Reviewerly/exHarmony huggingface dataset:
Improving Joint Training of Inference Networks and Structured Prediction Energy Networks

Deep energy-based models are powerful, but pose challenges for learning and inference (Belanger and McCallum, 2016). Tu and Gimpel (2018) developed an efficient framework for energy-based models by training "inference networks" to approximate structured inference instead of using gradient descent. However, their alternating optimization approach suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning. In this paper, we contribute several strategies to stabilize and improve this joint training of energy functions and inference networks for structured prediction. We design a compound objective to jointly train both cost-augmented and test-time inference networks along with the energy function. We propose joint parameterizations for the inference networks that encourage them to capture complementary functionality during learning. We empirically validate our strategies on two sequence labeling tasks, showing easier paths to strong performance than prior work, as well as further improvements with global energy terms.
Ladder Loss for Coherent Visual-Semantic Embedding

For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way -- relevant or irrelevant, and all "irrelevant" candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. In particular, the new ladder loss is proposed by extending the triplet loss inequality to a more general inequality chain, which implements variable push-away margins according to respective relevance degrees. In addition, a proper Coherent Score metric is proposed to better measure the ranking results including those "irrelevant" candidates. Extensive experiments on multiple datasets validate the efficacy of our proposed method, which achieves significant improvement over existing state-of-the-art methods.
Real-Time Reinforcement Learning

Markov Decision Processes (MDPs), the mathematical framework underlying most algorithms in Reinforcement Learning (RL), are often used in a way that wrongfully assumes that the state of an agent's environment does not change during action selection. As RL systems based on MDPs begin to find application in real-world safety critical situations, this mismatch between the assumptions underlying classical MDPs and the reality of real-time computation may lead to undesirable outcomes. In this paper, we introduce a new framework, in which states and actions evolve simultaneously and show how it is related to the classical MDP formulation. We analyze existing algorithms under the new real-time formulation and show why they are suboptimal when used in real-time. We then use those insights to create a new algorithm Real-Time Actor-Critic (RTAC) that outperforms the existing state-of-the-art continuous control algorithm Soft Actor-Critic both in real-time and non-real-time settings. Code and videos can be found at https://github.com/rmst/rtrl.
Privacy control on location and co-location in interdependent data

Co-area data about clients is progressively accessible on the web. The clients' IP addresses additionally establish a wellspring of co-area data. Consolidated with area data, such co-areas can be utilized to enhance the deduction of the clients' areas, in this way further compromising their area protection: As co-area data is considered, not just a client's accounted for areas and versatility examples can be utilized to restrict her, yet in addition those of her companions. In this paper, we think about this issue by evaluating the impact of co-area data on area security, considering an enemy, for example, an interpersonal organization administrator that approaches such data. We formalize the issue and determine an ideal surmising calculation that joins such co-area data, yet at the expense of high unpredictability. We suggest some rough derivation calculations, including an answer that depends on the conviction spread calculation accomplish on a general Bayesian system model, and we broadly assess their execution. Our trial results demonstrate that, even for the situation where the foe considers co-areas of the focused on client with a solitary companion, the middle area security of the client is diminished by up to 62% in a run of the mill setting. We additionally ponder the impact of the diverse parameters in various situations.
MULTISENSORY FLOW EXPERIENCE THROUGH AUGMENTED-VIRTUAL REALITY INTERACTIVE TECHNOLOGY EDUCATION

The industry of education has appeared to be found as little bit slow at embracing the effectiveness and usefulness of new technologies.As for now, the advancement of technology in the field of learning is highly appreciated and anticipated as well.The concerning people have now started to share and exchange their technology hub in order to develop hi-tech communities that is facilitating educational system.Having a fair access to knowledge is indeed a human right.In this regard, advent of augmented and virtual reality education to facilitate learning around the world has helped the students get engaged in experiential learning.The learning is faster, better, retained and has caused at making improved decisions.Industrialization that changed the entire world could have not been possible without technological advancement and innovation.Virtual reality technology comes under the expertise of next generation that will benefit both the educators and the learners.
Continental Scale Validation of Analysis Ready Data in Australia: Experience With Satellite Derived Surface Reflectance

Analysis Ready Data (ARD) are satellite data that have been pre-processed for immediate analysis with minimal user effort. The generation of Surface Reflectance (SR) from optical satellite data, involves a series of corrections to standardise the data and enable meaningful comparison of data from multiple sensors and across time. Surface reflectance data are foundational for time-series analyses and rapid generation of other information products. Field based validation of surface reflectance data is therefore critical to determine its fitness for purpose, and applicability for downstream product development.In this paper, an approach for continental scale validation of the surface reflectance data from Landsat-8 and Sentinel-2 satellites, using field-based measurements that are near-synchronous to the satellite observations over multiple sites across Australia is presented. Good practice measurement protocols governing the acquisition of field data, including field instrument calibration, sampling strategy and approach for post-collection processing and management of field spectral data are outlined.This study has been a nationally coordinated, collaborative field data collection campaign across Australia. Permanent field sites, to support validation efforts within the broader Earth Observation (EO) community for continental scale products were also identified. The approach is expected to serve as a model for coordinated ongoing validation of ARD products at continental to global scales.
Google reigns supreme

For the first time, quantum computers are starting to flex their muscles, report Daniel Cossins and Leah Crane
The Influence of Family Wealth and Parent Education Level on Students� Reading Literacy

This study aims to examine the influence of family background factors in terms of family wealth and parent education levels on students reading performance in Indonesia. The study utilises secondary data from the OECDs Programme for International Student Assessment (PISA) 2015 for Indonesia, in which 6513 students participated. This also specifically highlights the analysis of family wealth and parent education levels in possibly predicting the students reading literacy in Indonesia. In analysing the data, a quantitative approach was used which utilised statistically different analysis such as t-test, one-way ANOVA, two-way ANOVA, correlation and multiple linear regression analysis using WesVar version 5.1 software.The result found there were significant different reading scores between students from different family wealth and parent education levels. The students from high family wealth performed better than they with middle and low wealthy. Likewise, the children with highly educated mother and father had high scores than students whose parents had low and did not complete primary school. Moreover, the result of correlation and regression analysis revealed that all predictor variables, WEALTH, MISCED and FISCED, significantly associate and predict better reading literacy performance of 15-year-old students in Indonesia for PISA 2015 survey. Therefore, the implications of the study highlight opportunities to reform educational policies through data and evidence.
Pencil Drawing Video Rendering Using Convolutional Networks

Abstract Traditional pencil drawing rendering algorithms when applied to video may suffer from temporal inconsistency and shower‐door effect due to the stochastic noise models employed. This paper attempts to resolve these problems with deep learning. Recently, many research endeavors have demonstrated that feed‐forward Convolutional Neural Networks (CNNs) are capable of using a reference image to stylize a whole video sequence while removing the shower‐door effect in video style transfer applications. Compared with video style transfer, pencil drawing video is more sensitive to the inconsistency of texture and requires a stronger expression of pencil hatching. Thus, in this paper we develop an approach by combining a latest Line Integral Convolution (LIC) based method, specializing in realistically simulating pencil drawing images, with a new feed‐forward CNN that can eliminate the shower‐door effect successfully. Taking advantage of optical flow, we adopt a feature‐map‐level temporal loss function and propose a new framework to avoid the temporal inconsistency between consecutive frames, enhancing the visual impression of pencil strokes and tone. Experimental comparisons with the existing feed‐forward CNNs have demonstrated that our method can generate temporally more stable and visually more pleasant pencil drawing video results in a faster manner.
Warp and Learn: Novel Views Generation for Vehicles and Other Objects

In this work we introduce a new self-supervised, semi-parametric approach for synthesizing novel views of a vehicle starting from a single monocular image. Differently from parametric (i.e. entirely learning-based) methods, we show how a-priori geometric knowledge about the object and the 3D world can be successfully integrated into a deep learning based image generation framework. As this geometric component is not learnt, we call our approach semi-parametric. In particular, we exploit man-made object symmetry and piece-wise planarity to integrate rich a-priori visual information into the novel viewpoint synthesis process. An Image Completion Network (ICN) is then trained to generate a realistic image starting from this geometric guidance. This careful blend between parametric and non-parametric components allows us to i) operate in a real-world scenario, ii) preserve high-frequency visual information such as textures, iii) handle truly arbitrary 3D roto-translations of the input and iv) perform shape transfer to completely different 3D models. Eventually, we show that our approach can be easily complemented with synthetic data and extended to other rigid objects with completely different topology, even in presence of concave structures and holes (e.g. chairs). A comprehensive experimental analysis against state-of-the-art competitors shows the efficacy of our method both from a quantitative and a perceptive point of view. Supplementary material, animated results, code and data are available at: this https URL
Changing the Structure of Paragraphs and Texts in Arabic: A Case from News Reporting

This study explores the textual alterations of Arabic news structure and how it has been influenced by news texts produced in English. The paper precisely examines sentence, paragraph and text structures in terms of form and content in relation to news translation. It analyses news articles collated from Aljazeera and Al-Arabiya news networks. The collated corpus is translations from English into Arabic by these two media outlets. The analysis showed considerable changes that the form of Arabic textual structures has incurred, especially in the general layout of texts. Although it confirm Hatim’s (1997) text-type categorisation with regard to argumentation in Arabic news texts that Arabic lacks argumentative elements it news content, it exhibited significant shift in internal cohesion, paragraph transitions, and syntactic patterns. These changes could emanate from many other influencing factors, but translation is definitely one.
The DICOM Image Compression and Patient Data Integration using Run Length and Huffman Encoder

Maintaining human healthcare is one of the biggest challenges that most of the increasing population in Asian countries are facing today.There is an unrelenting need in our medical community to develop applications that are low on cost, with high compression, as huge number of patient's data and images need to be transmitted over the network to be reviewed by the physicians for diagnostic purpose.This implemented work represents discrete wavelet-based threshold approach.Using this approach by applying N-level decomposition on 2D wavelet types like Biorthogonal, Haar, Daubechies, Coiflets, Symlets, Reverse Biorthogonal, and Discrete Meyer, various levels of wavelet coefficients are obtained.The lossless hybrid encoding algorithm, which combines run-length encoder and Huffman encoder, has been used for compression and decompression purpose.This work is proposed to examine the efficiency of different wavelet types and to determine the best.The objective of this research work is to improve compression ratio and compression gain.
A Scalable Multilabel Classification to Deploy Deep Learning Architectures For Edge Devices.

Convolution Neural Networks (CNN) have performed well in many applications such as object detection, pattern recognition, video surveillance and so on. CNN carryout feature extraction on labelled data to perform classification. Multi-label classification assigns more than one label to a particular data sample in a data set. In multi-label classification, properties of a data point that are considered to be mutually exclusive are classified. However, existing multi-label classification requires some form of data pre-processing that involves image training data cropping or image tiling. The computation and memory requirement of these multi-label CNN models makes their deployment on edge devices challenging. In this paper, we propose a methodology that solves this problem by extending the capability of existing multi-label classification and provide models with lower latency that requires smaller memory size when deployed on edge devices. We make use of a single CNN model designed with multiple loss layers and multiple accuracy layers. This methodology is tested on state-of-the-art deep learning algorithms such as AlexNet, GoogleNet and SqueezeNet using the Stanford Cars Dataset and deployed on Raspberry Pi3. From the results the proposed methodology achieves comparable accuracy with 1.8x less MACC operation, 0.97x reduction in latency and 0.5x, 0.84x and 0.97x reduction in size for the generated AlexNet, GoogleNet and SqueezeNet CNN models respectively when compared to conventional ways of achieving multi-label classification like hard-coding multi-label instances into single labels. The methodology also yields CNN models that achieve 50\% less MACC operations, 50% reduction in latency and size of generated versions of AlexNet, GoogleNet and SqueezeNet respectively when compared to conventional ways using 2 different single-labelled models to achieve multi-label classification.
Sparse Lifting of Dense Vectors: Unifying Word and Sentence Representations

As the first step in automated natural language processing, representing words and sentences is of central importance and has attracted significant research attention. Different approaches, from the early one-hot and bag-of-words representation to more recent distributional dense and sparse representations, were proposed. Despite the successful results that have been achieved, such vectors tend to consist of uninterpretable components and face nontrivial challenge in both memory and computational requirement in practical applications. In this paper, we designed a novel representation model that projects dense word vectors into a higher dimensional space and favors a highly sparse and binary representation of word vectors with potentially interpretable components, while trying to maintain pairwise inner products between original vectors as much as possible. Computationally, our model is relaxed as a symmetric non-negative matrix factorization problem which admits a fast yet effective solution. In a series of empirical evaluations, the proposed model exhibited consistent improvement and high potential in practical applications.
ODE-Inspired Analysis for the Biological Version of Oja's Rule in Solving Streaming PCA

Oja's rule [Oja, Journal of mathematical biology 1982] is a well-known biologically-plausible algorithm using a Hebbian-type synaptic update rule to solve streaming principal component analysis (PCA). Computational neuroscientists have known that this biological version of Oja's rule converges to the top eigenvector of the covariance matrix of the input in the limit. However, prior to this work, it was open to prove any convergence rate guarantee. In this work, we give the first convergence rate analysis for the biological version of Oja's rule in solving streaming PCA. Moreover, our convergence rate matches the information theoretical lower bound up to logarithmic factors and outperforms the state-of-the-art upper bound for streaming PCA. Furthermore, we develop a novel framework inspired by ordinary differential equations (ODE) to analyze general stochastic dynamics. The framework abandons the traditional step-by-step analysis and instead analyzes a stochastic dynamic in one-shot by giving a closed-form solution to the entire dynamic. The one-shot framework allows us to apply stopping time and martingale techniques to have a flexible and precise control on the dynamic. We believe that this general framework is powerful and should lead to effective yet simple analysis for a large class of problems with stochastic dynamics.
Details and Proofs for: Stability Analysis of Multivariable Digital Control Systems with Uncertain Timing

The ever increasing complexity of real-time control systems results in significant deviations in the timing of sensing and actuation, which may lead to degraded performance or even instability. In this paper we present a method to analyze stability under mostly-periodic timing with bounded uncertainty, a timing model typical for the implementation of controllers that were actually designed for strictly periodic execution. In contrast to existing work, we include the case of multiple sensors and actuators with individual timing uncertainty. Our approach is based on the discretization of a linear impulsive system. To avoid the curse of dimensionality, we apply a decomposition that breaks down the complex timing dependency into the effects of individual sensor-actuator pairs. Finally, we verify stability by norm bounding and a Common Quadratic Lyapunov Function. Experimental results substantiate the effectiveness of our approach for moderately complex systems.
Examining the Impact of Discretization Technique on Sentiment Analysis for the Greek Language

Nowadays, information, communication and interaction between people worldwide have been facilitated by the rapid development of technology and they are mainly achieved through the internet. Internet users are now new creators of information data and express their ideas, their opinions, their feelings and their attitudes about products and services rather than passive information recipients. Given the evolution of modern technological advances, such as the proliferation of mobile devices social networks and services is extending. User-generated content in social media constitutes a very meaningful information source and consists of opinions towards various events and services. In this paper, we present a methodology that aims to analyze Greek text and extract indicative info towards users' opinions and attitudes. Specifically, we describe a supervised approach adopted that analyzes and classifies comments and reviews into the appropriate polarity category. Discretization techniques are also applied to improve the performance and the accuracy of classification procedures. Finally, we present an experimental evaluation that was designed and conducted and which revealed quite interesting findings.
Sistem Informasi Administrasi Membership Pada Pusat Kebugaran Gorilla Gym Jakarta

One of the characteristics of a community in a big city is the desire to live a healthy life, one of them is exercising by visiting a fitness center that has complete sports facilities and adequate equipment. with the increasing interest of the community to exercise the obstacles faced by fitness center providers are the complexity of handling membership, managing difficult training schedules and the limited information available regarding the management of a fitness center. to overcome these obstacles the author designed a membership administration information system at the fitness center by using the Agile Development methodology so that the system designed to be completed quickly and changes that often occur can be addressed immediately, this study uses a unified modeling language (UML) to design and document the system. As for the software used for system design is PHP web programming so that it can be accessed from anywhere and anytime with database management using MySql. So it is expected that the results of this study are membership fitness administration information system that is expected to help the manager in facilitating membership administration.