Reviewerly/exHarmony huggingface dataset:
Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets

Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine learning repository using support vector regression as the underlying machine learning algorithm, with varying number of data sources and sizes. The results show that the proposed method produces conservatively valid prediction intervals, and while we cannot retain the same efficiency as when all data is used, efficiency is improved through the proposed approach as compared to predicting using a single arbitrarily chosen source.
An Overview of the Application of Data Mining Technology in E-commerce

Data mining technology is an effective data analysis and processing technology. It analyzes and processes sales data according to corresponding association rules, classification, clustering, and prediction techniques, and discovers the hidden knowledge in the data, which in turn can be a marketing strategy. Decision making and product development provide decision-making and have a wide range of applications in e-commerce. This paper first expounds the data mining technology and e-commerce concept, then analyzes and summarizes the research results of domestic and foreign scholars, the application status and development of data mining in e-commerce, and finally the application of data mining technology in optimizing e-commerce website design. And the application in business customer relationship management is fully discussed and researched to explore the convergence of e-commerce development and data mining, and comprehensively promote technological innovation and application.
INDEPENDENCE OF THE CENTRAL BANK OF IRAQ AND ITS IMPACT ON ECONOMIC PERFORMANCE FOR THE PERIOD OF 2009-2018

A pattern of transfer of authority to central banks regarding monetary policy has witnessed. Expanding central or state bank freedom is an agreed formula for governments to stabilize the monetary position of country. The focus of previous studies on estimating state bank freedom centers on the developed countries. Therefore, this study is an attempt to fill the gap in literature on the effects of central bank autonomy on economic performance. This paper endeavors to measure the level of freedom in the state bank of Iraq to record its impact on economic performance over the period of 2009 to 2018. The study utilizes the vector error correction model to capture the long run relationship between independence of central bank and economic performance. The study found a negative influence of central bank independence on inflation, real rate of interest and unemployment rate. Furthermore, a positive impact of central bank independence on GDP growth and per capita GDP has witnessed in Iraq. This study provides proof in help the hypotheses stressing the unbiased monetary policy. This study recommends the monetary performance benefits of central bank freedom. This study discovers that it is feasible for countries to accomplish the advantages of effective monetary policy by protecting the central bank from political control.
West German Government’s Technical Assistance to South Korea: Focusing on the Hohmanneum / Korean-German Technical School

The West German government’s assistance to South Korea was the third largest, following the U.S and Japan. One characteristic of West German technical assistance was the partner projects, which meant not only sharing the costs but also the responsibility for the project. This research focuses on one project of the West German governments technical assistance, namely the technical school ‘Hohmanneum’ / ‘Korean-German Technical School’. The German technical consultant Fritz Hohmann founded the Hohmanneum in 1964 and applied at the West German Ministry for Development for technical assistance. However, in 1967 it was discovered that Hohmann had sexually abused more than 25 of his students. He had to leave the school, which was thereafter handed over to a Korean private foundation. To restore the ‘German honor’, the German government decided to continue with the assistance and subsequently operated the school as a partner project from 1969 to 1973 under the new name ‘Korean-German Technical School’.BRThe private foundations, the two governments and the actors on-site each had different expectations on the direction of the project’s implementation and negotiated it on-site. rough these negotiations the project was continuously adjusted and adapted to the Korean circumstances. Besides, through the Hohmanneum / Korean-German Technical School an analysis of the developmental discourse in Korea and of changes in the West German assistance subjects and policies was possible. Further research should be conducted on other technical schools supported by the West German government and their representativeness as assistance projects.
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relationship between users and items (e.g., movies), we extend this view to the tripartite relationship between users, datasets and services, which is present in data markets. Between these entities, we identify four use cases for recommendations: (i) recommendation of datasets for users, (ii) recommendation of services for users, (iii) recommendation of services for datasets, and (iv) recommendation of datasets for services. Using the open Meta Kaggle dataset, we evaluate the recommendation accuracy of a popularity-based as well as a collaborative filtering-based algorithm for these four use cases and find that the recommendation accuracy strongly depends on the given use case. The presented work contributes to the tripartite recommendation problem in general and to the under-researched portfolio of evaluating recommender systems for data markets in particular.
Development of Atlas, a flexible data structure framework

This document is one of the deliverable reports created for the ESCAPE project. ESCAPE stands for Energy-efficient Scalable Algorithms for Weather Prediction at Exascale. The project develops world-class, extreme-scale computing capabilities for European operational numerical weather prediction and future climate models. This is done by identifying Weather &amp; Climate dwarfs which are key patterns in terms of computation and communication (in the spirit of the Berkeley dwarfs). These dwarfs are then optimised for different hardware architectures (single and multi-node) and alternative algorithms are explored. Performance portability is addressed through the use of domain specific languages. In this deliverable report, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. Atlas provides data structures for building various numerical strategies to solve equations on the sphere or limited area's on the sphere. These data structures may contain a distribution of points (grid) and, possibly, a composition of elements (mesh), required to implement the numerical operations required. Atlas can also represent a given field within a specific spatial projection. Atlas is capable of mapping fields between different grids as part of pre- and post-processing stages or as part of coupling processes whose respective fields are discretised on different grids or meshes.
Slide Reduction, Revisited---Filling the Gaps in SVP Approximation

We show how to generalize Gama and Nguyen's slide reduction algorithm [STOC '08] for solving the approximate Shortest Vector Problem over lattices (SVP). As a result, we show the fastest provably correct algorithm for $\delta$-approximate SVP for all approximation factors $n^{1/2+\varepsilon} \leq \delta \leq n^{O(1)}$. This is the range of approximation factors most relevant for cryptography.
A Fast and Accurate One-Stage Approach to Visual Grounding

We propose a simple, fast, and accurate one-stage approach to visual grounding, inspired by the following insight. The performances of existing propose-and-rank two-stage methods are capped by the quality of the region candidates they propose in the first stage --- if none of the candidates could cover the ground truth region, there is no hope in the second stage to rank the right region to the top. To avoid this caveat, we propose a one-stage model that enables end-to-end joint optimization. The main idea is as straightforward as fusing a text query's embedding into the YOLOv3 object detector, augmented by spatial features so as to account for spatial mentions in the query. Despite being simple, this one-stage approach shows great potential in terms of both accuracy and speed for both phrase localization and referring expression comprehension, according to our experiments. Given these results along with careful investigations into some popular region proposals, we advocate for visual grounding a paradigm shift from the conventional two-stage methods to the one-stage framework.
High Speed and Low Area Energy Efficient FPGA Implementation using RSD based Elliptic Curve Cryptography

In this paper we will study high speed energy efficient FPGA implementation using redundant sign digit elliptic curve cryptography. In existing system to design processor with high area and low speed computation process so that we proposed to low area with high speed computation using Elliptical cryptography processor. Here we use redundant signed digit to reduce the overhead due to computations. For modular arithmetic operation for multiplication, we used Karatsuba-Ofman algorithm; an efficient modular adder without comparison and a high throughput modular divider using GCD, which results in a short data path for maximum frequency, Processor arithmetic operation consist of 256 bits. This proposed processor supports P256 NIST prime field curve standards using Xilinx 14.7 software we analyze logic size, area and power consumption. Keywords: Application-specific instruction-set processor (ASIP), elliptic curve cryptography (ECC), field programmable gate array (FPGA), Karatsuba-Ofman multiplication, redundant signed digit (RSD), Greatest common divisor (GCD)
Systemic Functional Genre on Students’ Reading Literacy

In the context of higher education, IT’s popularity and transparency has increased students’ higher awareness of writing in different evolving forms. It is undeniable that publishing scientific research is now seen more as success determinant in academic careers. Along with this demand, students need to deepen their advanced literacy and academic language skills to achieve good scientific writing. The aim of the study was to investigate the impact of Systemic Functional Genre approach in students’ reading literacy. A quantitative method of quasi-experimental design was carried out in this study. There were two groups involved: the experimental group (EG) and the control group (CG). During the implementation, both groups experienced in the different treatments. The population selected in this study was the second-semester learners of English Department, Muhammadiyah University of Jakarta. In selecting the sample, the researchers purposively choose two groups (n=60 students). To collect the data, the researchers implemented a reading test. Based on the research finding, the study indicated that the SFG-based Approach had a significant positive impact on improving the students’ reading literacy.
The Correlation Between Students’ Habit In Writing Diary And Students Ability In Writing Recount Text

The research entitled “The Correlation between Students’ Habit in Writing Diary and Students’ Ability in Writing Recount Text”. It was aimed to find out the significant correlation between students’ habit in writing diary and students’ ability in writing recount text. Quantitative method was used to find out the correlation between two variables. To find out the significant correlation between students’ habit in writing diary and students’ ability in writing recount text, this paper used ex post facto. The data of writing recount text result was counted with Pearson Product Momment. The result of Pearson test showed tobserved was 0.17and ttable was 2.1604. The writer used significant value 5% or 0.05. It means that H0 was accepted and Ha was rejected. Based on the result of Pearson Product Momment, there were no significant correlation between students’ habit in writing diary and students’ ability in writing recount text.
Hacking Stuff

In an effort to keep up with a world of too much, life hackers sometimes risk going too far.The open access edition of this book was made possible by generous funding from the MIT Libraries. Life hackers track and analyze the food they eat, the hours they sleep, the money they spend, and how they're feeling on any given day. They share tips on the most efficient ways to tie shoelaces and load the dishwasher; they employ a tomato-shaped kitchen timer as a time-management tool.They see everything as a system composed of parts that can be decomposed and recomposed, with algorithmic rules that can be understood, optimized, and subverted. In Hacking Life, Joseph Reagle examines these attempts to systematize living and finds that they are the latest in a long series of self-improvement methods. Life hacking, he writes, is self-help for the digital age's creative class.Reagle chronicles the history of life hacking, from Benjamin Franklin's Poor Richard's Almanack through Stephen Covey's 7 Habits of Highly Effective People and Timothy Ferriss's The 4-Hour Workweek. He describes personal outsourcing, polyphasic sleep, the quantified self movement, and hacks for pickup artists. Life hacks can be useful, useless, and sometimes harmful (for example, if you treat others as cogs in your machine). Life hacks have strengths and weaknesses, which are sometimes like two sides of a coin: being efficient is not the same thing as being effective; being precious about minimalism does not mean you are living life unfettered; and compulsively checking your vital signs is its own sort of illness. With Hacking Life, Reagle sheds light on a question even non-hackers ponder: what does it mean to live a good life in the new millennium?
Enriching the Summer Palace’s Cultural Experience

Cultural Heritage occurs when visitors tell stories of exceptional experiences at historic sites - after their visit occurs. For the Summer Palace’s directors to create exceptional experiences, exhibition designers must understand how to tell stories that have great meaning to the Summer Palace’s national and international visitors. Our research targets this question – we propose developing innovative and unique storytelling methods through interactive exhibitions throughout the Summer Palace to suspend the visitor’s sense of reality; increasing their sense of flow, suggestion, and engagement. We suggest the Summer Palace for this research project due to its status as a UNESCO World Heritage Site seeking to increase its international reputation. If directors and exhibition designers understand how advanced technology, including immersive exhibitions and augmented reality, can meet educational, enjoyment, and emotional needs of the Summer Palace’s diverse visitors during festivals and tourist visits, we may increase existing visitor’s engagement, and capture new tourist groups - who previously ignored the Palace. We hope our work shall ultimately contribute to making the Summer Palace China’s foremost Cultural Heritage Site and festival epicentre.
Design and Experimental Evaluation of a Multi-Ship Sensor-Based 3D World Modeling Architecture

There is emerging demand for multi-ship sensor-based 3D world modeling (3DWM) for autonomy/cognitive decision aiding avionics applications. In these systems, multiple ships collect and transmit perception sensor data that is fused into a common 3DWM, which is then used by other platforms for flight guidance in that environment. This paper illustrates key design considerations for these systems by exploring the fundamental scenario of leader-follower. This paper will detail the design trade space for the leader-follower scenario, focusing on 3DWM database representation/processing and data transmission. To demonstrate the feasibility of a baseline design approach on modern computing hardware, results will be presented from an experimental evaluation of a proof-of-concept system.
LibGuides: Alumni Access to e-Resources: Open Access and Free Resources

A guide for alumni on how to access select Goldsmiths e-Resources from home. A list of selected Open Access and free online resources relevant to subjects taught at Goldsmiths.
Recognizing Part Attributes with Insufficient Data

Recognizing attributes of objects and their parts is important to many computer vision applications. Although great progress has been made to apply object-level recognition, recognizing the attributes of parts remains less applicable since the training data for part attributes recognition is usually scarce especially for internet-scale applications. Furthermore, most existing part attribute recognition methods rely on the part annotation which is more expensive to obtain. To solve the data insufficiency problem and get rid of dependence on the part annotation, we introduce a novel Concept Sharing Network (CSN) for part attribute recognition. A great advantage of CSN is its capability of recognizing the part attribute (a combination of part location and appearance pattern) that has insufficient or zero training data, by learning the part location and appearance pattern respectively from the training data that usually mix them in a single label. Extensive experiments on CUB-200-2011 [51], CelebA [35] and a newly proposed human attribute dataset demonstrate the effectiveness of CSN and its advantages over other methods, especially for the attributes with few training samples. Further experiments show that CSN can also perform zero-shot part attribute recognition. The code will be made available at https://github.com/Zhaoxiangyun/Concept-Sharing-Network.
Using data taxonomy to achieve security in cloud computing

Cloud computing is a broad concept pertaining to different service models following the utility computing model. Owing to its numerous advantages like high resource elasticity, time improvement, IT maintenance cost reduction, and simplicity, cloud computing paradigm interests many customers. However, notions introduced by the cloud such as the multi-tenancy concept, the computation outsourcing, and the distributed resources increase the security concerns and make trust in cloud providers a critical security challenge. This paper suggests a new method for enhancing the confidentiality of data in the cloud. As data in the cloud has not the same sensitivity, encrypting it with the same algorithms can lead to a lack of security or of resources. The paper proposes to classify data according to a sensitivity level in order to give a suitable security model for each data. By this process, we optimise the use of security mechanisms and the resources consumption.
UdS Submission for the WMT 19 Automatic Post-Editing Task

In this paper, we describe our submission to the English-German APE shared task at WMT 2019. We utilize and adapt an NMT architecture originally developed for exploiting context information to APE, implement this in our own transformer model and explore joint training of the APE task with a de-noising encoder.
국내 식품냉동창고 온도분포 실태 및 확률분포모델 분석

This study aimed to generate a probability distribution model based on temperature data of frozen food storage facility as input variables for microbial risk assessment (MRA). We visited 8 food-handling businesses to collect temperature data from their cold storage warehouses. The overall mean temperature inside the storage facilities was -20.48 ± 3.08oC, with 20.4% of the facilities having above -18oC, with minimum and maximum temperature values of -10.3 and -25.80oC respectively. Temperature distributions by space locations of natural and forced convection were -22.57 ± 0.84 and -17.81 ± 1.47oC, -22.49 ± 1.05 and -17.94 ± 1.44oC, and -22.68 ± 1.03 and -18.08 ± 1.42oC in the upper (2.4~4 m), middle (1.5~2.4 m), and lower (0.7~1.5 m) shelves, respectively. Probability distributions from the temperature data were obtained using the program @RISK. Statistical ranking was determined using goodness of fit to determine the probability distribution model. Our results show that a log-normal distribution [5.9731, 3.3483, shift (-26.4281)] is most appropriate for relative MRA conduction.
Hierarchical clustering for seismic signal recognition

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Hierarchical clustering for seismic signal recognitionAuthors: Weilin HuangWeilin HuangChina University of Petroleum-Beijing (CUPB)Search for more papers by this authorhttps://doi.org/10.1190/segam2019-3209200.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic signal recognition can serve as a powerful auxiliary tool for analyzing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of unsupervised machine learning (ML). In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points in time domain are clustered into two clusters, i.e. signal or non-signal. The hierarchical clustering (HC) algorithm is used to group these sampling points. Four attributes, i.e. two short-term-average-to-long-term-average ratios (STA/LTAs), variance and envelope are investigated in the clustering process. In addition, to quantitatively evaluate the performance of seismic signal recognition properly, I propose two new statistical indicators, namely the rate between the total energies of original and recognized signals (RTE), and the rate between the average energies of original and recognized signals (RAE). Numerical experiments and applications to real micro-seismic/seismic data demonstrate feasibility and encouraging prospect of the proposed technique.Presentation Date: Tuesday, September 17, 2019Session Start Time: 1:50 PMPresentation Start Time: 1:50 PMLocation: Poster Station 9Presentation Type: PosterKeywords: algorithm, machine learning, interpretationPermalink: https://doi.org/10.1190/segam2019-3209200.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Weilin Huang, (2019), "Hierarchical clustering for seismic signal recognition," SEG Technical Program Expanded Abstracts : 4087-4091. https://doi.org/10.1190/segam2019-3209200.1 Plain-Language Summary Keywordsalgorithmmachine learninginterpretationPDF DownloadLoading ...