Reviewerly/exHarmony huggingface dataset:
Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise

Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.
A Deep Learning Model Based on Sparse Matrix for Point-of-Interest Recommendation

Point-of-interest (POI) recommendation that consists of location-based social networks (LBSNs) and provides personal services for users has become an important part in the field of recommendation system.Due to the sparseness of user check-in matrix, POI recommendation faces great challenges.However, most researches just consider of spatial and temporal impact on recommendation and do not solve the problem of sparsity.This paper proposes a POI recommendation model called RBMNMF which is based on sparse matrix of user check-ins.Firstly, by stacking restricted Boltzmann machines (RBM), the potential relationship between users and POIs is learned and multiple user-POI matrices are extracted.Second, fill the original sparse matrix by using non-negative matrix factorization (NMF).Finally, fuse those prediction matrices to generate final POI recommendation for users, which is benefit for solving the problem of sparsity effectively.Experiments on real-world data set prove that the model we propose has a better accuracy than traditional algorithms.
Old Utopias, New Tax Havens

Cryptocurrencies are frequently framed as future-oriented, technological innovations that decentralize money, thereby liberating it from centralized governance and the political tentacles of the state. This is misleading on several counts. First, electronic currencies cannot leave the politics of money behind even where they aim to disavow it. Instead, we can understand their impact as a political attempt to depoliticize money. Second, the dramatic price swings of cryptocurrencies challenge their self-fashioning as a new form of money and reveal them instead as speculative assets and securities in need of regulation. While the preferential tax and regulatory treatment of cryptocurrencies hinges on their nominal currency status, it is ironically precisely their success as speculative assets that has undermined these claims. Finally, far from heralding a radical break with the past, electronic currencies serve as a reminder of the unresolved global politics of money since the 1970s. To support these three interrelated theses this chapter places the rise of cryptocurrencies in the historical context of the international politics of money between the end of the Bretton Woods system and the response to the 2008 Financial Crisis.
Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report

This paper describes the machine translation system developed jointly by Baidu Research and Oregon State University for WMT 2019 Machine Translation Robustness Shared Task.Translation of social media is a very challenging problem, since its style is very different from normal parallel corpora (e.g.News) and also include various types of noises.To make it worse, the amount of social media parallel corpora is extremely limited.In this paper, we use a domain sensitive training method which leverages a large amount of parallel data from popular domains together with a little amount of parallel data from social media.Furthermore, we generate a parallel dataset with pseudo noisy source sentences which are back-translated from monolingual data using a model trained by a similar domain sensitive way.We achieve more than 10 BLEU improvement in both En-Fr and Fr-En translation compared with the baseline methods.
Review of Various A* Pathfinding Implementations in Game Autonomous Agent

Among many pathfinding algorithms, A* search algorithm is an algorithm that most commonly used in grid-based pathfinding. This is due to implementation of A* search which proven to be able to generate the optimal path in a relatively short time by combining two characteristics of Djikstra's and best-first search algorithm. In the implementation of A* search, the selection of heuristic function and data structure can affect the performance of the algorithm. The purpose of this research is to find the best heuristic function and data structure with regards of the performance in A* search implementation as a pathfinding algorithm in a 3D platform. In the experiment, some known heuristic functions and data structures will be tested on the various 3D platform with a different size and obstacle percentage. Based on experiment that have been done, euclidean squared distance is best heuristic function for 3D pathfinding problem, with regards of the implementation performance. In addition, we also found that binary heap is the best data structure to be implemented for 3D pathfinding problem, with regards of implementation performance.
IMPROVING SPEAKING SKILLS THROUGH SMALL GROUP DISCUSSION AT ELEVENTH GRADE STUDENTS OF SMA PLUS MUNIRUL ARIFIN NW PRAYA

This study was aimed at finding the effect of small group discussion in improving speaking skills at the seventh year students of SMA Plus NW. This research was conducted as quasi-experiment using a quantitative approach with One-Group Pretest-Posttest design. The population of the research was the eleventh-grade students of SMA Plus Munirul Arifin NW Praya in academic year 2018/2019. Each class consists of 28 students. The total population was 95 students. In this study, the researcher took one class as a sample. The class was eleventh Grade of MIPA 1 consisting of 28 students as the experiment. The researcher gave treatment to the experimental group and it used Small Group Discussion as the treatment of teaching speaking. The purpose of using the Small Group Discussion was to give new inspiration that can be applied in teaching speaking. Referring to the result pre-test and post-test showed that the sig (2 tailed) &gt; 0.05, it means that the null hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted. So the use of Small Group Discussion in teaching speaking is effective. The value of t-test was higher than the t-value of t-table (t-test 8.5148 &gt; t-table 2.006). It showed that teaching speaking using small group discussion has a positive effect to improve students' speaking skill. Besides that, the result of the mean of post-test was higher than the mean of pre-test (M2 = 18.43 &gt; M1 = 14.25). It means that teaching speaking by using small group discussion was more effective than teaching speaking without using small group discussion. In addition, small group discussion can improve students' speaking skill in the eleventh-grade students of SMA Plus Munirul Arifin NW Praya.
Application of K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column

Vertebral column as a part of backbone has important role in human body. Trauma in vertebral column can affect spinal cord capability to send and receive messages from brain to the body system that controls sensory and motoric movement. Disk hernia and spondylolisthesis are examples of pathologies on the vertebral column. Research about pathology or damage bones and joints of skeletal system classification is rare whereas the classification system can be used by radiologists as a second opinion so that can improve productivity and diagnosis consistency of the radiologists. This research used dataset Vertebral Column that has three classes (Disk Hernia, Spondylolisthesis and Normal) and instances in UCI Machine Learning. This research applied the K-NN algorithm for classification of disk hernia and spondylolisthesis in vertebral column. The data were then classified into two different but related classification tasks: “normal” and “abnormal”. K-NN algorithm adopts the approach of data classification by optimizing sample data that can be used as a reference for training data to produce vertebral column data classification based on the learning process. The results showed that the accuracy of K-NN classifier was 83%. The average length of time needed to classify the K-NN classifier was 0.000212303 seconds.
Meta-Modeling of Big Data visualization layer using On-Line Analytical Processing (OLAP)

Big data is about collecting, storing, managing, processing massive quantities of daa, but it is also about presenting various insights into the collected data through visualization.Visualization layer is located on the top of a layered architecture composed of Data Sources, Ingestion, Storage, Management, Monitoring, and Security layers.While each of these layers has its own challenges, visualization is presenting a particular challenge because of physical limitations of the display area and the chosen mode of data presentation.Along with the visualization modes, OLAP is a powerful technology for data discovery, including capabilities for limitless report viewing, complex analytical calculations, predictive scenario planning, and visualization.Based on our previous comparative studies in which we identified key concepts of visualization layer of major Big Data distributions, we propose in this paper to map this layer to OLAP visualization technology.To achieve this goal, we apply techniques related to Model Driven Engineering "MDE" to propose a universal meta-model for visualization layer in a Big Data systems, in which we integrated a meta-model of OLAP for data presentation.
On the Security of LWE Cryptosystem against Subversion Attacks

Abstract Subversion of cryptography has received wide attentions especially after the Snowden Revelations in 2013. Most of the currently proposed subversion attacks essentially rely on the freedom of randomness choosing in the cryptographic protocol to hide backdoors embedded in the cryptosystems. Despite the fact that significant progresses in this line of research have been made, most of them mainly considered the classical setting, while the research gap regarding subversion attacks against post-quantum cryptography remains tremendous. Inspired by this observation, we investigate a subversion attack against existing protocol that is proved post-quantum secure. Particularly, we show an efficient way to undetectably subvert the well-known lattice-based encryption scheme proposed by Regev (STOC 2005). Our subversion enables the subverted algorithm to stealthily leak arbitrary messages to the outsider who knows the backdoor. Through theoretical analysis and experimental observations, we demonstrate that the subversion attack against the LWE encryption scheme is feasible and practical.
Blind Queries Applied to JSON Document Stores

Social Media, Web Portals and, in general, information systems offer their own Application Programming Interfaces (APIs), used to provide large data sets concerning every aspect of day-by-day life. APIs usually provide data sets as collections of JSON documents. The heterogeneous structure of JSON documents returned by different APIs constitutes a barrier to effectively query and analyze these data sets. The adoption of NoSQL document stores, such as MongoDB, is useful for gathering these data sets, but does not solve the problem of querying the final heterogeneous repository. The aim of this paper is to provide analysts with a tool, named HammerJDB, that allows for blind querying collections of JSON documents within a NoSQL document database. The idea below is that users may know the application domain but it may be that they are not aware of the real structures of the documents stored in the database—the tool for blind querying tries to bridge the gap, by adopting a query rewriting mechanism. This paper is an evolution of a technique for blind querying Open Data portals and of its implementation within the Hammer framework, presented in some previous work. In this paper, we evolve that approach in order to query a NoSQL document database by evolving the Hammer framework into the HammerJDB framework, which is able to work on MongoDB databases. The effectiveness of the new approach is evaluated on a data set (derived from a real-life one), containing job-vacancy ads collected from European job portals.
A Meaningful Visually Secure Image Encryption Scheme

The cipher image generated by the traditional encryption scheme is meaningless, which is easy to attract people's attention in the process of image transmission, making the transmission unsafe. In this paper, we propose a meaningful visually secure image encryption scheme based on compressive sensing and integer wavelet transform. The scheme is divided into two phases. In the phases one, the plain image is compressed and encrypted based on compressive sensing by sparse and Random Bernoulli measurement matrix. Then the pixel position of the compressed image is permuted by the sequence generated from 2D-LASM in order to increase the security of the embedding phase. In the phases two, another color image is used as a carrier image, and then the integer wavelet transform is used to obtain the meaningful image. Specifically, the carrier image is firstly divided into coefficient matrix, and then the compressed cipher image is embedded into the coefficient matrix to obtain the visually secure image. The experimental results show that the scheme has good encryption and feasibility.
Technology in Second Language Education: Overcoming the Digital Divide

The use of information and communication technologies (ICT) can help teachers implement valuable instructional tasks where learners use the second language (L2) during input and output tasks, integrating various types of technologies, applications, and websites. Often, educational actors would argue; however, that limited access to ICT prevents teachers from implementing ICT-enhanced L2 instruction in the classroom. In this paper, we revisit that perspective through an exploration of three conditions related to the use of technologies for second language teaching: the digital divide, teacher education, and proper ICT-enhanced task implementation. Building upon a review of theoretical and empirical research on these conditions, we argue that limited access to ICT is not a determinant factor, as teacher education and careful L2 task planning can help teachers maximize the use of the limited resources available in their school contexts.
Automatic Evaluation of Cancer Treatment Texts for Gist Inferences and Comprehension

Background. It is difficult to write about cancer for laypeople such that everyone understands. One common approach to readability is the Flesch-Kincaid Grade Level (FKGL). However, FKGL has been shown to be less effective than emerging discourse technologies in predicting readability. Objective. Guided by fuzzy-trace theory, we used the discourse technology Coh-Metrix to create a Gist Inference Score (GIS) and applied it to texts from the National Cancer Institute website written for patients and health care providers. We tested the prediction that patient cancer texts with higher GIS scores are likely to be better understood than others. Design. In study 1, all 244 cancer texts were systematically subjected to an automated Coh-Metrix analysis. In study 2, 9 of those patient texts (3 each at high, medium, and low GIS) were systematically converted to fill-the-blanks (Cloze) tests in which readers had to supply the missing words. Participants (162) received 3 texts, 1 at each GIS level. Measures. GIS was measured as the mean of 7 Coh-Metrix variables, and comprehension was measured through a Cloze procedure. Results. Although texts for patients scored lower on FKGL than those for providers, they also scored lower on GIS, suggesting difficulties for readers. In study 2, participants scored higher on the Cloze task for high GIS texts than for low- or medium-GIS texts. High-GIS texts seemed to better lend themselves to correct responses using different words. Limitations. GIS is limited to text and cannot assess inferences made from images. The systematic Cloze procedure worked well in aggregate but does not make fine-grained distinctions. Conclusions. GIS appears to be a useful, theoretically motivated supplement to FKGL for use in research and clinical practice.
World Heritage meets Smart City in an Urban-Educational Hackathon in Rauma

IntroductionDuring recent years, the ‘smart city’ concept has emerged in literature (e.g., Kunttu, 2019; Markkula & Kune, 2018; Öberg, Graham, & Hennelly, 2017; Visvizi & Lytras, 2018). Inherently, the smart city concept includes urban innovation; therefore, simply developing and applying technology is not enough for success.
MetaStrider

Reduction is an operation performed on the values of two or more key-value pairs that share the same key. Reduction of sparse data streams finds application in a wide variety of domains such as data and graph analytics, cybersecurity, machine learning, and HPC applications. However, these applications exhibit low locality of reference, rendering traditional architectures and data representations inefficient. This article presents MetaStrider, a significant algorithmic and architectural enhancement to the state-of-the-art, SuperStrider. Furthermore, these enhancements enable a variety of parallel, memory-centric architectures that we propose, resulting in demonstrated performance that scales near-linearly with available memory-level parallelism.
Structuring the Industry 4.0 Landscape

The industry and research efforts to standardize Industry 4.0 related developments have merged into an unmanageable amount of reference models, architectures and specification activities.As these efforts have only been roughly coordinated, am incomprehensible and confusing landscape occurred.These developments contradict the initial need for more clarity and structure, especially as many different aspects are framed under the same terminology.We contribute to this challenge by providing a structured overview of the current state of standardization.We create a graphical landscape of Industry 4.0 specifications and standards based on an integrated knowledge graph.Various views provide different illustrations for several information needs.Explicitly stated relations between Industry 4.0 frameworks and technical standards enable the flexible discovery of related information.In addition, we use machine inference techniques to add new links and to further extend the knowledge graph.
Robust Optimal Control of Omni-directional Mobile Robot using Model Predictive Control Method

This paper proposes a model predictive control method of omni-directional mobile robot to realize robust optimal control. An omni-directional mobile robot based on mecanum wheel is designed. It can move instantaneously in any direction from any state. However, slipping happens easily which leads to bad performance of control. Imprecision of encoder and location system makes it unsteady of mobile robot’s motion. This leads to more cost of battery energy. So, in order to design effective control method, both kinematics and dynamics model of omni-directional mobile robot are established. A robust model predictive control method is then proposed to achieve path tracking of omni-directional robot. To prove effectiveness of proposed method, comparison experiment is then carried out.
Deep Triplet-Driven Semi-supervised Embedding Clustering

In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.
Random S-Boxes Generation Methods for Symmetric Cryptography

The main efficiency indicators of cryptographic Boolean functions and vector mappings, which are used as nodes of the complexity in symmetric cryptographic transformations, are substantiated. Methods of heuristic generation of cryptographic Boolean functions and nonlinear S-boxes for symmetric ciphers that meet the established security requirements are investigated. Prospects for further research with the aim of improving heuristic methods for the synthesis of random S-boxes are substantiated.
An Efficient Representation Using Harmony Search for Solving the Virtual Machine Consolidation

A data center with a large number of servers, large storage, and many network devices requires power for cooling to reduce heat generation, air conditioning, and emergency power generation facilities, in addition to power for operation internally consumed by infrastructure equipment. The power consumed by data centers worldwide makes up a large proportion. Although the size of data centers is expected to increase, we are already faced with power problems because stability is prioritized over efficiency when operating data centers in order to meet the Service Level Agreement (SLA) conditions. Most data centers are in a virtualization environment, and virtual machine consolidation using physical machine (PM) transitions to the idle mode through virtual machine (VM) migration has been suggested as one of the most effective ways to reduce the amount of power usage in a data center. This study takes into account the characteristics of virtualization environments and presents an algorithm that effectively solves VM consolidation (VMC) through operator design using a grouping representation method and a meta-heuristic method known as harmony search.
Test Metrics for Recurrent Neural Networks.

Recurrent neural networks (RNNs) have been applied to a broad range of applications such as natural language processing, drug discovery, and video recognition. This paper develops a coverage-guided testing approach for a major class of RNNs -- long short-term memory networks (LSTMs). We start from defining a family of three test metrics that are designed to quantify not only the values but also the temporal relations (including both step-wise and bounded-length) learned through LSTM's internal structures. While testing, random mutation enhanced with the coverage knowledge, i.e., targeted mutation, is designed to generate test cases. Based on these, we develop the coverage-guided testing tool testRNN. To our knowledge, this is the first time structural coverage metrics are used to test LSTMs. We extensively evaluate testRNN with a variety of LSTM benchmarks. Experiments confirm that there is a positive correlation between adversary rate and coverage rate, evidence showing that the test metrics are valid indicators of robustness evaluation. Also, we show that testRNN effectively captures erroneous behaviours in RNNs. Furthermore, meaningful information can be collected from testRNN for users to understand what the testing results represent. This is in contrast to most neural network testing works, and we believe testRNN is an important step towards interpretable neural network testing.