Reviewerly/exHarmony huggingface dataset:
Analysis of Virtual Currencies as Driver of Business Marketing

Brand perception is a powerful tool to engage consumers. In a globalized world, the differentiation between products and services is often very difficult. Firms are continuously policing competitors in case they copy their products and services. At the same time, they are looking for characteristic elements to differentiate them over the competition. In this context, new ideas are needed to create a brand image perception among users. Currently, virtual currencies are a novel disruptive technology that is beginning to be known in modern society. In fact, the future course of this new technology and its real effects remain unknown. However, this technology has a modern and innovative feeling that can help firms to gain popularity. This work lies with this emerging issue track at the cross-section of technology and business. Its main contribution is exploring the connection between virtual currency as payment method and marketing strategies of firms.
Lsh-sampling Breaks the Computation Chicken-and-egg Loop in Adaptive Stochastic Gradient Estimation

Stochastic Gradient Descent or SGD is the most popular optimization algorithm for large-scale problems. SGD estimates the gradient by uniform sampling with sample size one. There have been several other works that suggest faster epoch-wise convergence by using weighted non-uniform sampling for better gradient estimates. Unfortunately, the per-iteration cost of maintaining this adaptive distribution for gradient estimation is more than calculating the full gradient itself, which we call the chicken-and-the-egg loop. As a result, the false impression of faster convergence in iterations, in reality, leads to slower convergence in time. In this paper, we break this barrier by providing the first demonstration of a scheme, Locality sensitive hashing (LSH) sampled Stochastic Gradient Descent (LGD), which leads to superior gradient estimation while keeping the sampling cost per iteration similar to that of the uniform sampling. Such an algorithm is possible due to the sampling view of LSH, which came to light recently. As a consequence of superior and fast estimation, we reduce the running time of all existing gradient descent algorithms, that relies on gradient estimates including Adam, Ada-grad, etc. We demonstrate the effectiveness of our proposal with experiments on linear models as well as the non-linear BERT, which is a recent popular deep learning based language representation model.
TEACHING SPECIAL PIANO TO THE CHINESE STUDENTS. FROM THE EXPERIENCE OF THE FAR EASTERN STATE INSTITUTE OF ARTS

The article summarizes the authors’ long-term experience of teaching special piano to the Chinese students. The study is based on the results of a survey conducted among the Chinese graduates. The paper proposes methodological recommendations concerning repertoire policy, interaction with the students and influence of the Chinese intonation patterns on intoning techniques. For the first time in domestic musical education methodology, the paper raises the problems of discrepancy between the stereotypes of the Chinese students and the actual contingent of learners at the Russian higher education establishments.
SF6 Gas Infrared Thermal Imaging Leakage Detection Based on Faster-RCNN

Infrared thermal image is an important medium for SF6 gas leakage detection. However, when the environment is complex or the temperature difference between the background and the measured gas is slight, a clear and obvious image can hardly be obtained. Moreover, the procedures for the leakage detection fully relies on human beings, which means lots of manpower are required and some subjective miss detection may occur. To solve this problem, aiming at the infrared thermal image and inspired by the breakthroughs in computer version (CV), this paper proposes a leakage detection method to realize automatic detection for the first time. Faster-RCNN, instead of human beings, is employed to handle SF6 infrared thermography image. Faster-RCNN is an advanced convolutional neural network, which has been widely used in numerous practical situations including image identification and face recognition. Under certain circumstances, our Faster-RCNN method can effectively improve the SF6 gas leakage detection. The method has great academic significance and can be promoted in practical engineering.
PENGARUH DURASI BELAJAR TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS 5 LEDOK 006 SALATIGA

This study discusses how to learn mathematics which was reviewed in class 5 students of SD Negeri Ledok 06 Salatiga. This research was conducted through qualitative methods. Data sources were obtained through two sources, namely primary data obtained from research subjects by using data retrieval tools directly on the subject of class 5 information sources. Secondary data obtained from other parties indirectly from the subject of information sources namely teacher grade 5. Data collection was obtained using studying 5th grade students while at home, besides using study studios that are used to obtain data consisting of instruments that relate to students who are learning and learning mathematics in class 5. The population in this study offers 13 students divided into 8 women and 5 men male class 5 at SD Negeri Ledok 06. The results of the study show: 1. There is a positive influence between improving learning and mathematics learning outcomes, 2. Students who rank 1-5 have a higher learning duration compared to students who have short learning advantages.
Extensions and applications

The chapter starts by discussing how to combine gamut mapping and tone mapping, so as to be able to transform images where input and output can both be of any type of dynamic range and colour gamut. Next we recount how to extend our tone mapping methodology to take into account surround luminance and display characteristics. This is followed by a number of applications where our tone mapping framework has proven useful: improved encoding of HDR video, photorealistic style transfer, image dehazing and enhancement of backlit images. Then we introduce two common types of colour problems that might be encountered with HDR image creation and editing, where the encoding curve plays a role, and we propose methods to solve these issues.
Bayesian geophysical inversion with Gaussian process machine learning and trans-D Markov chain Monte Carlo

SummaryA key aspect of geophysical inversion is the ability to model the earth with a low dimensional representation. There exist various approaches to solve the inverse problem. However, most methods do not automatically adapt inverse model complexity or the number of active model parameters as dictated by data noise and sparse receiver coverage, do not quantify inverse model uncertainty or do not work equally well for 1D, 2D or 3D earth models. Low frequency electromagnetic (EM) inversion for example, can require for 3D problems upward of 106 cells to forward model. Only a small fraction of these cells is effectively resolvable and there are significant trade-offs between them. To address these limitations, we present a novel approach to earth model parametrization by using a Gaussian Processes (GP) machine learning (ML) technique, coupled with a parsimonious Bayesian trans-dimensional (trans-D) Markov chain Monte Carlo (McMC) sampling scheme. One aspect that sets our approach apart from recent spatial dimension agnostic algorithms in the trans-D or ML literature is the ability to specify inversion property priors directly, as opposed to doing so in a transform domain of the property. Finally, we note that our method falls in the category of ML approaches that do not attempt to learn the physics of the process, but instead learn the representation of parameter values through a misfit function. We apply the trans-D-GP method to a 1D controlled source electromagnetic (CSEM) and 2D non-linear regression problem, using actual field data from the Northwest Australian Shelf for the former. The key advantages in using our method are the simplicity of prior specification, parsimonious low dimensional representations, and ease of representing large-scale models in 1D, 2D or even 3D with the same parametrization and computer code.
Digital Bildung: Norwegian Students’ Understandingof Teaching and Learning with ICT

The aim of this paper is to elaborate on how students perceive their own learning and democratic development in relation to their digital interactions and competencies. This paper seeks an understanding of students’ perspectives on digital Bildung. We investigate these issues by comparing different notions of Bildung perspectives among young students from four Norwegian schools. The study consists of interviews with 12 focus groups of 3 students in each. The results indicate that the students experienced unwanted interference by the teachers during the school day, and that they felt a lack of democratic involvement in social conflicts, digital usages and other daily forms of decision-making. Further, the interviews indicated that students perceived their teachers as biased when it came to helping students in situations of bullying or other relationship challenges. In investigating digital interactions and competencies among Norwegian students, we identified a visible gap in the notion of Bildung and identity development between the students and the teachers. This finding suggests a need to further discuss the role of students in creating a better school system based on democratic ideals.
Graph Representation Learning via Multi-task Knowledge Distillation

Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general way is still an open problem. Traditional methods use handcraft graph features in a tabular form but suffer from the defects of domain expertise requirement and information loss. Graph representation learning overcomes these defects by automatically learning the continuous representations from graph structures, but they require abundant training labels, which are often hard to fulfill for graph-level prediction problems. In this work, we demonstrate that, if available, the domain expertise used for designing handcraft graph features can improve the graph-level representation learning when training labels are scarce. Specifically, we proposed a multi-task knowledge distillation method. By incorporating network-theory-based graph metrics as auxiliary tasks, we show on both synthetic and real datasets that the proposed multi-task learning method can improve the prediction performance of the original learning task, especially when the training data size is small.
Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an `additional' projection step to control the `gradient' bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type---in the sense that they hold under the most practical assumptions ---which is made possible by means of a novel multi-step Lyapunov analysis.
Deep Learning for Over-the-Air Non-Orthogonal Signal Classification

Non-cooperative communications, where a receiver can automatically distinguish and classify transmitted signal formats prior to detection, are desirable for low-cost and low-latency systems. This work focuses on the deep learning enabled blind classification of multi-carrier signals covering their orthogonal and non-orthogonal varieties. We define two signal groups, in which Type-I includes signals with large feature diversity while Type-II has strong feature similarity. We evaluate time-domain and frequency-domain convolutional neural network (CNN) models in simulation with wireless channel/hardware impairments. Simulation results reveal that the time-domain neural network training is more efficient than its frequency-domain counterpart in terms of classification accuracy and computational complexity. In addition, the time-domain CNN models can classify Type-I signals with high accuracy but reduced performance in Type-II signals because of their high signal feature similarity. Experimental systems are designed and tested, using software defined radio (SDR) devices, operated for different signal formats to form full wireless communication links with line-of-sight and non-line-of-sight scenarios. Testing, using four different time-domain CNN models, showed the pre-trained CNN models to have limited efficiency and utility due to the mismatch between the analytical/simulation and practical/real-world environments. Transfer learning, which is an approach to fine-tune learnt signal features, is applied based on measured over-the-air time-domain signal samples. Experimental results indicate that transfer learning based CNN can efficiently distinguish different signal formats in both line-of-sight and non-line-of-sight scenarios with great accuracy improvement relative to the non-transfer-learning approaches.
SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels

In social networks, preserving privacy and preserving correlation among sensitive labels are a matter of trade-off. This paper presents a supervised anonymization technique, SNI (social network immunization), to publish social networks having multiple sensitive labels with correlation. SNI publishes all sensitive labels without distorting them. It publishes sensitive labels along with innovative labels named “partial sensitive labels” in an immune graph and multiple supplementary trees. These graph and trees, by itself or with the combination of other objects, supply correlation among sensitive labels for membership analysis. We present a framework along with an algorithm for extracting the immune graph and supplementary trees. These graph and trees minimize the membership error rate for membership analysis. The practical evaluation of the cancer code label of individuals also indicates the effectiveness of the SNI method.
Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text

This article presents experiments with pseudonymised Swedish clinical text used as training data to de-identify real clinical text with the future aim to transfer non-sensitive training data to other hospitals. Conditional Random Fields (CFR) and Long Short-Term Memory (LSTM) machine learning algorithms were used to train de-identification models. The two models were trained on pseudonymised data and evaluated on real data. For benchmarking, models were also trained on real data, and evaluated on real data as well as trained on pseudonymised data and evaluated on pseudonymised data. CRF showed better performance for some PHI information like Date Part, First Name and Last Name; consistent with some reports in the literature. In contrast, poor performances on Location and Health Care Unit information were noted, partially due to the constrained vocabulary in the pseudonymised training data. It is concluded that it is possible to train transferable models based on pseudonymised Swedish clinical data, but even small narrative and distributional variation could negatively impact performance.
An analysis of Turkish complaints in computer mediated communication: the Tripadvisor case

Complaints have been an important part of&#x0D; everyday communication. As computers have become an important part of everyday&#x0D; life, complaints have started to be used in computer-mediated communication&#x0D; (CMC) as well. As they are authentic, they are invaluable for pragmatic&#x0D; studies. There are few studies in Turkish studying CMC complaints&#x0D; pragmatically, so this study has been conducted to investigate 100 online&#x0D; complaints in Turkish in a website called TripAdvisor. Current comments have&#x0D; been chosen as these five star hotels in Antalya, a touristic area in the south&#x0D; of Turkey, are most frequently complained hotels in 2018. Findings showed that&#x0D; most of the complaints (N=91) were indirect while one of them was direct and&#x0D; the other eight comments included both. It was also found that most of the&#x0D; complaints (N=94) were accompanied by at least one speech act, and advice and&#x0D; positive comment were found as the most frequently used speech acts, but they&#x0D; also has reference to complaining and current writing, regrets and greetings.&#x0D; Finally, forms of “there (be)” and some descriptive adjectives such as bad,&#x0D; terrible and detestable were discovered to be most frequently used to describe&#x0D; in complaints. Results are discussed with the light of previous research about&#x0D; complaints both in Turkish and in English.&amp;nbsp;
Fertilizer Production Planning Optimization Using Particle Swarm Optimization-Genetic Algorithm

Background: The applications of constrained optimization have been developed in many problems. One of them is production planning. Production planning is the important part for controlling the cost spent by the company.Objective: This research identifies about production planning optimization and algorithm to solve it in approaching. Production planning model is linear programming model with constraints : production, worker, and inventory.Methods: In this paper, we use heurisitic Particle Swarm Optimization-Genetic Algorithm (PSOGA) for solving production planning optimization. PSOGA is the algorithm combining Particle Swarm Optimization (PSO) and mutation operator of Genetic Algorithm (GA) to improve optimal solution resulted by PSO. Three simulations using three different mutation probabilies : 0, 0.01 and 0.7 are applied to PSOGA. Futhermore, some mutation probabilities in PSOGA will be simulated and percent of improvement will be computed.Results: From the simulations, PSOGA can improve optimal solution of PSO and the position of improvement is also determined by mutation probability. The small mutation probability gives smaller chance to the particle to explore and form new solution so that the position of improvement of small mutation probability is in middle of iteration. The large mutation probability gives larger chance to the particle to explore and form new solution so that the position of improvement of large mutation probability is in early of iteration.Conclusion: Overall, the simulations show that PSOGA can improve optimal solution resulted by PSO and therefore it can give optimal cost spent by the company for the planning.Keywords: Constrained Optimization, Genetic Algorithm, Linear Programming, Particle Swarm Optimization, Production Planning
Application of micro-XRF to characterise diamond drill-core from lithium-caesium-tantalum pegmatites

SummaryMicro X-ray fluorescence (μXRF) is a rapid and nondestructive technique used to acquire qualitative and quantitative data at high spatial resolution (i.e. μm scale). To date this technology has yet to be utilised to its full potential in the exploration, mining and metallurgical industries. Micro-XRF instrumentation can acquire a wealth of geochemical, mineralogical and petrological information for a versatile range of sample types and sizes, due to the large sample chamber and small X-ray beam size. The non-destructive nature of the instrument combined with the minimal sample preparation required is a major advantage over other geochemical techniques and makes μXRF ideally suited for rapid and inexpensive studies aimed at investigating micromorphology, chemical variability and mineral alteration in rock samples.In this paper, we present the findings of an ongoing case study on Lithium-Caesium-Tantalum (LCT) pegmatites, primarily focused on the Sinclair Caesium Deposit. The analyses were conducted on split diamond drill-core and highlight the quality of data that can be acquired by μXRF and the ability to accurately identify the mineralogy and quantify the chemical compositions of geological samples. The resulting element distribution and mineral maps enabled mineralogical discrimination, the identification of previously unknown mineralogical features and the detection of chemical differences at a micro-scale. Therefore, μXRF is a versatile tool that can be utilised in the exploration and mining industry for the rapid acquisition of high-quality qualitative and quantitative geochemical data on a variety of geological and environmental samples.
Surface Realization Shared Task 2019 (MSR19): The Team 6 Approach

This study describes the approach developed by the Tilburg University team to the shallow track of the Multilingual Surface Realization Shared Task 2019 (SR'19) (Mille et al., 2019).Based on Ferreira et al. ( 2017) and on our 2018 submission Ferreira et al. ( 2018), the approach generates texts by first preprocessing an input dependency tree into an ordered linearized string, which is then realized using a rulebased and a statistical machine translation (SMT) model.This year our submission is able to realize texts in the 11 languages proposed for the task, different from our last year submission, which covered only 6 Indo-European languages.The model is publicly available 1 .
Where is the destructive update problem?

I critically examine several objections to ‘destructive update’ of assignment functions in dynamic theories of anaphora, using a modular compositional treatment of the static/dynamic divide to highlight what static and dynamic approaches to assignment functions have in common, and how they differ. EARLY ACCESS