Reviewerly/exHarmony huggingface dataset:
SWeG: Lossless and Lossy Summarization of Web-Scale Graphs

Given a terabyte-scale graph distributed across multiple machines, how can we summarize it, with much fewer nodes and edges, so that we can restore the original graph exactly or within error bounds?
Large Scale Online Multiple Kernel Regression with Application to Time-Series Prediction

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks as follows: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume that a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this work presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual/heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. We propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks including application to AR, ARMA, and ARIMA time series. We develop novel approaches to make OMKR scalable for large datasets, to counter the problems arising from an unbounded number of support vectors. We also explore the effect of kernel combination at prediction level and at the representation level. Finally, we conduct extensive experiments to evaluate the empirical performance on both real-world regression and times series prediction tasks.
To the attention of mobile software developers: guess what, test your app!

Software testing is an important phase in the software development lifecycle because it helps in identifying bugs in a software system before it is shipped into the hand of its end users. There are numerous studies on how developers test general-purpose software applications. The idiosyncrasies of mobile software applications, however, set mobile apps apart from general-purpose systems (e.g., desktop, stand-alone applications, web services). This paper investigates working habits and challenges of mobile software developers with respect to testing. A key finding of our exhaustive study, using 1000 Android apps, demonstrates that mobile apps are still tested in a very ad hoc way, if tested at all. However, we show that, as in other types of software, testing increases the quality of apps (demonstrated in user ratings and number of code issues). Furthermore, we find evidence that tests are essential when it comes to engaging the community to contribute to mobile open source software. We discuss reasons and potential directions to address our findings. Yet another relevant finding of our study is that Continuous Integration and Continuous Deployment (CI/CD) pipelines are rare in the mobile apps world (only 26% of the apps are developed in projects employing CI/CD) – we argue that one of the main reasons is due to the lack of exhaustive and automatic testing.
Fork-free hybrid consensus with flexible Proof-of-Activity

Bitcoin and its underlying blockchain mechanism have been attracting much attention. One of their core innovations, Proof-of-Work (PoW), is notoriously inefficient which potentially motivates a centralization of hash power, defeating the original goal of decentralization. Proof-of-Stake (PoS) is later proposed to replace PoW. However, both PoW and PoS have different inherent advantages and disadvantages, so does Proof-of-Activity (PoA) of Bentov et al. (SIGMETRICS 2014) which only offers limited hybrids of two mechanisms. On the other hand, the hybrid consensus protocol of Pass and Shi (DISC 2017) aims to improve the efficiency by dynamically maintaining a rotating committee. Yet, there are unsatisfactory issues including chain forks and fair committee election. In this paper, we firstly devise a generalized variant of PoW. After that, we leverage our generalized PoW to construct a fork-free hybrid consensus protocol. We further combine our fork-free hybrid consensus mechanism with PoS for a flexible version of PoA with tunable parameters between PoW and PoS. Compared with Bentov et al.’s PoA, our “flexible PoA” improves the efficiency, leading to a more applicable consensus protocol.
A Novel Projective-Consistent Plane Based Image Stitching Method

When different target surfaces, in three-dimensional space, are mapped onto an image plane, they have different projections. These projections vary with the viewpoint. These local differences have influence on the accuracy of image stitching. Most of the existing image stitching methods divide an input image into a number of fixed-size cells, and the pixels within the same cell are then warped using the same local transformation model for the alignment. These methods are based on the hypothesis that the transformation models in one cell are consistent. However, this hypothesis does not hold in general. In this paper, we propose a novel projective-consistent plane based image stitching method (termed PCPS). It divides the overlapping regions of an input image into some projective-consistent planes according to the normal vectors' orientations of local regions and the reprojection errors of aligned images. The local projective transformation model is estimated for each projective-consistent plane. And then, a hybrid warping model is estimated. For the pixels in overlapping regions, the local projective transformation models are adopted to achieve a better alignment. While for the pixels in non-overlapping regions, a global projective transformation model is estimated by using the inliers uniformly distributed in the projective-consistent planes to avoid distortion. Compared with the state-of-the-art image stitching methods, the experimental results on a number of challenging image sequences show that the projective transformation model estimated by the proposed PCPS method for each projective-consistent plane is more accurate, and the achieved stitching results have less seams and projective distortion.
Reference-Free Quality Assessment of Sonar Images via Contour Degradation Measurement

Sonar imagery plays a significant role in oceanic applications since there is little natural light underwater, and light is irrelevant to sonar imaging. Sonar images are very likely to be affected by various distortions during the process of transmission via the underwater acoustic channel for further analysis. At the receiving end, the reference image is unavailable due to the complex and changing underwater environment and our unfamiliarity with it. To the best of our knowledge, one of the important usages of sonar images is target recognition on the basis of contour information. The contour degradation degree for a sonar image is relevant to the distortions contained in it. To this end, we developed a new no-reference contour degradation measurement for perceiving the quality of sonar images. The sparsities of a series of transform coefficient matrices, which are descriptive of contour information, are first extracted as features from the frequency and spatial domains. The contour degradation degree for a sonar image is then measured by calculating the ratios of extracted features before and after filtering this sonar image. Finally, a bootstrap aggregating (bagging)-based support vector regression module is learned to capture the relationship between the contour degradation degree and the sonar image quality. The results of experiments validate that the proposed metric is competitive with the state-of-the-art reference-based quality metrics and outperforms the latest reference-free competitors.
Context Embedding Based on Bi-LSTM in Semi-Supervised Biomedical Word Sense Disambiguation

Word sense disambiguation (WSD) is a basic task of natural language processing (NLP) and its purpose to choose the correct sense of an ambiguous word according to its context. In biomedical WSD, recent research has used context embeddings built by concatenating or averaging word embeddings to represent the sense of a context. These simple linear operations on neighbor words ignore the information about the sequence and may cause their models to be flawed in semantic representation. In this paper, we present a novel language model based on Bi-LSTM to embed an entire sentential context in continuous space by taking account of word order. We demonstrate that our language model can generate high-quality context representations in an unsupervised manner. Unlike the previous work that directly predicts the word senses, our model classifies a word in a context by building sense embeddings and this helps us set a new state-of-the-art result (macro/micro average) on both MSH and NLM datasets. In addition, with the same language model, we propose semi-supervised learning based on label propagation (LP) to reduce the dependence on biomedical data. The results show that this method can nearly approach the state-of-the-art results produced by our Bi-LSTM when reducing the labeled training data.
Reducing the Multiplicative Complexity in Logic Networks for Cryptography and Security Applications

Reducing the number of AND gates plays a central role in many cryptography and security applications. We propose a logic synthesis algorithm and tool to minimize the number of AND gates in a logic network composed of AND, XOR, and inverter gates. Our approach is fully automatic and exploits cut enumeration algorithms to explore optimization potentials in local subcircuits. The experimental results show that our approach can reduce the number of AND gates by 34% on average compared to generic size optimization algorithms. Further, we are able to reduce the number of AND gates up to 76% in best-known benchmarks from the cryptography community.
Multiobjective feature selection for microarray data via distributed parallel algorithms

Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency.
A Novel Grey Wolf Optimizer Algorithm With Refraction Learning

Grey wolf optimizer (GWO) is a relatively new algorithm in the field of swarm intelligence for solving numerical optimization as well as real-world optimization problems.However, the paramount challenge in GWO is that it is prone to stagnation in local optima.The main goal of this paper is to improve the searchability of GWO when a new learning strategy is introduced in the algorithm.This new operator, called refraction learning, is essentially an opposite-learning strategy that is inspired by the principle of light refraction in physics.This proposed operator is applied to the current global optima of the swarm in the GWO algorithm and is beneficial to help the population for jumping out of the local optima.A novel variant of GWO called RL-GWO based on refraction learning is proposed.A theoretical proof of convergence is provided.We investigate the performance of RL-GWO using two sets of benchmark test functions, i.e., 23 widely used benchmark test functions, and 30 test functions from the IEEE CEC 2014.A non-parametric Wilcoxon's test is performed to observe the impact of improving the global optima in the algorithm.It is concluded that RL-GWO is an efficient, effective, and reliable algorithm for solving function optimization problems.
2.24-Tb/s Physical Random Bit Generation With Minimal Post-Processing Based on Chaotic Semiconductor Lasers Network

Multi-channel physical random bit generator (RBG) based on a chaotic semiconductor lasers (SLs) network is proposed and experimentally demonstrated. In experiments, a small ring network consisting of three SLs mutually coupled with heterogeneous delays is constructed to obtain chaotic temporal waveforms. The results show that the time-delay signature of chaotic intensities in all the three SLs can be well concealed over wide range of parameters. Through linear combination of the three chaotic outputs, seven channels of chaotic entropy sources can be obtained. Furthermore, by introducing a minimal post-processing technique and directly extracting four least significant bits at 80-GS/s sampling rate, seven channel random bit sequences with verified randomness can be simultaneously achieved, and the total bit rate is 2.24 Tb/s. The proposed RBGs enabling such high generation rate have potential applications in the one-time pad encryption for the high-speed optical communication networks.
The Effect of Using Synectics Model on Creative Thinking and Metacognition Skills of Junior High School Students

This study aims (a) to determine the effect of synectics model on creative thinking skill (b) to determine the effect of synectics model on metacognition skill.(c) to find out the relationship between creative thinking and metacognition skills.This study uses a quasi-experimental type with one group pretest posttest design.The study population was 612 junior high school students with a total sample of 242.The instruments used consisted of essay tests which were assessed using the rubric of creative thinking skills and questionnaires about metacognition skill.Data were analyzed based on parametric statistical calculations.The results showed that the synectics model had an effect on creative thinking and metacognition skills.The results of the correlation analysis show R of 0.873.Based on the regression line equation Y = 12.27 + 0.8847x, that every 1-point increase in the score of metacognition skill results in an increase of 0.8847 creative thinking skill.
What Do Programmers Discuss About Blockchain? A Case Study on the Use of Balanced LDA and the Reference Architecture of a Domain to Capture Online Discussions About Blockchain Platforms Across Stack Exchange Communities

Blockchain-related discussions have become increasingly prevalent in programming Q&A websites, such as Stack Overflow and other Stack Exchange communities. Analyzing and understanding those discussions could provide insights about the topics of interest to practitioners, and help the software development and research communities better understand the needs and challenges facing developers as they work in this new domain. Prior studies propose the use of LDA to study the Stack Exchange discussions. However, a simplistic use of LDA would capture the topics in discussions blindly without keeping in mind the variety of the dataset and domain-specific concepts. Specifically, LDA is biased towards larger sized corpora; and LDA-derived topics are not linked to higher level domain-specific concepts. We propose an approach that combines balanced LDA (which ensures that the topics are balanced across a domain) with the reference architecture of a domain to capture and compare the popularity and impact of discussion topics across the Stack Exchange communities. Popularity measures the distribution of interest in discussions, and impact gauges the trend of popularity over time. We made a number of interesting observations, including: (1) Bitcoin, Ethereum, Hyperledger Fabric and Corda are the four most commonly-discussed blockchain platforms on the Stack Exchange communities. (2) A broad range of topics are discussed across the various platforms of distinct layers in our derived reference architecture. (3) The Application layer topics exhibit the highest popularity (33.2 percent) and fastest growth in topic impact since November 2015. (4) The Application, API, Consensus and Network layer topics are discussed across the studied blockchain platforms, but exhibit different distributions in popularity. (5) The impact of architectural layer topics exhibits an upward trend, but is growing at different speeds across the studied blockchain platforms. The breakdown of the topic impact across the architectural layers is relatively stable over time except for the Hyperledger Fabric platform. Based on our findings, we highlighted future directions and provided recommendations for practitioners and researchers.
Storyboarding of Recipes: Grounded Contextual Generation

Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context.We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain.The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps.We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task.In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL).Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model.We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes.We also discuss analysis of the output highlighting key important NLP issues for prospective directions.
Supersolid-Based Gravimeter in a Ring Cavity

We propose a novel type of composite light-matter interferometer based on a supersolidlike phase of a driven Bose-Einstein condensate coupled to a pair of degenerate counterpropagating electromagnetic modes of an optical ring cavity. The supersolidlike condensate under the influence of the gravity drags the cavity optical potential with itself, thereby changing the relative phase of the two cavity electromagnetic fields. Monitoring the phase evolution of the cavity output fields thus allows for a nondestructive measurement of the gravitational acceleration. We show that the sensitivity of the proposed gravimeter exhibits Heisenberg-like scaling with respect to the atom number. As the relative phase of the cavity fields is insensitive to photon losses, the gravimeter is robust against these deleterious effects. For state-of-the-art experimental parameters, the relative sensitivity $\mathrm{\ensuremath{\Delta}}g/g$ of such a gravimeter could be of the order of ${10}^{\ensuremath{-}10}\ensuremath{-}{10}^{\ensuremath{-}8}$ for a condensate of a half a million atoms and interrogation time of the order of a few seconds.
NTIRE 2019 Challenge on Video Super-Resolution: Methods and Results

This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in low-resolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.
Bounding the average gate fidelity of composite channels using the unitarity

Abstract There is currently a significant need for robust and efficient methods for characterizing quantum devices. While there has been significant progress in this direction, there remains a crucial need to precisely determine the strength and type of errors on individual gate operations, in order to assess and improve control as well as reliably bound the total error in a quantum circuit given some partial information about the errors on the components. In this work, we first provide an optimal bound on the total fidelity of a circuit in terms of component fidelities, which can be efficiently experimentally estimated via randomized benchmarking (RB). We then derive a tighter bound that applies under additional information about the coherence of the error, namely, the unitarity, which can also be estimated via a related experimental protocol. This improved bound smoothly interpolates between the worst-case quadratic and best-case linear scaling for composite error channels. As an application we show how our analysis substantially improves the achievable precision on estimates of the infidelities of individual gates under interleaved RB, enabling greater precision for current experimental methods to assess and tune-up control over quantum gate operations.
On the Complexity and Typology of Inflectional Morphological Systems

We quantify the linguistic complexity of different languages’ morphological systems. We verify that there is a statistically significant empirical trade-off between paradigm size and irregularity: A language’s inflectional paradigms may be either large in size or highly irregular, but never both. We define a new measure of paradigm irregularity based on the conditional entropy of the surface realization of a paradigm— how hard it is to jointly predict all the word forms in a paradigm from the lemma. We estimate irregularity by training a predictive model. Our measurements are taken on large morphological paradigms from 36 typologically diverse languages.
Hybrid Touch/Tangible Spatial 3D Data Selection

Abstract We discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.