Reviewerly/exHarmony huggingface dataset:
Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring

Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for ASP. Yet, the great request of resources for certain types of problems, as the planning ones, still constitutes a big limitation for problem solving. Particularly, in the case the program is grounded before the resolving phase, an exponential blow up of the grounding can generate a huge ground file, infeasible for single machines with limited resources, thus preventing even the discovering of a single non-optimal solution. To address this problem, in this paper we present a distributed approach to ASP solving, exploiting distributed computation benefits in order to overcome the just explained limitations. The here presented tool, which is called Distributed Answer Set Coloring (DASC), is a pure solver based on the well-known Graph Coloring algorithm. DASC is part of a bigger project aiming to bring logic programming into a distributed system, started in 2017 by Federico Igne with mASPreduce and continued in 2018 by Pietro Totis with a distributed grounder. In this paper we present a low level implementation of the Graph Coloring algorithm, via the Boost and MPI libraries for C++. Finally, we provide a few results of the very first working version of our tool, at the moment without any strong optimization or heuristic.
STELA: A Real-Time Scene Text Detector with Learned Anchor

To achieve high coverage of target boxes, a normal strategy of conventional one-stage anchor-based detectors is to utilize multiple priors at each spatial position, especially in scene text detection tasks. In this work, we present a simple and intuitive method for multi-oriented text detection where each location of feature maps only associates with one reference box. The idea is inspired from the twostage R-CNN framework that can estimate the location of objects with any shape by using learned proposals. The aim of our method is to integrate this mechanism into a onestage detector and employ the learned anchor which is obtained through a regression operation to replace the original one into the final predictions. Based on RetinaNet, our method achieves competitive performances on several public benchmarks with a totally real-time efficiency (26:5fps at 800p), which surpasses all of anchor-based scene text detectors. In addition, with less attention on anchor design, we believe our method is easy to be applied on other analogous detection tasks. The code will publicly available at https://github.com/xhzdeng/stela.
Multi-Temporal Aerial Image Registration Using Semantic Features

A semantic feature extraction method for multitemporal high resolution aerial image registration is proposed in this paper. These features encode properties or information about temporally invariant objects such as roads and help deal with issues such as changing foliage in image registration, which classical handcrafted features are unable to address. These features are extracted from a semantic segmentation network and have shown good robustness and accuracy in registering aerial images across years and seasons in the experiments.
Real-World Robotic Perception and Control Using Synthetic Data

Author(s): Tobin, Joshua P | Advisor(s): Abbeel, Pieter | Abstract: Modern deep learning techniques are data-hungry, which presents a problem in robotics because real-world robotic data is difficult to collect. Simulated data is cheap and scalable, but jumping the to use simulated data for real-world tasks is challenging. In this thesis, we discuss using synthetic data to learn visual models that allow robots to perform manipulation tasks in the real world. We begin by discussing domain randomization, a technique for bridging the reality gap by massively randomizing the visual properties of the simulator. We demonstrate that, using domain randomization, synthetic data alone can be used to train a deep neural network to localize objects accurately enough for a robot to grasp them in the real world. The remainder of the thesis discusses extensions of this approach to a broader range of objects and scenes. First, we introduce a data generation pipeline inspired by the success of domain randomization for visual data that creates millions of unrealistic procedurally generated random objects, removing the assumption that 3D models of the objects are present at training time. Second, we reformulate the problem from pose prediction to grasp prediction and introduce a generative model architecture that learns a distribution over grasps, allowing our models to handle pose ambiguity and grasp a wide range of objects with a single neural network. Third, we introduce an attention mechanism for 3-dimensional data. We demonstrate that this attention mechanism can be used to perform higher fidelity neural rendering, and that models learned this way can be fine-tuned to perform accurate pose estimation when the camera intrinsics are unknown at training time.We conclude by surveying recent applications and extensions of domain randomization in the literature and suggesting several promising directions for research in sim-to-real transfer for robotics.
Empower Good Governance with Public Assessed Schemes by Improved Sentiment Analysis Accuracy

Many government schemes were unsuccessful because lack of proper feedback on the ongoing schemes, where billion dollars investment is going to be in vain. Sentiment analysis is one of best approach to analyse opinions of the peoples on various government schemes. Sentiment analysis and machine learning techniques emerged to analyse huge social media corpora to track people's views on government policies, products and services. Sentiment analysis process consists of various phases which include data discovery, data collection, data pre-processing, and data analysis. Stemming is a process to generate the morphemes in natural language sentences for various applications such as sentiment analysis, information retrieval, and domain analysis. The stemming process involved two major errors, which are over-stemming and under-stemming errors. Most of sentiment analysis natural languages processing applications used Lancaster and Porter stemming algorithms where more than one word inflected into same morpheme, which causes the etymology behaviour of the stemming word and prone to classify the tweets false positives and false negative. The proposed un-prejudice light stemming algorithm prevent etymology behaviour of morpheme and sustain its meaning during stemming process by selecting a word which has maximum number of synonyms in lexical database.
Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems

This paper explores several strategies for Forensic Voice Comparison (FVC), aimed at improving the performance of the LRs when using generative Gaussian score-to-LR models. First, different anchoring strategies are proposed, with the objective of adapting the LR computation process to the case at hand, always respecting the propositions defined for the particular case. Second, a fully-Bayesian Gaussian model is used to tackle the sparsity in the training scores that is often present when the proposed anchoring strategies are used. Experiments are performed using the 2014 i-Vector challenge set-up, which presents high variability in a telephone speech context. The results show that the proposed fully-Bayesian model clearly outperforms a more common Maximum-Likelihood approach, leading to high robustness when the scores to train the model become sparse.
ANALISIS KLASTER PAUTAN LENGKAP UNTUK MENGELOMPOKKAN KABUPATEN/KOTA DI PROVINSI SULAWESI TENGAH BERDASARKAN INDIKATOR KRIMINALITAS

Criminality is all kinds of actions and deeds which is economically and psychologically harmful. The statistical method could be used to classify the crime is cluster analysis. Cluster analysis is a multivariate method which aims to classify a sample of subjects (or objects) on the basis of a set of measured variables into a number of different groups such that similar subjects are placed in to the same group. The objective of this research is to classify Regency/City in Central Sulawesi Province based on the criminality indicator and to discover the profile of each cluster which had been formed. The results of the study shows that those are two clusters formed: Cluster 1 consists of Buol, Banggai, Morowali, Toli-Toli, Donggala, and Tojo Una-Una Regency; Cluster 2 consists of Regency/Palu City, and Parigi Moutong. The profile of each cluster is: Cluster 1 with low crime rate on average and Cluster 2 with high crime rate on average.Keywords : Cluster Analysis, Complete Linkage, Criminality, Hierarchy Method.
28 Deep learning based cytology negative sample screening method

<h3>Objectives</h3> We show that deep learning can be used to create high accuracy cells classifiers that can support cytologists in their work. Two deep learning models are presented. The first model is used for cervical cancer cytology negative/positive prescreening and we show its high specificity at 100% Negative Predict Value (NPV). The second model is used to predict among 15 categories common to cervical analysis, as listed in figure 1(b). Our classifiers include Grad-Cam visualizations that show both models concentrate on relevant areas of images. <h3>Methods</h3> Classifiers are based on deep convolutional networks. The crucial aspects in achieving high accuracy are data augmentation and focal loss. Grad-Cam visualizations are used to explain models’ reasoning. <h3>Results</h3> Prescreening model, based on 2700 samples, achieves 95.89% overall accuracy and 91.62% specificity at 100% NPV (figure 1(a)) ; second stage screening model, based on approximately 9500 samples, achieves 92.03% overall accuracy (figure 1(b)). Grad-Cam visualizations show that both models concentrate on relevant areas of the image when making predictions (figure 1(c)). <h3>Conclusions</h3> We show that deep learning based classifiers can be useful in supporting cytologist in their work. Approximately 92% of samples that cytologist screen for cervical cancer are negative. With our model cytologists can concentrate their efforts on only positive samples and a small number of false positives predictions.
Enkripsi Data Citra untuk Model Warna RGB dan Treshold Menggunakan Algoritma Hill Cipher

With the increasingly widespread abuse of digital media, especially in the form of images or images, it increasingly disrupts the rights and privacy of everyone. Many forms of abuse that occur in digital media through internet facilities such as plagiarism of photographer&amp;#39;s work, recognition of the rights of the image, to upload photos of privacy to internet media. Therefore, one way to secure digital data in the form of images is to randomize (encrypt) the images that we feel are very important so that the image can no longer be interpreted by others. If we need these data, we just need to return it (decryption) so that the encryption image can return to its original form. Hill Cipher algorithm is one method to randomize a data by encoding and multiplying the matrix. For its application in the form of image data, a trial is needed by creating a software which will then analyze the results into several color models such as RGB, Grayscale (Keabuan) and Tresholding (Black and White). From the test results, it can be concluded that the greater the input matrix value of the Hill Cipher Algorithm, the more encrypted image results will be obtained or in other words the more incomprehensible the visual form by humans. Then the Hill Cipher algorithm cannot be applied to the color threshold model (black and white) because the matrix multiplication obtained does not have a diverse range of values
Carnegie Mellon’s first Open Science Symposium - Themes about research data and their reuse

Poster presented at the Research Data Alliance Plenary Meeting 13 in Philadelphia, PA April 1-4, 2019. In October 2018, Carnegie Mellon University (Pittsburgh, PA, USA) hosted its first Open Science Symposium (event website: https://events.mcs.cmu.edu/oss2018/). The two-day event was a collaboration between data specialists in the University Libraries and life sciences faculty and was funded by a grant for life sciences from the DSF Charitable Foundation. The first day was a symposium featuring talks, discussions, and a scientific speed dating reception. The second day was a series of hands-on workshops on open science tools and platforms. The event brought together researchers from across the sciences including faculty, graduate students, post-docs and data professionals in neuroscience, biology, computer science, and engineering. Guest speakers, 18 in total from both Carnegie Mellon as well as other from outside the university, included researchers with success stories of open science, creators of open source tools, and publishers and funders working to support emerging open science practices. The speakers were organized into four sessions (Open Science in Research, Open Data and Reproducibility, Open Tools and Platforms, and Open Access) and gave brief talks followed by a panel QA 2) new collaborative methods for data collection and analysis are reshaping the way that science is done, including the ‘observatory model’ of centralized data collection, citizen science, and data science; 3) open data does not equal reusable data - standard formatting and documentation (plus hard work and incentivization) is necessary to make data truly reproducible and reusable, not just open. The poster will present these themes together with examples from life sciences research presented by the symposium presenters. Presentations from the event have been documented on Open Science Framework: https://osf.io/54gue/. Carnegie Mellon University Libraries is currently growing their support for open science through expert services and infrastructure for researchers. The 2nd Open Science Symposium will be held on Nov. 7, 2019 in Pittsburgh.
Virtual Password Authentication Scheme in Hashed Domain

This paper proposes a virtual password authentication scheme which is able to work in the hashed domain. Many research in the area of virtual password have been proposed to overcome keylogger attacks. Unfortunately, current methods are still lack of complexities and have a short cracking time. Several research have partially overcome these problems. However, the proposed methods work only in the plain text domain which is vulnerable to the privacy and security attacks. To overcome this problem, we proposed a virtual password scheme that generates dynamic length of a random password consisting of both uppercase and lowercase alphabets, numbers, and symbols. Importantly, our method is also able to compare user's password despite it is hashed. With an efficient algorithm that help this method's computation time somewhat similar with regular login, the cracking time and complexity of this method are superior which scores 174 on password complexity and calculated to be cracked after 10,000+ and 9 centuries by brute force attack using a personal computer and conficker botnets respectively.
Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.
Route Packing: Geospatially-Accurate Visualization of Route Networks

We present route packing, a novel (geo)visualization technique for displaying several routes simultaneously on a geographic map while preserving the geospatial layout, identity, directionality, and volume of individual routes. The technique collects variable-width route lines side by side while minimizing crossings, encodes them with categorical colors, and decorates them with glyphs to show their directions. Furthermore, nodes representing sources and sinks use glyphs to indicate whether routes stop at the node or merely pass through it. We conducted a crowd-sourced user study investigating route tracing performance with road networks visualized using our route packing technique. Our findings highlight the visual parameters under which the technique yields optimal performance.
An AN ERROR ANALYSIS OF USING CONJUNCTION IN WRITING NARRATIVE TEXT (A Study at the First Grade Students of MTs YPKS Padangsidimpuan 2018/2019 Academic Year)

The aim of this research is to explain the quality of students’ writing in terms of conjunction and to analyze students’ problems using conjunction in writing narrative text. This research is a qualitative research method. This research also aim to know the causes of stuents’ problem using conjunction in writing narrative text. The writer chooses MTs YPKS Padangsidimpuan as the location of the research. The informant of this research are all of the students who comes from the seventh grade of MTs YPKS Padangsidimpuan. But, the writer only took one class to do this research that is VII-1.There are 30 students in this class and the writer only focus on 9 students. They have problems in using conjunction. The problems are related to the use of coordinating and subordinating conjunction. The reason of the problems in using conjunction in writing is based on linguistic, pragmatic, cognitive and cultural factor. Four of them give influence that causes the lack conjunction writing. Therefore, an intensive attention should be given by the lecture to facilitate the students to practice writing in the classroom and do peer correction.
Chiral Quantum Photonics in Semiconductor Nano-Photonic Waveguides

In this presentation, I will review progress towards implementing chiral quantum photonics in GaAs nano-photonic waveguides. The chiral coupling occurs when a semiconductor quantum dot (QD) is positioned close to a C-Point (chiral point) of the waveguide. The off-centre positioning of the QD breaks the inversion symmetry, and results in a strong dependence of the propagation direction on the circular polarization of the optical mode, with spin up and spin down exciton spins coupling to the left and right propagation directions respectively. In previous work we demonstrated incoherent, spin-dependent directional photoluminescence and initialization using non-resonant and quasi-resonant excitation [1], [2]. In this presentation, I will present results for coherent, resonant excitation in transmission and reflection geometries [3]. Numerical simulations for a quantum two-level system are able to reproduce the observed behaviour, and indicate that the spin-dependent phase shift in transmission is around 0.1ʌ in the current devices. Strategies to increase the phase shift towards the ideal value of ʌ required to implement chip-based spin networks will be discussed.
Performance of Recommender Systems: Based on Content Navigator and Collaborative Filtering

In the world of big data, many people find it difficult to access the information they need quickly and accurately. In order to overcome this, research on the system that recommends information accurately to users is continuously conducted. Collaborative Filtering is one of the famous algorithms among the most used in the industry. However, collaborative filtering is difficult to use in online systems because user recommendation is highly volatile in recommendation quality and requires computation using large matrices. To overcome this problem, this paper proposes a method similar to database queries and a clustering method (Contents Navigator) originating from a complex network.
Job scheduling on data centers with deep reinforcement learning

Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.
ANALISIS DAN EVALUASI DRAINASE KAWASAN PERUMAHAN BLANG BEURANDANG KABUPATEN ACEH BARAT

Many of the current drainage conditions have decreased in service functions. Some problems with  decreasing  drainage  function  are  inadequate  channel  capacity,  where  the  channel  is unable to accommodate flood discharge when rainfall with high intensity.. One of the area that have problems from the drainage system is housing in Blang Beurandang I, West Aceh. With this condition, the purpose of this research is to identify the existing capacity conditions of existing channels, so that the real condition of the existing channel will be obtained. The results of the comparison between the flood discharge and the existing channel capacity obtained that there are 26 channel that are not able to accommodate the flood discharge plan that is channels 43 , 41, 20, 13, 14, 10, 25.11, 49.50, 26, 27, 52, 31, 5, 30, 53, 54, 2, 32, 36, 38, 40, 35, 39 and channel 42. This condition requires alternative handling so that the channel can accommodate  t h e rain discharge with in enlarge the channel dimension. Channel planning consists of several sizes and dimensions with a budget of costs that is Rp. 736.218/000,-. The results of this study will also be a reference for the Village Government in planning the construction of drainage channels and the provide a budget as an effort to handle inundation and floods.