Reviewerly/exHarmony huggingface dataset:
Visualization of a two-dimensional tree modeling using fractal based on L-system

Abstract The purpose of this paper is to present a visualization of two-dimensional trees using fractals and L-Systems. Grammar in L-System consisting of letters, axioms, and production rules can be used to construct trees in various forms according to the production rules used. With the formation of grammar from the tree pattern in a fractional manner, the grammar can be reused to construct other tree patterns at a higher level. The iterations made on certain production rules visually affect the tree model. Experiments were carried out on the L-System generator to see the results of tree visualization by testing various grammars. Testing was done by trial and error in production rules to get a realistic visualization of a two-dimensional tree model. From the experimental results produced a variety of grammar, where modifications to the production rules produced different tree models. Visually, from the iteration results, the higher the iteration value, the two-dimensional tree was not formed properly. When the iteration value or n &gt; 5 the two-dimensional tree model results from an iteration it lost detail from the branch.
Investigating the Use of Instagram as a MALL Tool: The Case of Al-Imam University EFL Students

This study aimed to investigate the benefits and barriers of using Instagram in English language learning.The sample of this study consisted of 151 randomly-selected female students enrolled in the department of English at the College of Languages and Translation at Al-Imam Muhammad Ibn Saud Islamic University in the academic year 1436/1437-2015/2016 in the first semester.The researcher used both a five-point Likert scale questionnaire as a quantitative instrument and semi-structured interviews with students as a qualitative instrument to collect the data of the study.Those instruments were validated by ten university professors.Similarly, the reliability of the questionnaire was achieved by calculating Cronbach Alpha formula.The findings of the study revealed that the most important benefits of using Instagram in learning English were as follows: improving the reading, writing, and listening skills, providing enjoyable learning experiences, enriching vocabulary, improving oral communication skills, and enjoying a sense of freedom of time and place.Furthermore, the results of the study unveiled the following challenges of using Instagram in English learning: distracting attention, lack of direction from the syllabuses, lack of encouragement from instructors, embarrassment, lack of immediate feedback, the cost of Internet access, and the difficulty of writing long passages.Finally, the findings of the study presented the following solutions to Instagram barriers in English language learning: creating learning accounts and posting important things in the lecture, following only specialized accounts for learning English.
A supervised learning to index model for approximate nearest neighbor image retrieval

Image indexing plays a significant role in approximate K-nearest neighbor image retrieval. In the past decades, the most commonly used methods in image indexing are unsupervised-based. We recognize that unsupervised-based methods cannot benefit from supervised information. In this paper, supervised learning to index method is proposed to realize approximate K-nearest neighbor image retrieval. For designing the supervised learning to index algorithm, we first need to establish the supervision information of an image that should be quantified to the codeword in the codebook. Thus, we propose an image relabeling algorithm to relabel the database images so that the supervised codebook learning can be realized. Second, compared with only distance-based unsupervised image indexing method, a better idea of supervised learning to index is incorporating the distance-based similarities with the classification probabilities of samples at the image indexing stage. In order to achieve this goal, a classifier is also trained based on the relabeled images, and we finally build a constraint optimization objective function to learn the codebook and the classifier simultaneously. The proposed method is verified on the MNIST, the CIFAR-10, and the ImageNet data sets. Experimental results demonstrate the effectiveness of the proposed method.
CLOUDGEN: Workload Generation for the Evaluation of Cloud Computing Systems

In this paper, we propose CLOUDGEN workflow that produces synthetic workloads for Infrastructure and Platform as a Service for the evaluation of resource management approaches in cloud computing systems. To this end, CLOUDGEN systematically processes and clusters records in a given workload trace and fits distributions for different workload parameters within the clusters. Different than the previous work, clustering is carried out to produce different virtual machine types for achieving models that are suitable for producing Infrastructure and Platform as a Service workload models. Finally, we demonstrate CLOUDGEN by modeling recent Azure traces with enough detail to enable researchers to use these models and generating synthetic traces that are statistically similar to the Azure traces.
Revisiting Client Puzzles for State Exhaustion Attacks Resilience

In this paper, we address the challenges facing the adoption of client puzzles as a means to protect the TCP connection establishment channel from state exhaustion DDoS attacks. We model the problem of selecting the puzzle difficulties as a Stackelberg game with the server as the leader and the clients as the followers and obtain the equilibrium solution for the puzzle difficulty. We then present an implementation of client puzzles inside the TCP stack of the Linux 4.13.0 kernel. We evaluate the performance of our implementation and the obtained solution against a range of attacks through reproducible experiments on the DETER testbed. Our results show that client puzzles are effective at boosting the tolerance of the TCP handshake channel to state exhaustion DDoS attacks by rate limiting malicious attackers while allocating resources for legitimate clients.
Visual Place Recognition by DTW-based sequence alignment

Place recognition, also called visual localization, facilitates the autonomous navigation capabilities of the future of driverless cars. This paper proposes a new place recognition algorithm that considers the appearancebased methodology to localize the vehicle by utilizing visual route map, i.e. a sequence of images, or sets of features extracted from these images, that were recorded over different times and dates for the route environments. These reference sequences are accurately labeled and annotated using GPS tags or manually using odometry information. The dynamic time warping (DTW) algorithm is used to achieve image sequence alignment and find the best match for each frame from the test sequence. The proposed algorithm considered hand-crafted features like SIFT, HOG, and LDB. Experiments, using common challenging and benchmark datasets, i.e. “UQ St Lucia” and “Nordland”, have been conducted, and it has been observed that the proposed technique has significantly improved the performance of well-known appearancebased descriptors SIFT, HOG, and LDB as compared to its individual performance and to some of the state-of-the-art localization and mapping methods such as ABLE (Binary-appearance Loop-closure).
Advanced Solutions Aimed at the Monitoring of Falls and Human Activities for the Elderly Population

Ageing is a global phenomenon which is pushing the scientific community forward the development of innovative solutions in the context of Active and Assisted Living (AAL). Among functionality to be implemented, a major role is covered by falls and human activities monitoring. In this paper, main technological solutions to cope with the aforementioned needs are briefly introduced. A specific focus is given on solutions for Falls recognition and classification. A case of study is presented, where a classification methodology based on an event-driven correlation paradigm and an advanced threshold-based classifier is addressed. The receiver operating characteristic (ROC) theory is used to properly define thresholds’ values while, in order to properly assess performances of the classification methodology proposed, dedicated metrics are suggested, such as sensitivity and specificity. The solution proposed shows an average Sensitivity of 0.97 and an average Specificity of 0.99.
Generative Adversarial Networks for Text Using Word2vec Intermediaries

Generative adversarial networks (GANs) have shown considerable success, especially in the realistic generation of images.In this work, we apply similar techniques for the generation of text.We propose a novel approach to handle the discrete nature of text, during training, using word embeddings.Our method is agnostic to vocabulary size and achieves competitive results relative to methods with various discrete gradient estimators.
Dual Monolingual Cross-Entropy Delta Filtering of Noisy Parallel Data

We introduce a purely monolingual approach to filtering for parallel data from a noisy corpus in a low-resource scenario. Our work is inspired by Junczysdowmunt:2018, but we relax the requirements to allow for cases where no parallel data is available. Our primary contribution is a dual monolingual cross-entropy delta criterion modified from Cynical data selection Axelrod:2017, and is competitive (within 1.8 BLEU) with the best bilingual filtering method when used to train SMT systems. Our approach is featherweight, and runs end-to-end on a standard laptop in three hours.
EXTREMIST TEXT DETECTION IN SOCIAL WEB

Online or cyber extremism is one of the critical problem for the security of Russia and other countries as social web is widely used for radical activity and propaganda.This paper considers the problem of extremist text detection in Russian social media.We propose models and methods for identification of extremist text in Russian, which apply deep linguistic parsing and statistical processing of texts.We also present the dataset of terrorist, religious hate, racism and other radical texts in Russian and results of experiments on this dataset.It was shown, that low-dimensional psycholinguistic and semantic features of texts allow detecting extremist texts with quite good performance while lexical features allow recognizing topics of the detected extremist texts.
Detection of fraud risks in retailing sector using MLP and SVM techniques

In today's business conditions, where business activities are spreading over a wide geographical area, fraud auditing processes have crucial importance especially for the retailing sector which has a high branch network.In the retailing sector, especially purchasing processes are subject to high fraud risks.This paper shows that it is possible to detect fraudulent processes by applying data mining techniques on operational data related to purchasing activities.Within this scope, in order to detect the fraudulent purchasing operations, support vector machine (SVM) models with different kernels and artificial neural networks methods have been used and successful results have been achieved.The results of the two methods have been examined comparatively and it shows that optimized SVM classifier outperforms others.Besides, in this study, it is presumed that the detected fraud data can be proactively used in the struggle against fraud with fraud-governance risk and compliance software by converting it into scenario analysis.
Artificial Error Generation with Fluency Filtering

The quantity and quality of training data plays a crucial role in grammatical error correction (GEC).However, due to the fact that obtaining human-annotated GEC data is both timeconsuming and expensive, several studies have focused on generating artificial error sentences to boost training data for grammatical error correction, and shown significantly better performance.The present study explores how fluency filtering can affect the quality of artificial errors.By comparing artificial data filtered by different levels of fluency, we find that artificial error sentences with low fluency can greatly facilitate error correction, while high fluency errors introduce more noise.
Virtual Humanity—Access, Empathy and Objectivity in VR Film Making

This scene setting considers the meaning of immersive technologies for humanitarian encounters, particularly 360 degree video "virtual reality" film making. Drawing on scholarship on the political economy of technology, aesthetics, affect and visual securitisation, as well as a number of notable deployments of VR films as a tool for NGO and IGO project fundraising, it will consider the interchange between immersion, emotion and action within VR as humanitarian praxis, and what kind of politics this may produce. In particular, the article considers the UNICEF film "Clouds Over Sidra" and the African Parks co-sponsored film "The Protectors" which highlight not only the utility of the technology in creating immersive experiences for audiences and donors but also some of the broader politics of empathy and authorial control arising from a visual technology that purports to allow audiences a more objective "see for yourself" style experience.
Dimensionality reduction: theoretical perspective on practical measures

Dimensionality reduction plays a central role in real-world applications for Machine Learning, among many fields. In particular, metric dimensionality reduction where data from a general metric is mapped into low dimensional space, is often used as a first step before applying machine learning algorithms. In almost all these applications the quality of the embedding is measured by various average case criteria. Metric dimensionality reduction has also been studied in Math and TCS, within the extremely fruitful and influential field of metric embedding. Yet, the vast majority of theoretical research has been devoted to analyzing the worst case behavior of embeddings and therefore has little relevance to practical settings. The goal of this paper is to bridge the gap between theory and practice view-points of metric dimensionality reduction, laying the foundation for a theoretical study of more practically oriented analysis. This paper can be viewed as providing a comprehensive theoretical framework addressing a line of research initiated by VL [NeuroIPS' 18] who have set the goal of analyzing different distortion measurement criteria, with the lens of Machine Learning applicability, from both theoretical and practical perspectives. We complement their work by considering some important and vastly used average case criteria, some of which originated within the well-known Multi-Dimensional Scaling framework. While often studied in practice, no theoretical studies have thus far attempted at providing rigorous analysis of these criteria. In this paper we provide the first analysis of these, as well as the new distortion measure developed by [VL18] designed to possess Machine Learning desired properties. Moreover, we show that all measures considered can be adapted to possess similar qualities. The main consequences of our work are nearly tight bounds on the absolute values of all distortion criteria, as well as first approximation algorithms with provable guarantees.
A Distortion Model of Laser Sheet for a Laser Line Scanner With Large Fan Angle

This article proposes an analytical method of computing distorted laser sheet of a laser line scanner and compensating it. In this article, a distortion model of laser sheet generated from a laser line scanner with large fan angle is derived from physical principles to achieve accurate depth perception even around its edges on which significant depth estimation errors occur in existing algorithms. From a laser beam incident obliquely on a contact surface of two cylindrical lenses, a curved laser sheet is expressed in terms of the nonzero incident angle by the laws of geometrical optics. From the mathematical model of the distorted laser sheet, the incident angle is estimated through an optimization technique and then its estimate is used for depth computation. It is shown through simulations and experiments that the proposed distortion model enables a proper compensation scheme and hence the distortion-compensated depth estimation errors are reduced all over the range of interest, specially much around the both side edges of the laser sheet. Quantitatively in comparison to the compensation-free method, the depth estimation error is improved from 225 to 28 mm on average and from 1781 to 187 mm for worst-case scenarios near edges of a laser sheet.
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource Settings

We study the problem of incremental domain adaptation of a generic neural machine translation model with limited resources (e.g., budget and time) for human translations or model training.In this paper, we propose a novel query strategy for selecting "unlabeled" samples from a new domain based on sentence embeddings for Arabic.We accelerate the finetuning process of the generic model to the target domain.Specifically, our approach estimates the informativeness of instances from the target domain by comparing the distance of their sentence embeddings to embeddings from the generic domain.We perform machine translation experiments (Ar-to-En direction) for comparing a random sampling baseline with our new approach, similar to active learning, using two small update sets for simulating the work of human translators.For the prescribed setting we can save more than 50% of the annotation costs without loss in quality, demonstrating the effectiveness of our approach.
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets

Paraphrase generation is an interesting and challenging NLP task which has numerous practical applications.In this paper, we analyze datasets commonly used for paraphrase generation research, and show that simply parroting input sentences surpasses state-of-theart models in the literature when evaluated on standard metrics.Our findings illustrate that a model could be seemingly adept at generating paraphrases, despite only making trivial changes to the input sentence or even none at all.
Performance of DKI Jakarta Governor and Vice Governor on 2017-2018 based on Sentiment Analysis using Twitter and Instagram Data

Sentiment analysis is one of the topics that recently getting more popular on political field or government-related things. Analyzing citizens' view of the government, including Governor and Vice Governor of DKI Jakarta for 2017-2022 period, is one of tasks that can be done using sentiment analysis. Data related to that topic are gathered from Twitter and Instagram for further analysis. N-gram, emoji, and all-caps is used as features to classify sentiment of each item. Based on the experiment, those features can help to increase classification performance. Naïve Bayes, Random Forest, and SVM algorithm are compared to select the best algorithm out of those three algorithms. Based on the experiment, SVM get the best result with highest accuracy and F1-score on both domains. The result of the classification shows that citizens tend to have neutral view on Governor and Vice Governor of DKI Jakarta for 2017-2022 period on their first year of governance. In addition, there are more positives than negatives on citizens' view.
E-SBOT: A Soft Service Robot for User-Centric Smart Service Delivery

In order to improve the user oriented service delivery, we present a soft service robot, e-SBOT. It plays the "bridge" role between a user and massive external services. By sensing explicit and implicit intentions and demands of a user, it helps find appropriate services and construct coarse-grained service solutions. Both global and personal knowledge graph are utilized to for intention reasoning and service solution planning. Sensors are designed for the dynamic changes of external services and resources. By e-SBOT, the cognition of a user could be amplified to a large extent. This paper gives a brief introduction to e-SBOT's technical challenges, architecture design, and key theoretical problems.
UdS Submission for the WMT 19 Automatic Post-Editing Task

In this paper, we describe our submission to the English-German APE shared task at WMT 2019.We utilize and adapt an NMT architecture originally developed for exploiting context information to APE, implement this in our own transformer model and explore joint training of the APE task with a de-noising encoder.
NICT’s Machine Translation Systems for the WMT19 Similar Language Translation Task

This paper presents the NICT's participation in the WMT19 shared Similar Language Translation Task.We participated in the Spanish-Portuguese task.For both translation directions, we prepared state-of-the-art statistical (SMT) and neural (NMT) machine translation systems.Our NMT systems with the Transformer architecture were trained on the provided parallel data enlarged with a large quantity of back-translated monolingual data.Our primary submission to the task is the result of a simple combination of our SMT and NMT systems.According to BLEU, our systems were ranked second and third respectively for the Portuguese-to-Spanish and Spanish-to-Portuguese translation directions.For contrastive experiments, we also submitted outputs generated with an unsupervised SMT system.
Video-Based Action Recognition Using Dimension Reduction of Deep Covariance Trajectories

Convolutional Neural Networks (CNNs) have been very successful in extracting discriminative features from video data. These deep features can be summarized using covariance descriptors for further analysis. However, due to large number of potential features, the covariance descriptors are often very high dimensional. To facilitate large scale data analysis, we propose a novel, metric-based dimension-reduction technique that reduces large covariances to small ones. Then, we represent videos as trajectories on the space of covariance matrices, or symmetric-positive definite matrices (SPDMs), and use a Riemannian metric on this space to quantify differences across these trajectories. These distance features can then be used for classification of video sequences. We illustrate this comprehensive framework using data from the UCF11 dataset for action recognition, with classification rates that match or outperform state-of-the-art techniques.
Research on Multi-USV Cooperative Search Method

A pheromone-based multi-USV search method is proposed to search target USV in unknown environment. The search area is discretized into grid area. Then the motion model, collision type and communication mode of multi-USV system are analyzed. Then the update method of moving target point and target point set of USV is analyzed. The update method is to calculate the pheromone of each target point, and the maximum target point of pheromone is selected as the next moving target point of USV, so that USV can select and update moving target points dynamically and in real time. Then, the fitness function is established based on artificial potential field method. Finally, the simulation experiment of the proposed algorithm is carried out, and the simulation results show that the proposed search method is in two different search environments. At the same time, the proposed method is compared with traversal method and random method, the simulation results show that the proposed multi-USVs cooperative search method has high detection rate of target USV, and each USV can adjust its motion path in real time according to the detected environmental information.