Reviewerly/exHarmony huggingface dataset:
Flickr Image Community Analytics by Deep Noise-Refined Matrix Factorization

Accurately categorizing Flickr images into multiple pre-defined communities (e.g., “architecture” and “peaceful”) is an indispensable technique in multimedia analysis, graphic design, fashion recommendation, etc. In practice, these communities are constructed and updated manually, which is subjective and intolerably time consuming. To alleviate these shortcomings, a noise-refined deep matrix factorization (MF) framework is proposed to intelligently discover communities from million-scale Flickr users, wherein the semantic tag correlations and community correlations are simultaneously encoded. More specifically, it is believable that Flickr communities are high-level clues on the basis of human visual semantic perception. Thereby, a MF algorithm is employed to approximate the community label matrix by the product of pairwise factor matrices, which represent the latent representations of user-provided tags and the corresponding basis matrix respectively. Subsequently, an end-to-end deep model is formulated to hierarchically derive the latent deep representation from raw image pixels to semantic tags. To robustly handle contaminated image semantic tags and community labels, an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm constraint is encoded to enhance the MF. Meanwhile, to optimally exploit the rich context information of Flickr images, the intrinsic structure between image semantic tags and between communities are collaboratively captured. Finally, the upgraded MF and the deep model are seamlessly combined into a unified framework, which is solved by an iterative algorithm. Experiments on 2 M Flickr images have demonstrated the superiority of our approach. Besides, the discovered Flickr communities can improve photo retargeting and visual aesthetics assessment significantly.
Deep Ordinal Regression for Pledge Specificity Prediction

Many pledges are made in the course of an election campaign, forming important corpora for political analysis of campaign strategy and governmental accountability.At present, there are no publicly available annotated datasets of pledges, and most political analyses rely on manual analysis.In this paper we collate a novel dataset of manifestos from eleven Australian federal election cycles, with over 12,000 sentences annotated with specificity (e.g., rhetorical vs. detailed pledge) on a finegrained scale.We propose deep ordinal regression approaches for specificity prediction, under both supervised and semi-supervised settings, and provide empirical results demonstrating the effectiveness of the proposed techniques over several baseline approaches.We analyze the utility of pledge specificity modeling across a spectrum of policy issues in performing ideology prediction, and further provide qualitative analysis in terms of capturing party-specific issue salience across election cycles.
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts

Elissa Redmiles, Lisa Maszkiewicz, Emily Hwang, Dhruv Kuchhal, Everest Liu, Miraida Morales, Denis Peskov, Sudha Rao, Rock Stevens, Kristina Gligorić, Sean Kross, Michelle Mazurek, Hal Daumé III. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Aplikasi Peminjaman Ruangan Rapat Kantor Gubernur Provinsi Kepulauan Bangka Belitung Berbasis Android

The loan of meeting room in the office of Bangka Belitung Province governor is currently still through the manual process that is lending must meet the manager who is in the office of the governor to obtain approval on lending The required room. Lending room that is still manual will be changed to Android based. The built-in system that will be built using the Java programming language with the helper platform is Android Studio, PHP, and MySQL database. The system's analytical techniques used in this research are object-oriented and use a Unified Modelling Language (UML) modeling tool to illustrate built-in applications. This research result shows that this room loan application can be used by the user well and can borrow room with this application. With this application is expected to help users to more easily borrow the room.
Boundary states for entanglement robustness under dephasing and bit flip channels*

We investigate the robustness of entanglement for a multiqubit system under dephasing and bit flip channels. We exhibit the difference between the entanglement evolution of the two forms of special states, which are locally unitarily equivalent to each other and therefore possess precisely the same entanglement properties, and demonstrate that the difference increases with the number of qubits n . Moreover, those two forms of states are either the most robust genuine entangled states or the most fragile ones, which confirm that local unitary (LU) operations can greatly enhance the entanglement robustness of n -qubit states.
Solving Multiple Traveling Salesman Problem by Meerkat Swarm Optimization Algorithm

Multiple Traveling Salesman Problem (MTSP) is one of various real-life applications, MTSP is the extension of the Traveling Salesman Problem (TSP). TSP focuses on searching of minimum or shortest path (traveling distance) to visit all cities by salesman, while the primary goal of MTSP is to find shortest path for m paths by n salesmen with minimized total cost. Wherever, total cost means the sum of distances of all salesmen. In this work, we proposed metaheuristic algorithm is called Meerkat Swarm Optimization (MSO) algorithm for solving MTSP and guarantee good quality solution in reasonable time for real-life problems. MSO is a metaheuristic optimization algorithm that is derived from the behavior of Meerkat in finding the shortest path. The implementation is done using many dataset from TSPLIB95. The results demonstrate that MSO in most results is better than another results that compared in average cost that means the MSO superior to other results of MTSP.
An Analysis of Differential Privacy Research in Location Data

Location data is becoming ubiquitous with the spread of smart devices, and social media geo-tagged feeds. However, sharing location data may lead to serious privacy risks that must not be overlooked. Differential privacy is the standard technique that provides strong privacy guarantees regardless of the adversary's side information. Usually, this is achieved by adding noise to the true result of the statistical query extracted from the data. However, a straight forward application of differential privacy to location data is not always possible. The growing interest in designing solutions to achieve differential privacy that take into account the characteristics of location data is evident from the substantial number of works done in this field. This paper briefly reviews research works done in differential privacy targeted toward location data from the data flow perspective, including the collection, aggregation, and mining. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing frameworks, as well as novel approaches, that tackle the characteristics of location data. We identify multiple challenges to the application of differential privacy to location data, such as the calibration of the added noise to assure utility, finding the optimal spatial division to release the location aggregate per region while balancing privacy and utility. We also discuss the future directions concluded from the analysis.
Organizational Control Policy, Information Security Deviance, and Moderating Effect of Power Distance Orientation

Researchers generally believe that organizational control can deter employees' information security deviant behaviors. However, these relationships are not always observed. Based on the cognitive appraisal theory, this study extends the content domain of information security research by examining the moderating effect of power distance orientation, a kind of cultural value, on these relationships. In the results, first, the severity of penalty and the certainty of detection decreases employees' computer abuse. Second, employee power distance orientation moderates the relationships of the severity of penalty with employee computer abuse, such that the negative relationships are stronger for employees with higher power distance orientation. The findings suggest the deterrent function of cultural values employees hold in organizational behavior.
The AFRL WMT19 Systems: Old Favorites and New Tricks

This paper describes the Air Force Research Laboratory (AFRL) machine translation systems and the improvements that were developed during the WMT19 evaluation campaign.This year, we refine our approach to training popular neural machine translation toolkits, experiment with a new domain adaptation technique and again measure improvements in performance on the Russian-English language pair.
CRITICAL DATA: TEACHING DESIGN THROUGH CRITICAL DESIGN, PHYSICAL COMPUTING, AND DIGITAL DATA

Industrial design students in Mexico carry educational backwardness that does not allow them to learn new skills related to problem-solving, critical thinking and technology.Especially if industrial design has had to acknowledge that products will have to respond to the demands brought by the Internet of Things.Mexico is a struggling country in terms of design and innovation as it has been in the bottom of most indicators used by the OECD to measure science, technology, and innovation in the last ten years, obtaining the 56 th position on the Global Innovation Index.We foresee that Mexican companies would need to recruit talent that could implement innovation and technology on a faster pace with a deep understanding of the limitations of its context and a problem-solving attitude toward new challenges.We describe how, by using critical design and challenge-based learning, we could incentivise students to develop skills related to critical thinking, problem solving and technology.A case study is described with its methodologies and tools on implementing critical design on challenge-based-learning methods.Through a survey, we learned that after each exercise, most of the students perceived that they felt more confident using and understanding technology, as well as having a better understanding of their context, learning new methodologies and felt more prepared to face their professional life.By sharing our methodologies and results through this article, we hope to encourage other scholars, design educators and designers to use critical design as a tool for education in an ever-changing context.
Multi-Frame Super Resolution with Deep Residual Learning on Flow Registered Non-Integer Pixel Images

Super-Resolution (SR) of low-quality images is an important topic of research in image processing and computer vision field. Using multi-frame, super-resolution algorithm can reconstruct high-resolution images by incorporating the information of the subsequent images. Most of the super-resolution techniques for multi-frames either use a more traditional or mathematical approach or deep learning based approach with optical flow in consideration. In this paper, we develop a way to combine the optical flow enabled sub-pixel registration method for mapping into the high-resolution grid and a deep residual learning approach for restoring features with noise removal. The results exhibit a significant gain over the state of art methods and the bi-cubic interpolation method.
A Development of Decision Support Systems Selection of Employee Acceptance Using Weighted Product Method

Employee selection is an activity to get human resources for a company. Researchers conducted research at PT. Gajah Tunggal Tbk where currently the selection process still uses the conventional method, the current assessment of participants uses technical and non-technical tests with interview methods but at present the company does not yet have a standardized weighting of values tested, so companies sometimes have difficulty determining who should receive from the test participants. Based on the above conditions, researchers conducted decision support system design research using the Weighted Product method to assist companies in getting the right employees according to the employees' technical needs and abilities. Decision support system development refers to the results of data collection using qualitative and quantitative data collection methods. Development of decision support system is built based on the waterfall system development model using the PHP programming language. The system testing method uses the black box method. The design of decision support system from the recruitment selection generator from this study is expected to be a reference for the selection process of employees in similar industries with the place of research conducted by researchers.
APE through Neural and Statistical MT with Augmented Data. ADAPT/DCU Submission to the WMT 2019 APE Shared Task

Automatic post-editing (APE) can be reduced to a machine translation (MT) task, where the source is the output of a specific MT system and the target is its post-edited variant.However, this approach does not consider context information that can be found in the original source of the MT system.Thus a better approach is to employ multi-source MT, where two input sequences are considered -the original source and the MT output.Extra context information can be introduced in the form of extra tokens that identify certain global properties of a group of segments, added as a prefix or a suffix to each segment.Successfully applied in domain adaptation of MT as well as on APE, this technique deserves further attention.In this work we investigate multi-source neural APE (or NPE) systems with training data which has been augmented with two types of extra context tokens.We experiment with authentic and synthetic data provided by WMT 2019 and submit our results to the APE shared task.We also experiment with using statistical machine translation (SMT) methods for APE.While our systems score bellow the baseline, we consider this work a step towards understanding the added value of extra context in the case of APE.
Toward Automated Content Feedback Generation for Non-native Spontaneous Speech

In this study, we developed an automated algorithm to provide feedback about the specific content of non-native English speakers’ spoken responses. The responses were spontaneous speech, elicited using integrated tasks where the language learners listened to and/or read passages and integrated the core content in their spoken responses. Our models detected the absence of key points considered to be important in a spoken response to a particular test question, based on two different models: (a) a model using word-embedding based content features and (b) a state-of-the art short response scoring engine using traditional n-gram based features. Both models achieved a substantially improved performance over the majority baseline, and the combination of the two models achieved a significant further improvement. In particular, the models were robust to automated speech recognition (ASR) errors, and performance based on the ASR word hypotheses was comparable to that based on manual transcriptions. The accuracy and F-score of the best model for the questions included in the train set were 0.80 and 0.68, respectively. Finally, we discussed possible approaches to generating targeted feedback about the content of a language learner’s response, based on automatically detected missing key points.
The RWTH Aachen University Machine Translation Systems for WMT 2019

This paper describes the neural machine translation systems developed at the RWTH Aachen University for the De→En, Zh→En and Kk→En news translation tasks of the Fourth Conference on Machine Translation (WMT19).For all tasks, the final submitted system is based on the Transformer architecture.We focus on improving data filtering and fine-tuning as well as systematically evaluating interesting approaches like unigram language model segmentation and transfer learning.For the De→En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39.6% BLEU on newstest2019.In the Zh→En task, we show 1.3% BLEU improvement over our last year's submission, which we mostly attribute to the splitting of long sentences during translation.We further report results on the Kk→En task where we gain improvements of 11.1% BLEU over our baseline system.On the same task we present a recent transfer learning approach, which uses half of the free parameters of our submission system and performs on par with it.
Multilayer Fusion and Chunk-Based Transmittance Estimation for Natural Hazy Image Enhancement

Aiming at the uneven distribution of haze concentration and color imbalance in haze weather images, a natural hazy image enhancement method which combines with multilayer fusion and chunk-based is proposed. Based on the atmospheric physical model, the detail and base layers of scene images can be extracted using multilayer decomposition and nonlinear mapping function. Iterative Box Filter can improve the accuracy of ambient light selection and avoid the imbalance of ambient light estimation. The image is segmented into blocks, and the block images are processed by the same operations which are multilayer decomposition and nonlinear mapping function to obtain the detail maps of block images. By splicing these detail maps into the whole detail maps and combining with the dark channel priori, a good transmittance estimation map can be obtained. Throughout the experiment, the guide image filter is utilized to preserve the edges of the image and color correction is added to the model. The experiment results demonstrate that our proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons.
Cross-Cultural Transfer Learning for Text Classification

Large training datasets are required to achieve competitive performance in most natural language tasks.The acquisition process for these datasets is labor intensive, expensive, and time consuming.This process is also prone to human errors.In this work, we show that crosscultural differences can be harnessed for natural language text classification.We present a transfer-learning framework that leverages widely-available unaligned bilingual corpora for classification tasks, using no task-specific data.Our empirical evaluation on two tasks -formality classification and sarcasm detection -shows that the cross-cultural difference between German and American English, as manifested in product review text, can be applied to achieve good performance for formality classification, while the difference between Japanese and American English can be applied to achieve good performance for sarcasm detection -both without any task-specific labeled data.
Grammatical-Error-Aware Incorrect Example Retrieval System for Learners of Japanese as a Second Language

Existing example retrieval systems do not include grammatically incorrect examples, or only present a few examples, if any.Even if a retrieval system has a wide coverage of incorrect examples along with the correct counterparts, learners need to know whether their query includes errors.Considering the usability of retrieving incorrect examples, our proposed method uses a large-scale corpus and presents correct expressions along with incorrect expressions using a grammatical error detection system so that the learner does not need to be aware of how to search for examples.Intrinsic and extrinsic evaluations indicate that our method improves the accuracy of example sentence retrieval and the quality of a learner's writing.
MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in this paper, we propose a novel diverse density (DD) and multiple part similarity combination method for multiple instance learning, named MILDMS. First, we model the target concepts optimization with a DD function constraint on positive and negative instance space, which can greatly improve the robustness to label noise problem. Next, we combine the positive and negative instances in the bag (generated by hand-crafted and convolutional neural network features) with multiple part similarities to construct an MIL kernel. We evaluate the proposed approach on the MUSK dataset, whose results MUSK1 (91.9%) and MUSK2 (92.2%) show our method is comparable to other MIL algorithms. To further demonstrate generality, we also present experimental results on the PASCAL VOC 2007 and 2012 (46.5% and 42.2%) and COREL (78.6%) that significantly outperforms the state-of-the-art algorithms including deep MIL and other non-deep MIL algorithms.
Mathematical model of “dialectical logic”, and relatively autonomous cognitive robots

The object of investigation is the notorious problem of "dialectical-logic", and its relevance to constructing relatively autonomous cognitive robots.The subjectmatter is formal axiological aspect of the mentioned object.The method is constructing and studying discrete mathematical models.Scientific novelty: for the first time such a
Pemberdayaan Masyarakat Berbasis Gerakan Literasi di Taman Baca Masyarakat Pondok Sinau Lentera Anak Nusantara

Empowerment derived from the word ‘power’. Therefore, the main idea of empowerment in contact with the concept of power. Meanwhile, the authority itself is often attributed with the ability to make other people do what we want, regardless of their wishes and interests. Empowerment refers to the ability of the people, especially the weak and vulnerable groups so that they have the ability in some respects. This in turn makes the praxis of empowerment of communities on the territory of the literacy movement. The inclusion of community empowerment on the literacy movement to make the movement experienced a transformation from the previous only general movement based on issues that are more specific, one that is in the Pondok Sinau Lentera Anak Nusantara as setting research. The purpose of this research is to know the realization community empowerment-based literacy movement and the impact of socio-economic change in the surrounding communities. By using the theory of community empowerment and literacy theory and qualitative descriptive method, it can be noted that there is a relationship between the empowerment of community-based literacy movement toward socio-economic changes in the surrounding communities. The background and the focus of community empowerment-based literacy movement in Pondok Sinau Lentera Anak Nusantara is as follows: (1) the existence of empowerment-based literacy movement in Pondok Sinau Lentera Anak Nusantara the answer to the problems of social-historical territory of the village; (2) the realization of the empowerment funded based on the principle of volunteerism and independence; (3) the main focus of the activities of empowerment is the transformation of the values of the characters building for the productive age group as well as encourage socio-economic independence based on a series of soft skill thematic activities.