Reviewerly/exHarmony huggingface dataset:
Assessing Students’ Ability in Writing Argumentative Essay at SMAN 1 Soe Kab. Timur Tengah Selatan NTT

This research aims at finding out the students’ ability in writing argumentative essay at SMAN 1 Soe Kab. Timur Tengah Selatan NTT. The researcher will focus on evaluating the students’ writing ability in one aspect of writing namely mechanics. The researcher applied quantitative method and descriptive analysis design. The research was conducted in August 2017. This research was conducted at SMAN 1 Soe Kabupaten Timur Tengah Selatan NTT.. The population of this research was the eleventh grade students at SMAN 1 Soe Kabupaten Timur Tengah Selatan NTT in the 2018/2019 academic year. The number of population is 142 in four classes. The researcher took 5 students from each class as the sample of the research. Therefore, there were 20 students as sample. The test was used to find out the students’ ability in writing argumentative essay. In collecting data, the researcher asked the students to write an argumentative essay by choosing one of the give topics. To analyze the data, the researcher focused on evaluating the students’ writing in mechanics criteria. The result of the research shows that the students still have low ability in using mechanics in writing argumentative essay. The mean score of students is 2.45. In the students’ writing, most of the students got problem in: (1) The use of capital in the beginning of a sentence. Many of students still use small letter when they write the first letter in the beginning of a paragraph. (2)The students still use some capital letter in the middle of sentences. They write with capital letter in not appropriate position. (3) Some students still do not understand about the use of comma and point in a sentence.
Understanding the Proxy Ecosystem: A Comparative Analysis of Residential and Open Proxies on the Internet

Proxy servers act as an intermediary and a gateway between users and other servers on the Internet, and have many beneficial applications targeting the privacy of users, including bypassing server-side blocking, regional restrictions, etc. Despite the beneficial applications of proxies, they are also used by adversaries to hide their identity and to launch many attacks. As such, many websites restrict access from proxies, resulting in blacklists to filter out those proxies and to aid in their blocking. In this work, we explore the ecosystem of proxies by understanding their affinities and distributions comparatively. We compare residential and open proxies in various ways, including country-level and city-level analyses to highlight their geospatial distributions, similarities, and differences against a large number of blacklists and categories therein, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> spam and maliciousness analysis, to understand their characteristics and attributes. We conclude that, while aiming to achieve the same goal, residential and open proxies still have distinct characteristics warranting considering them separately for the role they play in the larger Internet ecosystem. Moreover, we highlight the correlation of proxy locality distribution and five country-level characteristics, such as their Internet censorship, political stability, and Gross Domestic Product (GDP).
Fraud prevention and detection in a blockchain technology environment: challenges posed to forensic accountants

This paper set out to explore the challenges posed by blockchain to forensic accountants in the prevention and detection of fraud. Blockchain will create a decentralised environment where transactions and data have no third-party control. This technology is capable of disrupting accounting and audit because it is capable of automating financial records and audit processes. The fraud analysis in a digital environment is complex and the evolution of new technologies or innovations such as blockchain, artificial intelligence, and robotics have added to these challenges. The framework for analysis adopted is a qualitative study using the library research methodology. The findings portray that blockchain technology is not 100% flawless, impenetrable to malicious attacks and hacking. The results of the study found that technology will affect the core functions of accountants, but the overall effects on the roles of forensic accountants and auditors are still unknown.
Treemap Literacy: A Classroom-Based Investigation

Visualization literacy, the ability to interpret and understand visual designs, has gained momentum in the educational and information visualization communities. The goal of this research is to identify and address barriers to treemap literacy – a popular visual design, with a view to improve a non-expert user’s treemap visualization literacy skills. In this paper we present the results of two years of an information visualization assignment, which are used to identify the barriers to and challenges of understanding and creating treemaps. From this, we develop a treemap visualization literacy test. Then, we propose a pedagogical tool that facilitates both teaching and learning of treemaps and advances treemap visualization literacy. To investigate the efficiency of this educational software, we then conduct a classroom-based study with 25 participants. We identify the properties of treemaps that can hinder literacy and cognition based on the results from the treemap visualization literacy test. Results also provide further support for the use of our tool that had a positive effect on treemap literacy skills of university students.
A Stochastic Multi-layer Algorithm for Semi-discrete Optimal Transport with Applications to Texture Synthesis and Style Transfer

This paper investigates a new stochastic algorithm to approximate semi-discrete optimal transport for large-scale problem, i.e., in high dimension and for a large number of points. The proposed technique relies on a hierarchical decomposition of the target discrete distribution and the transport map itself. A stochastic optimization algorithm is derived to estimate the parameters of the corresponding multi-layer weighted nearest neighbor model. This model allows for fast evaluation during synthesis and training, for which it exhibits faster empirical convergence. Several applications to patch-based image processing are investigated: texture synthesis, texture inpainting, and style transfer. The proposed models compare favorably to the state of the art, either in terms of image quality, computation time, or regarding the number of parameters. Additionally, they do not require any pixel-based optimization or training on a large dataset of natural images.
Impact of Farmers' Livelihood Capital Differences on Their Livelihood Strategies in Three Gorges Reservoir Area

Yin, K. and Xiao, Y., 2020. Impact of farmers' livelihood capital differences on their livelihood strategies in Three Gorges Reservoir Area. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 258–262. Coconut Creek (Florida), ISSN 0749-0208.In this study, use the binary and multivariate logistic regression models were used to analyze the impact of farmers' livelihood capital differences on their livelihood strategies. The results show that: (1) Farmers in Three Gorges Reservoir Area have the highest overall scores for physical capital, financial capital is second, social capital is at the middle-low level, and human capital and natural capital are at low levels. (2) The total capital value of different types of farmers shows the same law, that is, professional agriculture types>non-agricultural types>non-agricultural -based and agricultural supplement types>agriculture-based and non-agricultural supplement types>traditional agricultural types. (3) The higher the natural capital of farmers, the more their livelihood strategies tend to choose non-agricultural livelihood strategies such as traditional agriculture, professional agriculture, and agricultural-based part-time employment. The increase of the remaining four major capitals, which will obviously promote the transition of farmers to non-agricultural and more professional agricultural livelihood strategies. In response to the above research results, the government should strengthen its commitment to the development of local education; Optimize land space allocation and revitalize agricultural land resources through land circulation and other methods; Create a good external infrastructure environment and actively guide and support the development of large professional households, family farms, and farmers' cooperatives; Provide financial support, improve the level of mechanization through comprehensive management of rural land. Guide farmers improve the quality and livelihood of farmers.
Enhancing human resource management in process improvement projects

Effectively managing human resources is an essential part of achieving the goals of any project. It can be challenging, however, to identify and then classify human resources into appropriate roles, particularly in the early stages of a project. To address this issue, a modified approach for managing project human resources is proposed that focuses on reducing/eliminating the omission and/or misclassification of these resources. Given the motivation from a case example of a process improvement project, this approach was developed to support the inclusion/reclassification of project team members and/or project stakeholders throughout the life cycle of a project. The focus of our proposed approach leverages learning opportunities throughout the initial stages of a project as project management tools and process improvement methods are used to investigate a problem and integrates that knowledge within the project through an augmented change request process. More specifically, the proposed approach equips project teams with the knowledge needed to question the appropriateness of a project’s human resources and their classification into various roles. By improving human resource management practices within projects, this research aims to help organizations enhance their project success rates.
Research on Target Tracking Algorithm Using Millimeter-Wave Radar on Curved Road

Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

In this paper, we propose a Distributed Accumulated Newton Conjugate gradiEnt (DANCE) method in which sample size is gradually increasing to quickly obtain a solution whose empirical loss is under satisfactory statistical accuracy. Our proposed method is multistage in which the solution of a stage serves as a warm start for the next stage which contains more samples (including the samples in the previous stage). The proposed multistage algorithm reduces the number of passes over data to achieve the statistical accuracy of the full training set. Moreover, our algorithm in nature is easy to be distributed and shares the strong scaling property indicating that acceleration is always expected by using more computing nodes. Various iteration complexity results regarding descent direction computation, communication efficiency and stopping criteria are analyzed under convex setting. Our numerical results illustrate that the proposed method outperforms other comparable methods for solving learning problems including neural networks.
Proportionally Fair Clustering Revisited

In this work, we study fairness in centroid clustering. In this problem, k cluster centers must be placed given n points in a metric space, and the cost to each point is its distance to the nearest cluster center. Recent work of Chen et al. [Chen et al., 2019] introduces the notion of a proportionally fair clustering, in which no group of at least n/k points can find a new cluster center which provides lower cost to each member of the group. They propose a greedy capture algorithm which provides a 1+√2 approximation of proportional fairness for any metric space, and derive generalization bounds for learning proportionally fair clustering from samples in the case where a cluster center can only be placed at one of finitely many given locations in the metric space. We focus on the case where cluster centers can be placed anywhere in the (usually infinite) metric space. In case of the L² distance metric over ℝ^t, we show that the approximation ratio of greedy capture improves to 2. We also show that this is due to a special property of the L² distance; for the L¹ and L^∞ distances, the approximation ratio remains 1+√2. We provide universal lower bounds which apply to all algorithms. We also consider metric spaces defined on graphs. For trees, we show that an exact proportionally fair clustering always exists and provide an efficient algorithm to find one. The corresponding question for general graph remains an interesting open question. Finally, we show that for the L² distance, checking whether a proportionally fair clustering exists and implementing greedy capture over an infinite metric space are NP-hard problems, but (approximately) solvable in special cases. We also derive generalization bounds which show that an approximately proportionally fair clustering for a large number of points can be learned from a small number of samples. Our work advances the understanding of proportional fairness in clustering, and points out many avenues for future work.
Towards a Computational Measure of Plot Tellability

Measuring the quality of plot is a desirable feature for computational narrative systems.One of the notions of plot quality used in narrative theory is called tellability, which can be derived from certain structural properties, namely the types of events present and the way they are connected.These structures include not only actualized events, but also take into account virtual plans and the affective valencies of events.The present paper introduces Marie-Laure Ryan's tellability principles and suggests to computationally model them using an affective multi-agent simulation system.It discusses how such an approach implies a broader understanding of plot than commonly assumed and analysis several existing narrative systems under these considerations.Furthermore, it introduces a plot-graph formalism that allows the computational representation and analysis of the extended plot understanding.An approach to automatically generating the plot-graph is suggested in the context of the introduced multi-agent simulation system.
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning

The performance of the code a compiler generates depends on the order in which it applies the optimization passes. Choosing a good order--often referred to as the phase-ordering problem, is an NP-hard problem. As a result, existing solutions rely on a variety of heuristics. In this paper, we evaluate a new technique to address the phase-ordering problem: deep reinforcement learning. To this end, we implement AutoPhase: a framework that takes a program and uses deep reinforcement learning to find a sequence of compilation passes that minimizes its execution time. Without loss of generality, we construct this framework in the context of the LLVM compiler toolchain and target high-level synthesis programs. We use random forests to quantify the correlation between the effectiveness of a given pass and the program's features. This helps us reduce the search space by avoiding phase orderings that are unlikely to improve the performance of a given program. We compare the performance of AutoPhase to state-of-the-art algorithms that address the phase-ordering problem. In our evaluation, we show that AutoPhase improves circuit performance by 28% when compared to using the -O3 compiler flag, and achieves competitive results compared to the state-of-the-art solutions, while requiring fewer samples. Furthermore, unlike existing state-of-the-art solutions, our deep reinforcement learning solution shows promising result in generalizing to real benchmarks and 12,874 different randomly generated programs, after training on a hundred randomly generated programs.
Examination of polarization coupling in a plucked musical instrument string via experiments and simulations

In this article, the transient motion of a realistically plucked guitar string is studied experimentally and numerically in both transversal polarizations. The frequency dependent damping and suitable initial conditions are identified in the experiment and used in a simulation. For this reason an experimental set-up consisting of a string, an excitation mechanism and two laser Doppler vibrometers is developed. The excitation mechanism performs a realistic and reproducible plucking motion with a plectrum. Two laser Doppler vibrometers are used to measure the string oscillation transversally in two polarizations. The experimental set-up makes it possible to measure the string’s motion under reproducible conditions and, hence, at different positions for the same oscillation. This capability renders the identification of suitable initial conditions, i.e., initial displacement and velocity as well as the pre-tension, for a string model possible. Furthermore, a finite element model for the string is developed that takes into account the oscillation in both transversal planes of polarization and the coupling between them. Finally, the model results are in good agreement with the measurements. With help of the numerical model it can be vividly shown that the coupling between the polarizations of the oscillation is due to a torsional movement of the string on the saddle.
Catching Numeric Inconsistencies in Graphs

Numeric inconsistencies are common in real-life knowledge bases and social networks. To catch such errors, we extend graph functional dependencies with linear arithmetic expressions and built-in comparison predicates, referred to as numeric graph dependencies (NGDs). We study fundamental problems for NGDs. We show that their satisfiability, implication, and validation problems are Σ p 2 -complete, Π p 2 -complete, and coNP-complete, respectively. However, if we allow non-linear arithmetic expressions, even of degree at most 2, the satisfiability and implication problems become undecidable. In other words, NGDs strike a balance between expressivity and complexity. To make practical use of NGDs, we develop an incremental algorithm IncDect to detect errors in a graph G using NGDs in response to updates Δ G to G . We show that the incremental validation problem is coNP-complete. Nonetheless, algorithm IncDect is localizable, i.e., its cost is determined by small neighbors of nodes in Δ G instead of the entire G . Moreover, we parallelize IncDect such that it guarantees to reduce running time with the increase of processors. In addition, to strike a balance between the efficiency and accuracy, we also develop polynomial-time parallel algorithms for detection and incremental detection of top-ranked inconsistencies. Using real-life and synthetic graphs, we experimentally verify the scalability and efficiency of the algorithms.
Correction to: Multi-criteria collaborative filtering recommender by fusing deep neural network and matrix factorization

An amendment to this paper has been published and can be accessed via the original article.
An Application of Ternary Hash Retrieval Method for Remote Sensing Images in Panoramic Video

With the development of multimedia technology, the application of panoramic video is emerging, and there are specific application scenarios in the field of drones. Remote sensing images have tremendous applications in data acquisition and processing in the panoramic maps used to construct the panoramic videos. Efficient remote sensing image retrieval can greatly improve the efficiency of image utilization and get a fast response in the construction of panoramic video. Thus, how to achieve fast and efficient remote sensing image retrieval becomes increasingly important. Scholars have explored a number of ways to address this problem, from traditional manual feature-based retrieval methods to deep hash retrieval methods that today can cope with large data volumes. The hash method is a way to encode high-dimensional data into a set of binary codes while maintaining similarity between images. In this paper, we propose an end-to-end ternary label depth hashing method that is capable of both feature learning and hash coding processes in one-stage. The experimental results show that the algorithm has high accuracy and good retrieval effect.
An Intelligent Platform for Offline Learners Based on Model-Driven Crowdsensing Over Intermittent Networks

Despite the continuous growth of global Internet users, almost 4 billion people do not use the Internet. The offline population includes people who live in developing regions or rural aging communities. In this context, we propose a learning-support platform for learners without an easy, reliable, and affordable means to access digital learning environments on the Internet. Unlike existing systems that provide little support for efficient educational data collection from offline learners, our proposed platform combines delay-tolerant networking mechanisms and active learning-based model-driven crowdsensing techniques to deliver learning materials and collect educational data efficiently.