Reviewerly/exHarmony huggingface dataset:
FAIR: Fraud Aware Impression Regulation System in Large-Scale Real-Time E-Commerce Search Platform

Fraud sellers in e-commerce usually promote their products via fake transactions. Such behaviors damage the reputation of the e-commerce platform and jeopardize the business environment in the platform. The search engine of existing e-commerce platforms mainly focuses on generating transactions by matching users' queries and sellers' products. The most common method to defense fraud sellers is to set up a blacklist based on fraud detection and manual investigation, and then punish those sellers in the list, which is inefficient and can only cover a small fraction of potential fraud sellers. In this paper, we propose the first fraud aware impression regulation system (FAIR) which is data-driven and can work in large-scale e-commerce platforms. Its main function is to actively regulate the impressions received by all potential fraud sellers in a real-time fashion. It utilizes the reinforcement learning architecture to dynamically adjust the impression regulation strategy under different reward settings, which can not only promote the impression regulation effects, but also improve the revenue of the platform simultaneously. We deploy FAIR on the Taobao platform of Alibaba, one of the world's largest e-commerce search platform, and perform an A/B test for two weeks. The results show that FAIR can effectively reduce the fraud impressions and improve the overall platform revenue at the same time.
Deep Residual Learning Using Data Augmentation for Median Filtering Forensics of Digital Images

This paper addresses the median filtering forensics for a lossy compressed image with low resolution, which is essential for the identification of fake images and fake videos. A deep residual model with training data augmentation is employed in the proposed method. To solve the dilemma that the low-resolution image is the lack of enough statistical pixels for extracting reliable features, we propose a filter layer to widen the inputs for the convolutional neural network (CNN). First, we perform the high-pass filtering to an image in the filtered layer and stack the multiple filtered residuals into 16-channel feature maps as inputs of CNN. Then, a deep residual CNN model has proposed to self-learn the median filtering traces that are hidden in the JPEG lossy compressed image. To alleviate the over-fitting issue of the deeper CNN model, we employ a data augmentation scheme in the training to increase the diversity of training data and, thus, obtain a more stable median filtering detector. The experimental results demonstrate that the proposed net with training data augmentation outperforms state of the arts in both baseline test and generalization ability test, achieving at least 2% higher in terms of detection accuracy.
Local Low-Rank and Sparse Representation for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a fundamental task in a plethora of HSI applications. Global low-rank property is widely adopted to exploit the spectral-spatial information of HSIs, providing satisfactory denoising results. In this paper, instead of adopting the global low-rank property, we propose to adopt a local low rankness for HSI denoising. We develop an HSI denoising method via local low-rank and sparse representation, under an alternative minimization framework. In addition, the weighted nuclear norm is used to enhance the sparsity on singular values. The experiments on widely used hyperspectral datasets demonstrate that the proposed method outperforms several state-of-the-art methods visually and quantitatively.
DM_NLP at SemEval-2018 Task 12: A Pipeline System for Toponym Resolution

This paper describes DM-NLP's system for toponym resolution task at Semeval 2019.Our system was developed for toponym detection, disambiguation and end-to-end resolution which is a pipeline of the former two.For toponym detection, we utilized the stateof-the-art sequence labeling model, namely, BiLSTM-CRF model as backbone.A lot of strategies are adopted for further improvement, such as pre-training, model ensemble, model averaging and data augment.For toponym disambiguation, we adopted the widely used searching and ranking framework.For ranking, we proposed several effective features for measuring the consistency between the detected toponym and toponyms in GeoNames.Eventually, our system achieved the best performance among all the submitted results in each sub task.
LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness

This paper describes LTL-UDE's systems for the SemEval 2019 Shared Task 6.We present results for Subtask A and C. In Subtask A, we experiment with an embedding representation of postings and use a Multi-Layer Perceptron and BERT to categorize postings.Our best result reaches the 10th place (out of 103) using BERT.In Subtask C, we applied a two-vote classification approach with minority fallback, which is placed on the 19th rank (out of 65).
Pengaruh Era Digital Terhadap Perkembangan Bahasa Anak

Language development is an indicator of development. 80% of developmental disorders are caused by lack of stimulation. Use of interactive media (digital) is one of the stimulations in this digital era. Interactive media is an audio-visual media that is expected to improve children's language skills. This media can increase vocabulary, phonation and the child's ability to understand colors, numbers and discourse / text.
Dynamic assessment method of air target threat based on improved GIFSS

As the air combat environment becomes more complicated and changeable, accurate threat assessment of air target has a significant impact on air defense operations. This paper proposes an improved generalized intuitionistic fuzzy soft set (GIFSS) method for dynamic assessment of air target threat. Firstly, the threat assessment index is reasonably determined by analyzing the typical characteristics of air targets. Secondly, after the GIFSS at different time is obtained, the index weight is determined by the intuitionistic fuzzy set entropy and the relative entropy theory. Then, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted GIFSS is obtained. Finally, threat assessment of five air targets is carried out by using the improved GIFSS (I-GIFSS) and comparison methods. The validity and superiority of the proposed method are verified by calculation and comparison.
Implementasi literasi budaya dan kewargaan sebagai solusi disinformasi pada generasi millennial di Indonesia

The emergence of disinformation phenomena that occur currently in the millennial generation is due to the easy-going attitude that believes in the information obtained without looking at the truth or examining the credibility of the information source. Eventually, the millennial generation does not have the analytical thinking process of using information. The purpose of this study was to get a picture of disinformation that occurred in the millennial generation and explained the application of cultural literacy and citizenship as a solution to overcome disinformation. The research used the literature study method with a qualitative approach. Data collection used primary and secondary data obtained through various kinds of literature in the last ten years (2009-2019) such as books, e-books, e-journals, and proceedings. Based on the results of the analysis of the problems studied and supported by the theory used, the results showed that cultural and citizenship education could be applied in millennial generation literacy styles. This generation could be more selective in receiving and processing information obtained, could prevent the emergence of hoaxes and hate speech circulating in the community and instill cultural values and citizenship in the lifestyle in the millennial era. The steps taken are activity programs that contain useful information processing process that shapes an individual to become more selective in processing information obtained as well as implementing cultural literacy and citizenship independently in schools, family, and environment.
An End-to-End Conditional Random Fields and Skip-Connected Generative Adversarial Segmentation Network for Remote Sensing Images

Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively.
Agile architecture in the digital era: Trends and practices

The speed of response to change and fluidity are key preconditions for the next generation of IT solutions in the digital world.We are witnessing a rather unimaginable expansion of the use of technology in everyday life, on the one hand, and a continuous increase in the speed of software delivery, on the other, which significantly increased expectations and contributed to the adoption of agile methods and practices, shifting the pendulum of software architecture from traditional to agile methods and practices.Agile architecture, as a result of the transformation of a traditional and agile approach to software development, is a new approach that uses agile techniques to deliver a flexible architecture, adaptable to changing demands, tolerant to changes, which is the result of the iterative-incremental design of the agile process of software development.In recent years, there has been a shift in focus, in practice and research, from people and processes to integration technologies and application's hosting, which has led to the emergence of microservices and increased interest in software architecture and design.One consequence of this is the emergence and development of new approaches in the process of building Agile architecture, such as Continuous Architecting, Lean Architecting or Evolutionary Architecting, which essentially share the same goals.In this connection, in order to understand better the concept and the new role of Agile architecture in the digital era, it is necessary to study the genesis of Agile architecture, as a special approach in software development, to identify current trends and practices that are adapted to the contemporary digital environment (scalability, distribution, complexity).The results of conducted systematic literature review will help researchers and practitioners in better understanding of what Agile architecture is and its role, the current trends and directions of future development, and practices that are particularly useful in the development of complex software, with the aim of broadening the application and improvement of the agile software development process.
Grand Adventure of Augmented Reality in Landscape of Surgery

Computer as an integral part of continual advancements in medicine has experienced tremendous development to minimize the risks and improving the precision of the surgery. Our review included multi-disciplinary publications in English from 2014 to 2017 using Springer, Oxford library, Elsevier, PubMed, Google Scholar, and Springer search engines using terms of "augmented reality (AR), "plastic surgery," and "surgery " and "Augmented Reality Ethics and challenges". It was shown that AR has been successfully effective in different branches of surgery, but with concerns and challenges like acceptance, privacy, different physical, security and behavioral threats. To come over them partially, a methodological approach for cyber threat landscape proactive exploration has been suggested.
A Web Platform for Integrated Vulnerability Assessment and Cyber Risk Management

Cyber risk management is a very important problem for every company connected to the internet. Usually, risk management is done considering only Risk Analysis without connecting it with Vulnerability Assessment, using external and expensive tools. In this paper we present CYber Risk Vulnerability Management (CYRVM)—a custom-made software platform devised to simplify and improve automation and continuity in cyber security assessment. CYRVM’s main novelties are the combination, in a single and easy-to-use Web-based software platform, of an online Vulnerability Assessment tool within a Risk Analysis framework following the NIST 800-30 Risk Management guidelines and the integration of predictive solutions able to suggest to the user the risk rating and classification.
Point-Based Value Iteration for Finite-Horizon POMDPs

Partially Observable Markov Decision Processes (POMDPs) are a popular formalism for sequential decision making in partially observable environments. Since solving POMDPs to optimality is a difficult task, point-based value iteration methods are widely used. These methods compute an approximate POMDP solution, and in some cases they even provide guarantees on the solution quality, but these algorithms have been designed for problems with an infinite planning horizon. In this paper we discuss why state-of-the-art point-based algorithms cannot be easily applied to finite-horizon problems that do not include discounting. Subsequently, we present a general point-based value iteration algorithm for finite-horizon problems which provides solutions with guarantees on solution quality. Furthermore, we introduce two heuristics to reduce the number of belief points considered during execution, which lowers the computational requirements. In experiments we demonstrate that the algorithm is an effective method for solving finite-horizon POMDPs.
Exemplar-based image inpainting using angle-aware patch matching

Image inpainting has been presented to complete missing content according to the content of the known region. This paper proposes a novel and efficient algorithm for image inpainting based on a surface fitting as the prior knowledge and an angle-aware patch matching. Meanwhile, we introduce a Jaccard similarity coefficient to advance the matching precision between patches. And to decrease the workload, we select the sizes of target patches and source patches dynamically. Instead of just selecting one source patch, we search for multiple source patches globally by the angle-aware rotation strategy to maintain the consistency of the structures and textures. We apply the proposed method to restore multiple missing blocks and large holes as well as object removal tasks. Experimental results demonstrate that the proposed method outperforms many current state-of-the-art methods in patch matching and structure completion.
Pakistani Learners’ Perceptions Regarding Mobile Assisted Language Learning in ESL Classroom

Technological advancement with its extensive use in every field of life has also impelled the educators to apply innovative techniques inside the classrooms. Mobile assisted language learning (here on wards MALL) is a latest technique which is gaining popularity. The current paper intends to explore the perceptions of Pakistani ESL learners on integrating MALL in English language classroom. The study used quantitative paradigm as research design. The population of the study comprised of Intermediate students, studying in public-sector colleges of Lahore. From the said population 60 students from 6 public sector colleges in Lahore were selected through simple random sampling. The data were congregated through a close-ended questionnaire. The collected data were later analyzed with the help of SPSS. The results illustrated that the Pakistani students have shown positive inclination towards MALL usage inside the ESL classrooms. The study also highlights another feature of MALL that it not only supports learning with ease and comfort but also motivates learners to learn in a collaborative ambiance. If MALL can be implemented intelligently in Pakistani classrooms it can be an influential tool for language learning.
Efficient Concept Induction for Description Logics

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.
An Online Calibration Method for Microsoft HoloLens

An optical see-through (OST) head-mounted display (HMD) is an appropriate option for an image-guided surgical navigation system (IGS) based on augmented reality (AR). However, the calibration of OST-HMDs has been challenging, since the augmented results can only be observed by the users wearing it. In this paper, we proposed an online calibration method based on the single-point active alignment method (SPAAM) for a HoloLens using a virtual tracking system. The transformation matrices between the world coordinates and the virtual holographic coordinates are calculated using a linear model and decomposed by the singular value decomposition (SVD) algorithm. The surgical workspace is analyzed, and there are nine sampling points for the offline calibration: eight at the vertices and one at the center of a cube of the workspace. 20 groups of data at each sampling point are collected. Five alignments should be implanted in the online calibration. The transformation matrices are compensated for the offset of every use of a HoloLens. To assess the accuracy of the calibration method, in accordance with the RANSAC algorithm, eight groups of additional data at random non-sampling locations are collected. Three different users that are familiar with the calibration procedures performed the calibration. The average distance error of our calibration was below 6 mm, and the rotation error was up to 5°. It is a user-friendly, simple method for the online calibration of OST-HMDs.
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization

We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience score, which is modeled using an attention-based deep neural network, to represent the salience degree of the summary for yielding the document. We devise a contrastive training strategy to learn the salience estimation network, and then use the learned salience score as a guide and iteratively extract the most salient sentences from the document as our generated summary. In experiments, our model not only achieves state-of-the-art ROUGE scores on CNN/Daily Mail dataset, but also shows strong robustness in the out-of-domain test on DUC2007 test set. Moreover, our model reaches a ROUGE-1 F-1 score of 39.41 on CNN/Daily Mail test set with merely 1/100 training set, demonstrating a tremendous data efficiency.
Multi-Task Learning for Coherence Modeling

We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment.We propose a hierarchical neural network trained in a multitask fashion that learns to predict a documentlevel coherence score (at the network's top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks.We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.
SADIH: Semantic-Aware DIscrete Hashing

Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in largescale multimedia retrieval applications. Particularly, supervised hashing has recently received considerable research attention by leveraging the label information to preserve the pairwise similarities of data points in the Hamming space. However, there still remain two crucial bottlenecks: 1) the learning process of the full pairwise similarity preservation is computationally unaffordable and unscalable to deal with big data; 2) the available category information of data are not well-explored to learn discriminative hash functions. To overcome these challenges, we propose a unified Semantic-Aware DIscrete Hashing (SADIH) framework, which aims to directly embed the transformed semantic information into the asymmetric similarity approximation and discriminative hashing function learning. Specifically, a semantic-aware latent embedding is introduced to asymmetrically preserve the full pairwise similarities while skillfully handle the cumbersome n×n pairwise similarity matrix. Meanwhile, a semantic-aware autoencoder is developed to jointly preserve the data structures in the discriminative latent semantic space and perform data reconstruction. Moreover, an efficient alternating optimization algorithm is proposed to solve the resulting discrete optimization problem. Extensive experimental results on multiple large-scale datasets demonstrate that our SADIH can clearly outperform the state-of-the-art baselines with the additional benefit of lower computational costs.
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search

In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning. In ACE, we use actor ensemble (i.e., multiple actors) to search the global maxima of the critic. Besides the ensemble perspective, we also formulate ACE in the option framework by extending the option-critic architecture with deterministic intra-option policies, revealing a relationship between ensemble and options. Furthermore, we perform a look-ahead tree search with those actors and a learned value prediction model, resulting in a refined value estimation. We demonstrate a significant performance boost of ACE over DDPG and its variants in challenging physical robot simulators.