Reviewerly/exHarmony huggingface dataset:
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension

Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages.Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem.We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.
CoVSCode: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE

Real-time collaborative programming is an emerging approach that supports a team of programmers to view and edit shared source code at the same time. Each programmer can edit any part of the source code, and changes become instantly visible at other collaborating sites. With a broad range of application scenarios and benefits, real-time collaborative programming has attracted increasing interest from academia and industry. Lightweight integrated development environments (lightweight IDEs) have rapidly grown in popularity in the recent years, but there are serious problems and limitations with existing real-time collaboration support for lightweight IDEs. In this study, we contribute a novel real-time collaborative programming environment named CoVSCode that supports unconstrained and flexible real-time collaboration based on Visual Studio Code, one of the most popular and widely used lightweight IDEs. We present design objectives and rationales, the workflow and functional design from collaborating programmers’ perspectives, major technical issues and solutions, and prototype implementation, as well as a set of experimental evaluations that have demonstrated the technical feasibility and good performance of the prototype system. All approaches, techniques and solutions derived in this work are generic, which can also be applied in building real-time collaborative programming environments for other lightweight IDEs.
Performance Study of Mixed Reality for Edge Computing

Edge computing is a recent paradigm where computing resources are placed close to the user, at the edge of the network. This is a promising enabler for applications that are too resource-intensive to be run on an end device, but at the same time require too low latency to be run in a cloud, such as for example mixed reality (MR). In this work, we present MR-Leo, a prototype for creating an MR-enhanced video stream. It enables offloading of the point cloud creation and graphic rendering at the edge. We study the performance of the prototype with regards to latency and throughput in five different configurations with different alternatives for the transport protocol, the video compression format and the end/edge devices used. The evaluations show that UDP and MJPEG are good candidates for achieving acceptable latency and that the design of the communication protocol is critical for offloading video stream analysis to the edge.
Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis

Information security risk analysis is a compulsory requirement both from the side of regulating documents and information security management decision making process. Some researchers propose using expert systems (ES) for process automation, but this approach requires the creation of a high-quality knowledge base. A knowledge base can be formed both from expert knowledge or information collected from other sources of information. The problem of such approach is that experts or good quality knowledge sources are expensive. In this paper we propose the problem solution by providing an automated ES knowledge base development method. The method proposed is novel since unlike other methods it does not integrate ontology directly but utilizes automated transformation of existing information security ontology elements into ES rules: The Web Ontology Rule Language (OWL RL) subset of ontology is segregated into Resource Description Framework (RDF) triplets, that are transformed into Rule Interchange Format (RIF); RIF rules are converted into Java Expert System Shell (JESS) knowledge base rules. The experiments performed have shown the principal method applicability. The created knowledge base was later verified by performing comparative risk analysis in a sample company.
Interactive Marker-based Augmented Reality for CPR Training

CPR, or Cardiopulmonary Resuscitation, is a lifesaving technique useful for the case in which someone's heartbeat or breathing has stopped due to heart attack.Without proper CPR, nine out of ten patients die.The American Heart Association recommends CPR with chest compressions in the event of witnessing such an incident.For proper CPR training, taking a class with a CPR instructor is usually the best choice, but it is not practical and costly for mass training, especially in schools and universities.There are many new techniques available that can replace traditional CPR training and Augmented Reality (AR) is one of them.AR is the technology that integrates virtual objects or environments, created by digital technology, with the real world.Augmented Reality using marker-based technique is a good option, since a trainee can have a realistic look at the patient, know the position of the hand on the chest, identify the number of chest compressions per minute, and also know the pressure that he or she puts on the chest.Besides that, the status of the operation can be displayed along with a recording system for analysis.In this research, we chose marker-based AR due to its precision in distance measurement.For measuring the pressure on the chest, we use a marker-marker interaction technique.Unity 3D cross-platform game engine and Qualcomm's Vuforia-an augmented reality software development kit (SDK) for mobile devices that enables the creation of augmented reality applications-are required.The results from our experiment with a group of people with non-CPR training confirm that the configuration increases the speed and accuracy of CPR training.
An Empirical Study Into the Success of Listed Smart Contracts in Ethereum

Since it takes time and effort to put a new product or service on the market, one would like to predict whether it will be a success. In general this is not possible, but it is possible to follow best practices in order to maximize the chance of success. A smart contract is intended to encode business logic and is therefore at the heart of every new business on the Ethereum blockchain. We have investigated how to measure the success of smart contracts, and whether successful smart contracts have characteristics that less successful smart contracts lack. The appearance of a smart contract on a listing website such as Etherscan or StateoftheDapps is such a characteristic. In this paper, we present a three-pronged analysis of the relative success of listed smart contracts. First, we have used statistical analysis on the publicly visible transaction history of the Ethereum blockchain to determine that listed contracts are significantly more successful than their unlisted counterparts. Next, we have conducted a survey among more than 200 developers via an anonymous online survey about their experience with the listing process. A significant majority of respondents do not believe that listing a contract itself contributes to its success, but they believe that the extra attention that is typically paid in tandem with the listing process does contribute. Finally, based on the respondents' answers, we have drafted 10 recommendations for developers and validated them by submitting them to an international panel of experts.
In Search for a SAT-friendly Binarized Neural Network Architecture

Analyzing the behavior of neural networks is one of the most pressing challenges in deep learning. Binarized Neural Networks are an important class of networks that allow equivalent representation in Boolean logic and can be analyzed formally with logic-based reasoning tools like SAT solvers. Such tools can be used to answer existential and probabilistic queries about the network, perform explanation generation, etc. However, the main bottleneck for all methods is their ability to reason about large BNNs efficiently. In this work, we analyze architectural design choices of BNNs and discuss how they affect the performance of logic-based reasoners. We propose changes to the BNN architecture and the training procedure to get a simpler network for SAT solvers without sacrificing accuracy on the primary task. Our experimental results demonstrate that our approach scales to larger deep neural networks compared to existing work for existential and probabilistic queries, leading to significant speed ups on all tested datasets.
Optimal Convolutional Neural Network Architecture Design Using Clonal Selection Algorithm

In this paper, the Clonal Selection Algorithm (CSA) is implemented to determine the optimal architecture of a convolutional neural network (CNN) for the classification of handwritten character digits.The efficacy of CNN in image recognition is one of the central motives why the world has woken up to the effectiveness of deep learning.During training, an optimal CNN architecture can extract complex features from the data that is being trained; however, the ideal architecture of a CNN for a specific problem cannot be determined by some standard procedure.In practice, CNN architectures are generally designed using human expertise and domain knowledge.By using CSA, optimal architecture of CNN can be determined autonomously through evolution of hyperparameters of the architecture for a given dataset.In this work, proposed methodology is tested on EMNIST dataset which is an enhanced version of MNIST dataset.The results have proven that the CSA based tuning is capable of generating optimal CNN architectures.Through this proposed technique, the best architecture of CNN for a given problem can be selfdetermined without any human intervention.
Representing Closed Transformation Paths in Encoded Network Latent Space

Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space. In many cases, data transformations are defined by linear paths in this latent space. However, the Euclidean structure of the latent space may be a poor match for the underlying latent structure in the data. In this work, we incorporate a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latent space to accommodate this structure. In particular, we focus on applications in which the data has closed transformation paths which extend from a starting point and return to nearly the same point. Through experiments on data with natural closed transformation paths, we show that this model introduces the ability to learn the latent dynamics of complex systems, generate transformation paths, and classify samples that belong on the same transformation path.
Single Image Highlight Removal for Real-Time Image Processing Pipelines

This paper presents a fully automatic method for the separation of diffuse and specular reflection components from a single image.Overall, the mechanisms in which the available methods operate on are computationally costly and do not translate well to modern hardware-implemented image processing pipelines, such as the ones present in consumer electronics.Consequently, the objective of this article is to introduce a simple yet effective method for specular highlight removal.It is based on the dichromatic reflection model and operates through histogram matching in the YCbCr color space.The proposed method performs in real-time.It only uses global image statistics and point-wise intensity transformations.Experimental evaluation shows that the proposed approach has competitive results in comparison to stateof-the-art methods.Limitations of the proposed approach are seldom and are common to most methods available.The proposed method, however, achieves better quality results with much less computational cost, thus enabling feasibility in systems with limited processing power.
Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model – a neural architecture consisting of two hierarchically connected Transformer networks – is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

Dynamic patterns are characterized by complex spatial and motion patterns. Understanding dynamic patterns requires a disentangled representational model that separates the factorial components. A commonly used model for dynamic patterns is the state space model, where the state evolves over time according to a transition model and the state generates the observed image frames according to an emission model. To model the motions explicitly, it is natural for the model to be based on the motions or the displacement fields of the pixels. Thus in the emission model, we let the hidden state generate the displacement field, which warps the trackable component in the previous image frame to generate the next frame while adding a simultaneously emitted residual image to account for the change that cannot be explained by the deformation. The warping of the previous image is about the trackable part of the change of image frame, while the residual image is about the intrackable part of the image. We use a maximum likelihood algorithm to learn the model parameters that iterates between inferring latent noise vectors that drive the transition model and updating the parameters given the inferred latent vectors. Meanwhile we adopt a regularization term to penalize the norms of the residual images to encourage the model to explain the change of image frames by trackable motion. Unlike existing methods on dynamic patterns, we learn our model in unsupervised setting without ground truth displacement fields or optical flows. In addition, our model defines a notion of intrackability by the separation of warped component and residual component in each image frame. We show that our method can synthesize realistic dynamic pattern, and disentangling appearance, trackable and intrackable motions. The learned models can be useful for motion transfer, and it is natural to adopt it to define and measure intrackability of a dynamic pattern.
Image Formation Model Guided Deep Image Super-Resolution

We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. The proposed algorithm first uses a deep neural network to estimate intermediate high-resolution images, blurs the intermediate images using known blur kernels, and then substitutes values of the pixels at the un-decimated positions with those of the corresponding pixels from the low-resolution images. The output of the pixel substitution process strictly satisfies the image formation model and is further refined by the same deep neural network in a cascaded manner. The proposed framework is trained in an end-to-end fashion and can work with existing feed-forward deep neural networks for super-resolution and converges fast in practice. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods.
A Study of Call Graph Construction for JVM-Hosted Languages

Call graphs have many applications in software engineering, including bug-finding, security analysis, and code navigation in IDEs. However, the construction of call graphs requires significant investment in program analysis infrastructure. An increasing number of programming languages compile to the Java Virtual Machine (JVM), and program analysis frameworks such as WALA and SOOT support a broad range of program analysis algorithms by analyzing JVM bytecode. This approach has been shown to work well when applied to bytecode produced from Java code. In this paper, we show that it also works well for diverse other JVM-hosted languages: dynamically-typed functional Scheme, statically-typed object-oriented Scala, and polymorphic functional OCaml. Effectively, we get call graph construction for these languages for free, using existing analysis infrastructure for Java, with only minor challenges to soundness. This, in turn, suggests that bytecode-based analysis could serve as an implementation vehicle for bug-finding, security analysis, and IDE features for these languages. We present qualitative and quantitative analyses of the soundness and precision of call graphs constructed from JVM bytecodes for these languages, and also for Groovy, Clojure, Python, and Ruby. However, we also show that implementation details matter greatly. In particular, the JVM-hosted implementations of Groovy, Clojure, Python, and Ruby produce very unsound call graphs, due to the pervasive use of reflection, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invokedynamic</monospace> instructions, and run-time code generation. Interestingly, the dynamic translation schemes employed by these languages, which result in unsound static call graphs, tend to be correlated with poor performance at run time.
Optimization of Feature Selection Using Genetic Algorithm in Naïve Bayes Classification for Incomplete Data

In the case of high dimensional data with missing values, the process of collecting data from various sources may be miss accidentally, which affected the quality of learning outcomes.a large number of machine learning methods can be applied to explore the search area for imputation and selection of features and parameters.ML classification needs preprocessing with self-organizing map imputation (SOMI) before the imputation of missing values is done to improve the accuracy of the model.This study introduces a new approach that combines naïve Bayes classification (NBC) and genetic algorithm (GA) optimization procedures to effectively explore the search space based on a sample of experimental points.GA is a classification model approach based on the selection of features that cause computational problems, such as reduced dimensions, uncertainty and imbalanced data sets with various classes.In the experiment, preprocessing the data using SOMI yielded error results that were up to 10% for various data sets with missing data compared to other methods.In the SOMI-GANB hybrid model, the experimental results show that the proposed method can significantly improve accuracy by up to 90% compared to other imputation methods and without feature selection.SOMI can be used for homogeneous, heterogeneous and mixed data sets.The results from the experiment clearly showed that the proposed method could significantly increase the yield compared to the other imputation methods and without feature selection.The combination of GA and naïve Bayes classification was chosen because they are simple, easy-to-understand methods that are very effective in finding optimal solutions from a set of possible solutions.Naïve Bayes imputation had higher accuracy compared to neural network imputation.
Inefficiency of K-FAC for Large Batch Size Training

There have been several recent work claiming record times for ImageNet training. This is achieved by using large batch sizes during training to leverage parallel resources to produce faster wall-clock training times per training epoch. However, often these solutions require massive hyper-parameter tuning, which is an important cost that is often ignored. In this work, we perform an extensive analysis of large batch size training for two popular methods that is Stochastic Gradient Descent (SGD) as well as Kronecker-Factored Approximate Curvature (K-FAC) method. We evaluate the performance of these methods in terms of both wall-clock time and aggregate computational cost, and study the hyper-parameter sensitivity by performing more than 512 experiments per batch size for each of these methods. We perform experiments on multiple different models on two datasets of CIFAR-10 and SVHN. The results show that beyond a critical batch size both K-FAC and SGD significantly deviate from ideal strong scaling behaviour, and that despite common belief K-FAC does not exhibit improved large-batch scalability behavior, as compared to SGD.
List Decoding of Direct Sum Codes

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)List Decoding of Direct Sum CodesVedat Levi Alev, Fernando Granha Jeronimo, Dylan Quintana, Shashank Srivastava, and Madhur TulsianiVedat Levi Alev, Fernando Granha Jeronimo, Dylan Quintana, Shashank Srivastava, and Madhur Tulsianipp.1412 - 1425Chapter DOI:https://doi.org/10.1137/1.9781611975994.85PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider families of codes obtained by “lifting” a base code through operations such as k-XOR applied to “local views” of codewords of , according to a suitable k-uniform hypergraph. The k-XOR operation yields the direct sum encoding used in works of [Ta-Shma, STOC 2017] and [Dinur and Kaufman, FOCS 2017]. We give a general framework for list decoding such lifted codes, as long as the base code admits a unique decoding algorithm, and the hypergraph used for lifting satisfies certain expansion properties. We show that these properties are indeed satisfied by the collection of length k walks on a sufficiently strong expanding graph, and by hypergraphs corresponding to high-dimensional expanders. Instantiating our framework, we obtain list decoding algorithms for direct sum liftings corresponding to the above hypergraph families. Using known connections between direct sum and direct product, we also recover (and strengthen) the recent results of Dinur et al. [SODA 2019] on list decoding for direct product liftings. Our framework relies on relaxations given by the Sum-of-Squares (SOS) SDP hierarchy for solving various constraint satisfaction problems (CSPs). We view the problem of recovering the closest codeword to a given (possibly corrupted) word, as finding the optimal solution to an instance of a CSP. Constraints in the instance correspond to edges of the lifting hypergraph, and the solutions are restricted to lie in the base code . We show that recent algorithms for (approximately) solving CSPs on certain expanding hypergraphs by some of the authors also yield a decoding algorithm for such lifted codes. We extend the framework to list decoding, by requiring the SOS solution to minimize a convex proxy for negative entropy. We show that this ensures a covering property for the SOS solution, and the “condition and round” approach used in several SOS algorithms can then be used to recover the required list of codewords. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011