, and successful organization so it is a package of strategy, tasks, processes, technologies, and stakeholders.It is observed that there is a lacking of sustainability practice in software engineering like other engineering communities.There are many software developing models that exist with limited scope in quality control for sustainability.Given the aforementioned viewpoint, this research proposes a new software project management framework, "SQ-Framework".Its hybrid structure consists of the features of methods, quality models, and sustainability.The execution guidance of "SQ-Framework" is provided according to "Karlskrona manifesto".A manager can use the framework to improve the management process of a project, a developer can integrate quality factors with sustainability into the product, an executive could be motivated to integrate quality and sustainability strategy in the organization, and the users get inspiration to practice sustainability.
Improved Fixed-Budget Results via Drift Analysis

Fixed-budget theory is concerned with computing or bounding the fitness value achievable by randomized search heuristics within a given budget of fitness function evaluations. Despite recent progress in fixed-budget theory, there is a lack of general tools to derive such results. We transfer drift theory, the key tool to derive expected optimization times, to the fixed-budged perspective. A first and easy-to-use statement concerned with iterating drift in so-called greed-admitting scenarios immediately translates into bounds on the expected function value. Afterwards, we consider a more general tool based on the well-known variable drift theorem. Applications of this technique to the LeadingOnes benchmark function yield statements that are more precise than the previous state of the art.
Peningkatan Minat Baca Pada Peserta Didik Sekolah Dasar Negeri 202/IX Bukit Makmur Kabupaten Muaro Jambi

Reading is a complex activity that directs large numbers of separate actions. One of the ways to achieve good reading success is interest. because without interest all activities will be carried out less effectively and efficiently. The reading interest of SDN 202 / IX Bukit Makmur students if observed as a whole is balanced or almost the same. However, it turns out that there are differences in reading interest between male and female students. The results of problem identification then emerged several problems at SDN 202 / IX Bukit Makmur which were identified as follows: (a) Students have not been seen to optimally utilize learning resources and learning facilities such as books and lessons, (b) students have not actively read books in the library. Based on the above problems, this research focuses on: Efforts made by classroom teachers to further increase the reading interest of class III students at SDN 202 / IX Bukit Makmur, Sei Bahar Sei Bahar District, Muaro Jambi Regency. This study aims to describe the teacher's efforts to increase reading interest in grade III SDN 202 / IX Bukit Makmur. This research is a descriptive qualitative research. The subjects of this study were 1 class teacher and 29 students III SDN 202 / IX Bukit Makmur. Data collection techniques using observation and interviews. Data analysis techniques in this study used data reduction, data presentation, and drawing conclusions. The results showed that the teacher's efforts to increase reading interest in third grade students of SDN 202 / IX Bukit Makmur, namely: (1) Encouraging children to tell stories about what they have heard or read, (2) Buying books that attract reading interest, (3) Swapping books with friends, (4) giving books as gifts, and (5) providing reading time. The reading interest of the third grade students was shown by the presence of several students visiting the library to borrow books or read books during recess.