Reviewerly/exHarmony huggingface dataset:
KEPRIBADIAN TOKOH UTAMA DAN NILAI KERJA KERAS DALAM NOVEL RANTAU 1 MUARA KARYA FUADI SERTA RELEVANSINYA DENGAN PEMBELAJARAN APRESIASI SASTRA DI SMA

&lt;em&gt;This study aims to describe and explain (1) the intrinsic element;(2) personality aspect of the main character;(3) character education; and(4) the relevance of the personality of the main character and the value of character education in the novel Rantau 1 Muara Fuadi with learning literature appreciation in High School.This research is a descriptive-qualitative research which used psychology literature approach. The data collection techniques used in this study were document analysis techniques and interviews. The data validity techniques used were data triangulation and source triangulation.The results of this research showed that the structural elements that build include: the theme of education, groove mix, setting a place located in Bandung, Jakarta, Maninjau, America, and England, the personality, the standpoint of using the technique of acknowledgment and the message to consistently pursue ideals.Through the three dimensional character is obtained figures of Alif and Dinara. The personality of the main characters in the novel, analyzed using Freud’s psychoanalytic theory which includes three personality structures Id, Ego, and Superego. Novel Rantau 1 Muara also contains the value of hard work education found on the main characters, Alif and Dinara. Novel Rantau 1 Muara is relevant to the learning of literature appreciation in high school class XII.&lt;/em&gt;
Investigations of the thermomechanical behavior of a coarse-grained aluminum multicrystal using Constrained full-field measurements methods

With the intention of achieving an experimental grain scale energy balance at finite strain and at the grain scale, a mechanical test on a coarse-grained aluminium is presented in this paper using two complementary imaging techniques based on visible and infrared light. Specific image processing methods referred to as Constrained Digital Image Correlation (Constrained DIC) and Constrained InfraRed Thermography (Constrained IRT) are applied to investigate the thermomechanical behavior at the microstructural scale. Constrained DIC is used to obtain displacement and strain fields during the test, while Constrained IRT provides an estimate of temperature and heat source fields induced by the mechanical loading. The proposed “constrained” methods allow to enforce an adjustable level of constraints on a measured field (displacement or temperature) without referring to a specific finite-element description. In that manner, it is possible to decouple the measurement model and the interpretation model while keeping regularizing constraints (such as continuity of the fields). In this paper, we mainly focus on the kinematic analysis of the experimental test. Electron Backscatter Diffraction (EBSD) is also used in this case to experimentally characterize the microstucture of a 3 mm thick specimen with centimetric grain size.
Workplace Violence and Social Engineering Among Korean Employees

The purpose of this study is to investigate if workplace violence has a negative influence on employees who are exposed to social engineering. This article explores if information security culture can be helpful to make them to resist social engineering. In the results, first, job-related bullying and abusive supervision decreases employees' intention to resist social engineering. Second, information security culture decreases the negative effect of job-related bullying, abusive supervision or organizational politics on employees' intention to resist social engineering.
Sense of Presence and Learning Satisfaction among Students of Different Age Groups in a 3-D Virtual World

Virtual worlds are growing in popularity very quickly.This growing popularity of 3-dimensional (3-D) virtual worlds has drawn attention from educationists.Today, 3-dimensional (3-D) virtual worlds are exploited for online and virtual learning.Unlike the common online learning platforms, a virtual world environment closely resembles a 3-D video games environment.Thus the age of students might affect their sense of presence, interaction, and satisfaction in the said environment.Hence this study was conducted to investigate whether there are differences between students of different age groups on their sense of presence (place presence, social presence, and co-presence) and their learning satisfaction.The study was carried out for six weeks and involved 33 part-time diploma students with the use of interview and questionnaires as instruments.In this study, the researcher developed our own 3-D virtual world, known as ViEW, by using the Open Wonderland open source virtual world program.A nonparametric Mann-Whitney U analysis was applied to explore the differences between young and senior participants in terms of their sense of place presence, social presence, co-presence, and learning satisfaction.The results indicated significant differences between young and senior students in terms of place presence, co-presence, and learning satisfaction, but no differences were identified for social presence.These results might be in regard with the means of conducted the learning, which were in the forms of cooperative and synchronous learning by utilizing audio communication most of the time.Several recommendations for future research related to the study were also provided.
The use of dictogloss technique on ELT classroom: An experiment study of students listening comprehension

The objective of the research was to find out whether or not the use of English dictogloss technique is able to improve listening comprehension of the second year students of SMP Negeri 3 Dua Pitue. This research employed a quasi-experimental method that applied Experimental and control class. The population of this research was eighth-grade students (VIII) of SMP Negeri 3 Dua Pitue in 2014-2015 academic years. A total number of population was 48 students and taken a sample by using total sampling technique. The instrument that used in this research was listening comprehension test. The result of the data of the data analysis showed the mean score of the experimental group got the mean score (77.0) and while the control group gained mean score (63.3) this showed that there was significant difference between the students who were through by using English dictogloss technique and those who were without using English dictogloss. The researcher found that the t-test value was lower than t-table (0.112 2.021). It means that the alternative hypothesis (H1) was accepted and the null hypothesis (H0) was rejected. It can be concluded that the students hadgood listening comprehension achievement by using English dictogloss.
Philosophical Approaches to Intellectual Property Law Scholarship

Abstract Intellectual property (IP) law and philosophy is an interdisciplinary approach to scholarship that applies insights and methods from philosophy to the legal, normative, theoretical, political, and empirical questions presented by the project of organizing and regulating the creation and dissemination of knowledge, technology, and culture. In this chapter, I outline four types of IP-law-and-philosophy scholarship, focusing specifically on the discipline of analytic philosophy (with appropriate caveats about the coherence of that discipline). These modes of scholarship can be categorized as: (1) the jurisprudence of the IP system; (2) philosophical analysis of IP law; (3) applied philosophy in IP; and (4) normative theory of IP. Category (4) is obviously a special case of category (3), focusing specifically on applications of moral philosophy. Within each category, I provide illustrative examples of past scholarship and suggestions for further research.
Author identifier analysis: Name authority control in two institutional repositories

The aim of this poster is to analyze name authority control in two institutional repositories to determine the extent to which faculty researchers are represented in researcher identifier databases. A purposive sample of 50 faculty authors from Florida Southern College (FSC) and Ryerson University (RU) were compared against five different authority databases: Library of Congress Name Authority File (LCNAF), Scopus, Open Researcher and Contributor ID (ORCID), Virtual International Authority File (VIAF), and International Standard Name Identifier (ISNI). We first analyzed the results locally, then compared them between the two institutions. The findings show that while LCNAF and Scopus results are comparable between the two institutions, the difference in the ORCID, VIAF, and ISNI are considerable. Additionally, the results show that the majority of authors at each institution are represented in two or three external databases. This has implications for enhancing local authority data by linking to external identifier authority data to augment institutional repository metadata.
Pedestrian Retrieval Using Generated Samples and Multistream Layer in Sensor Networks

In this paper, we propose a novel loss function named the hybrid quadruplet loss (HQL) to utilize the generated samples for pedestrian retrieval in sensor networks. The proposed HQL employs a set of quadruplets in order to maintain an appropriate margin between the real sample and the generated sample, reduce the intra-class variations and enlarge the inter-class variations. By this way, the generalization of the deep model could be improved. Furthermore, to identify the extremely similar pedestrians, we propose a novel multistream layer to mine imperceptible information from different aspects. The proposed multistream layer utilizes various filters with different morphologies to capture discriminative features in multiple scales, and it is flexible to follow any convolutional layer. Experiments on the three large-scale pedestrian retrieval databases (Market1501, CUHK03, and DukeMTMC-reID) demonstrate that the proposed method outperforms other state-of-the-art methods.
Pemahaman dan Preferensi Bahasa Masyarakat Indonesia pada Istilah Komputer dan Internet

Technology, including computers and internet, cannot be separated from Indonesians. At the beginning of computers and internet existence in Indonesia, the government took an initiative to make computers and internet equivalent terms through Presidential Decree No. 2 Year 2001. This aimed to ease Indonesians in using computers and/or internet. This effort then generated “Senarai Padanan Istilah" consisting of 711 equivalents from 629 English terms. This study aims to investigate Indonesians’ comprehension, language preference, including their reason in choosing a particular language regarding computer and/or internet terms. The data were obtained from online questionnaire. The data obtained then were used to investigate Indonesians’ comprehension of the terms as well as their language preference relating to the computer and/or internet terms. The results are then presented descriptively. This research results in: 1) Indonesians have a fairly high understanding related to computer and/or internet equivalent terms; 2) however, Indonesians prefer English to bahasa Indonesia, as many as 91.9% of the respondents; 3) English preference by the respondents is motivated by English terms familiarity, English terms ease to be understood, the lack of appropriateness of the equivalent terms, English shorter terms, and the unavailability of equivalent terms in the computer program they run.
Research on iterative repair algorithm of Hyperchaotic image based on support vector machine

The damaged area of the hyperchaotic image is prone to lack of texture information. It needs to make image restoration design to improve the information expression ability of the image. In this paper, an iterative restoration algorithm of hyperchaotic image based on support vector machine is proposed. The sample blocks in the damaged region of hyperchaotic images are divided into smooth mesh structures according to block segmentation method, and the neighborhood pixels of which points need to repair are ranked efficiently according to gradient values. According to the edge fuzzification features, the position of the important structural information of the damaged area is located. A multi-dimensional spectral peak search method is applied to construct the information feature subspace of image texture, so as to find the best matching block for restoring the damaged region of hyperchaotic image. Considering the features of structural information and texture information, the maximum likelihood algorithm is used to reconstruct the pixel elements in the image region by piecewise fitting. Through the support vector machine algorithm, the image iterative restoration is carried out. The simulation results show that the restoration method for hyperchaotic image can achieve effective restoration of image damaged area, the quality of restorationed image is better, and the computation speed is fast. The image restoration method can effectively ensure the visual effect of the reconstructed image.
A Dialectica-Like Interpretation of a Linear MSO on Infinite Words

Abstract We devise a variant of Dialectica interpretation of intuitionistic linear logic for "Equation missing"<!-- image only, no MathML or LaTex -->, a linear logic-based version $$\mathsf {MSO}$$ over infinite words. "Equation missing"<!-- image only, no MathML or LaTex --> was known to be correct and complete w.r.t. Church’s synthesis, thanks to an automata-based realizability model. Invoking Büchi-Landweber Theorem and building on a complete axiomatization of $$\mathsf {MSO}$$ on infinite words, our interpretation provides us with a syntactic approach, without any further construction of automata on infinite words. Via Dialectica, as linear negation directly corresponds to switching players in games, we furthermore obtain a complete logic: either a closed formula or its linear negation is provable. This completely axiomatizes the theory of the realizability model of "Equation missing"<!-- image only, no MathML or LaTex -->. Besides, this shows that in principle, one can solve Church’s synthesis for a given $$\forall \exists $$ -formula by only looking for proofs of either that formula or its linear negation.
Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment

In cloud computing environment, in order to optimize the deployment scheduling of resources, it is necessary to improve the accuracy of the optimal solution, guarantee the convergence ability of the algorithm, and improve the performance of cloud computing. In this paper, a multi-objective optimization algorithm based on improved particle swarm is proposed. A multi-objective optimization model is built. Improved multi-scale particle swarm is used to optimize the built multi-objective model. The combination of the global search capability and the local search capability of the algorithm is realized by using Gaussian variation operator with varied scales. The large scale Gaussian variation operator with concussion characteristics can complete fast global search for decision space, so that particles can quickly locate the surrounding area of the optimal solution, which enhances the ability to escape the local optimal solution of the algorithm and avoids the occurrence of precocious convergence. The small scale variation operator gradually reduces the area near the optimal solution. Experimental results show that the improved particle swarm optimization algorithm can effectively improve the precision of the optimal solution and ensure the convergence of the algorithm.
Generating Responses Expressing Emotion in an Open-Domain Dialogue System

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.
User Queries for Semantic Search Engine

The amount of linked open data stored on the internet is increasing hugely, yet it is difficult for non-expert users to access the semantic data without having knowledge of SPARQL language.Whilst the modern semantic search engines tackled the problem with standard user interfaces, these interfaces are not very usable for the users with no or little knowledge of semantic web technologies.One way to achieve a user friendly interface is the use of an effective tool to convert a natural language query into a standard SPARQL query.This paper introduces an efficient way of information retrieval from online repositories and a system to match user entered keywords with related ontologies to format a query.This paper proposes a solution based on a previously modelled framework and introduces algorithms for query tagging and keyword mapping in order to formulate SPARQL queries.Finally, the paper describes examples using prototype user interface.
Minimizing Geo-Distributed Interactive Service Cost With Multiple Cloud Service Providers

Geo-distributed interactive service is a type of Internet cloud service. Since data centers belonging to multiple cloud service providers (CSPs) can provide lower resource provision cost for these services, we try to overcome the two challenges in selecting the data centers from multi-CSPs for our cost optimization. First, as the resource prices vary across data centers, scheduling data centers and geo-distributed interactive services have a tremendous consequence for optimizing the cost. Second, the resource demand is uncertain, and the geo-distributed interactive service needs enough resources to meet the quality-of-service constraint. A regularized stochastic decomposition algorithm, namely, two-dimension resource provision (2DRP), is proposed for solving the two-stage stochastic linear programming problems. In stage one, users express tail latency constraints for each geo-distributed interactive service, letting 2DRP choose the lowest price-per-instance data center group and determine the right amount of resources. In stage two, 2DRP estimates the impact of the request rate and the amount of resources on tail latency and uses the decomposition technique to adjust the resource provision plan to handle the demand uncertainty. We evaluate 2DRP over a set of workload scenarios on real clouds by reproducing the Internet request with data center workload traces, and the results show the availability of our algorithm for cost minimization geo-distributed interactive services with multi-CSPs.
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning

In the framework of MDP, although the general reward function takes three arguments—current state, action, and successor state; it is often simplified to a function of two arguments—current state and action. The former is called a transition-based reward function, whereas the latter is called a state-based reward function. When the objective involves the expected total reward only, this simplification works perfectly. However, when the objective is risk-sensitive, this simplification leads to an incorrect value. We propose three successively more general state-augmentation transformations (SATs), which preserve the reward sequences as well as the reward distributions and the optimal policy in risk-sensitive reinforcement learning. In risk-sensitive scenarios, firstly we prove that, for every MDP with a stochastic transition-based reward function, there exists an MDP with a deterministic state-based reward function, such that for any given (randomized) policy for the first MDP, there exists a corresponding policy for the second MDP, such that both Markov reward processes share the same reward sequence. Secondly we illustrate that two situations require the proposed SATs in an inventory control problem. One could be using Q-learning (or other learning methods) on MDPs with transition-based reward functions, and the other could be using methods, which are for the Markov processes with a deterministic state-based reward functions, on the Markov processes with general reward functions. We show the advantage of the SATs by considering Value-at-Risk as an example, which is a risk measure on the reward distribution instead of the measures (such as mean and variance) of the distribution. We illustrate the error in the reward distribution estimation from the reward simplification, and show how the SATs enable a variance formula to work on Markov processes with general reward functions.
CBOR/JSON binding of IODEF

RFC7970 specified an information model and a corresponding XML datamodel for exchanging incident and indicator information. This draftprovides an alternative data model implementation in CBOR/JSON.
Robust Stereo Visual Odometry Based on Probabilistic Decoupling Ego-Motion Estimation and 3D SSC

The outliers caused by noise and mismatching severely restrict the precision of visual odometry. Moreover, the dynamic environment is also a crucial element that decreases the robustness of the systems. This paper presents a robust stereo visual odometry by decoupled ego-motion estimation based on probabilistic matches and rejecting the outliers of dynamic objects through motion segmentation. Fast ZNCC method, based on local sum table and partition upper bound schemes, is presented for selecting probabilistic matches while keeping run-time efficiency. The selection of multi-correspondences can avoid mismatching of corresponding points. In consideration of noise interference, the essential matrix is computed in a probabilistic framework to estimate the initial value of the rotation matrix without estimated depth errors involved. Then, in order to estimate pose robustly in dynamic environment, a modified sparse subspace clustering (SSC) method is discussed, which aims to cluster the tracked 3D points cloud to avoid errors caused by affine transformation. The non-negative constraint makes the method suitable for fast moving camera. The proposed 3D-SSC method removes the outliers belonging to dynamic objects effectively. Finally, the detected inliers and depths are employed to estimate the translation matrix and refine rotation matrix. The proposed method is evaluated on the KITTI benchmark and compared with the state-of-the-art methods. The results show that our method is more robust as it can detect outliers more accurately in dynamic environments and achieve higher precision in motion estimation.
Learning Set Functions with Limited Complementarity

We study PMAC-learning of real-valued set functions with limited complementarity. We prove, to our knowledge, the first nontrivial learnability result for set functions exhibiting complementarity, generalizing Balcan and Harvey’s result for submodular functions. We prove a nearly matching information theoretical lower bound on the number of samples required, complementing our learnability result. We conduct numerical simulations to show that our algorithm is likely to perform well in practice.
On Resolving Ambiguous Anaphoric Expressions in Imperative Discourse

Anaphora resolution is a central problem in natural language understanding. We study a subclass of this problem involving object pronouns when they are used in simple imperative sentences (e.g., “pick it up.”). Specifically, we address cases where situational and contextual information is required to interpret these pronouns. Current state-of-the art statisticallydriven coreference systems and knowledge-based reasoning systems are insufficient to address these cases. In this paper, we introduce, with examples, a general class of situated anaphora resolution problems, propose a proof-of-concept system for disambiguating situated pronouns, and discuss some general types of reasoning that might be needed.