Reviewerly/exHarmony huggingface dataset:
Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation

Graph pattern cardinality estimation is the problem of estimating the number of embeddings of a query graph in a data graph. This fundamental problem arises, for example, during query planning in subgraph matching algorithms. There are two major approaches to solving the problem: sampling and synopsis. Synopsis (or summary)-based methods are fast and accurate if synopses capture information of graphs well. However, these methods suffer from large errors due to loss of information during summarization and inherent assumptions. Sampling-based methods are unbiased but suffer from large estimation variance due to large sample space. To address these limitations, we propose Alley, a hybrid method that combines both sampling and synopses. Alley employs 1) a novel sampling strategy, random walk with intersection, which effectively reduces the sample space, 2) branching to further reduce variance, and 3) a novel mining approach that extracts and indexes tangled patterns as synopses which are inherently difficult to estimate by sampling. By using them in the online estimation phase, we can effectively reduce the sample space while still ensuring unbiasedness. We establish that Alley has worst-case optimal runtime and approximation quality guarantees for any given error bound ε and required confidence μ. In addition to the theoretical aspect of Alley, our extensive experiments show that Alley outperforms the state-of-the-art methods by up to orders of magnitude higher accuracy with similar efficiency.
Recognition of Emotion from Emoticon with Text in Microblog Using LSTM

With the advent of internet technology and social media, patterns of social communication in daily lives have changed whereby people use different social networking platforms. Microblog is a new platform for sharing opinions by means of emblematic expressions, which has become a resource for research on emotion analysis. Recognition of emotion from microblogs (REM) is an emerging research area in machine learning as the graphical emotional icons, known as emoticons, are becoming widespread with texts in microblogs.Studies hitherto have ignored emoticons for REM, which led to the current study where emoticons are translated into relevant emotional words and a REM method is proposed preserving the semantic relationship between texts and emoticons.The recognition is implemented using a Long-Short-Term Memory (LSTM) for the classification of emotions.The proposed REM method is verified on Twitter data and the recognition performances are compared with existing methods.The higher recognition accuracy unveils the potential of the emoticon-based REM for Microblogs applications.
Seamless Language Learning in an ESL Context

Amidst the vigorous development of technology in the Malaysian education scene, the emergence of seamless learning is steadily fitting into the Fourth Industrial Revolution (4IR) teaching and learning settings, particularly in English as a Second Language (ESL) context.At the moment, seamless learning is paving its way for a more effective ESL practice.Seamless learning has the potential to revolutionize teaching and learning by enriching the opportunity of continuous learning.Seamless learning offers the ability for learners to learn whenever they are triggered to especially with the aid of technological tools such as mobile devices.The implementation of technology has continuously shown its advantages to both students and educators.Thus, there is a strong assumption for the concept of seamless learning to be able to produce similar outcomes.This conceptual paper will discuss a few issues revolving the theme of seamless learning for ESL learning which include seamless learning theories followed by the effectiveness of implementing technology for an ESL seamless learning experience.The paper continues and ends with the discussion of seamless learning for ESL learning beyond the classroom.The discussion undertakes a review of past literature in the context of ESL from recent years.This paper will help in providing a clear understanding of seamless learning as well as highlighting the potentials of creating ESL learning beyond the classroom.
A Semantic Relation Graph Reasoning Network for Object Detection

Object detection is a basic task in computer vision, and it plays an important role in the fields of robotics, security, and autonomous driving. However, the object detection algorithms at present usually extract the features of a single region and then perform detection, ignoring the semantic context between objects and scenes, which will produce bad effect in detection. In order to use the semantic context between objects and scenes, this paper considers object detection as a graph reasoning problem. In this paper, we obtain the prior knowledge of the co-occurrence among the objects and between objects and scenes through statistics of the dataset, then we mainly use two modules to extract the semantic relationship between objects and scenes. The first one extracts the prior knowledge between objects through of graph convolutional networks(GCN), and introduces the graph attention networks(GAT) to learn hidden knowledge about the semantic context relationship between objects adaptively, and by concating these knowledge then use them for detection. The second one uses MLP to generate S-L coefficients and multiplies the scene features and the S-L coefficients to obtain scene-object related features for object detection. We have conducted experiments to verify our method on the PASCAL VOC dataset, and the experiments show that our method can effectively improve the accuracy of object detection.
Opinion Mining about Online Education Basing on GDELT and Twitter Data

This public health incident transformed teaching activities from offline to online. The media content and comments on social media provide a dataset for digging the public opinion on online learning. This study uses the GDELT and TWITTER platforms' data, searching “COVID” and “online education” as keywords; the relevant information is collected and analyzed in python. The results of this public opinion mining will play an essential role in discovering the problem of online teaching in this pandemic.
Category Theory Framework for Variability Models with Non-functional Requirements

In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation

AutoAugment [4] has sparked an interest in automated augmentation methods for deep learning models. These methods estimate image transformation policies for train data that improve generalization to test data. While recent papers evolved in the direction of decreasing policy search complexity, we show that those methods are not robust when applied to biased and noisy data. To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset. In our AutoDO model, we explicitly estimate a set of per-point hyperparameters to flexibly change distribution of train data. In particular, we include hyperparameters for augmentation, loss weights, and softlabels that are jointly estimated using implicit differentiation. We develop a theoretical probabilistic interpretation of this framework using Fisher information and show that its complexity scales linearly with the dataset size. Our experiments on SVHN, CIFAR-10/100, and ImageNet classification show up to 9.3% improvement for biased datasets with label noise compared to prior methods and, importantly, up to 36.6% gain for underrepresented SVHN classes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Alignment Completeness for Relational Hoare Logics

Relational Hoare logics (RHL) provide rules for reasoning about relations between programs. Several RHLs include a rule we call sequential product that infers a relational correctness judgment from judgments of ordinary Hoare logic (HL). Other rules embody sensible patterns of reasoning and have been found useful in practice, but sequential product is relatively complete on its own (with HL). As a more satisfactory way to evaluate RHLs, a notion of alignment completeness is introduced, in terms of the inductive assertion method and product automata. Alignment completeness results are given to account for several different sets of rules. The notion may serve to guide the design of RHLs and relational verifiers for richer programming languages and alignment patterns.
Efficient Detection of Multilingual Hate Speech by Using Interactive Attention Network with Minimal Human Feedback

Online hate speech on social media has become a critical problem for social network services that has been further fueled by the self-isolation in the COVID-2019 pandemic. Current studies have primarily focused on detecting hate speech in one language due to the complexity of the task; however, hate speech has no boundaries across the languages and geographies in the real world nowadays. This demands further investigation on multilingual hate speech detection methods, with strong requirements for model interpretability to effectively understand the context of the model errors. In this paper, we propose a Multilingual Interactive Attention Network (MLIAN) model for hate speech detection on multilingual social media text corpora, by building upon the attention networks for interpretability and human-in-the-loop paradigm for model adaptability. This model interactively learns to give attention to the relevant contextual words and leverage the labels for the hate target mentions from the simulated human feedback. We evaluated the proposed model on SemEval-2019 Task 5 datasets in English and Spanish. Extensive experimentation of model training in both settings of single and multiple language data demonstrates the superior performance of our model (with AUC more than 84%) compared to the strong baselines. Our results show that human feedback not only improves the model performance but also helps to improve the interpretability of the model by establishing a strong connection between the learned attention weights and semantic frames for the text across languages. Further, an analysis of the amount of human feedback required to achieve reliable and increased model performance shows that less than 4% of training data is sufficient. The application of the MLIAN method can inform future studies on multilingual hate speech.
Comparison of RNN and Embeddings Methods for Next-item and Last-basket Session-based Recommendations

Recurrent Neural Networks (RNNs) have been shown to perform very effectively in session-based recommendation settings, when compared to other commonly used methods that consider the last viewed item of the user and precomputed item-to-item similarities. However, there is little systematic study on how RNNs perform in comparison to methods that use embeddings for item representation for Collaborative Filtering. In this paper we use two industry datasets to compare RNNs with other sequential recommenders that use various embedding methods to represent items. The first dataset corresponds to a typical e-commerce session-based scenario demanding effective next-item recommendation. The second dataset represents a last-basket prediction setting. Results show that although the RNN greatly outperforms embedding methods in the next-item scenario, the dynamic nature of the RNNs has not the same positive impact in the last-basket prediction task. We also present and test a framework that enables the hybrid utilization of text content and item sequences using embeddings. Finally, we report on experiments with reranking methods that demonstrate the effectiveness of simple and practical methods, using item categories, to improve the results.
Human-level Ordinal Maintainability Prediction Based on Static Code Metrics

One of the greatest challenges in software quality control is the efficient and effective measurement of maintainability. Thorough expert assessments are precise yet slow and expensive, whereas automated static analysis yields imprecise yet rapid feedback. Several machine learning approaches aim to integrate the advantages of both concepts.
Convergence rate of the (1+1)-evolution strategy with success-based step-size adaptation on convex quadratic functions

The (1+1)-evolution strategy (ES) with success-based step-size adaptation is analyzed on a general convex quadratic function and its monotone transformation, that is, f(x) = g((x - x*)TH(x - x*)), where g: R → R is a strictly increasing function, H is a positive-definite symmetric matrix, and x* ∈ Rd is the optimal solution of f. The convergence rate, that is, the decrease rate of the distance from a search point mt to the optimal solution x*, is proven to be in O(exp(-L/Tr(H))), where L is the smallest eigenvalue of H and Tr(H) is the trace of H. This result generalizes the known rate of O(exp(-1/d)) for the case of H = Id (Id is the identity matrix of dimension d) and O(exp(-1/(d · ξ))) for the case of H = diag(ξ · Id/2, Id/2). To the best of our knowledge, this is the first study in which the convergence rate of the (1+1)-ES is derived explicitly and rigorously on a general convex quadratic function, which depicts the impact of the distribution of the eigenvalues in the Hessian H on the optimization and not only the impact of the condition number of H.
Motivating High Performance Serverless Workloads

The historical motivation for serverless comes from internet-of-things, smartphone client server, and the objective of simplifying programming (no provisioning) and scale-down (pay-for-use). These applications are generally low-performance best-effort. However, the serverless model enables flexible software architectures suitable for a wide range of applications that demand high-performance and guaranteed performance. We have studied three such applications - scientific data streaming, virtual/augmented reality, and document annotation. We describe how each can be cast in a serverless software architecture and how the application performance requirements translate into high performance requirements (invocation rate, low and predictable latency) for the underlying serverless system implementation. These applications can require invocations rates as high as millions per second (40 MHz) and latency deadlines below a microsecond (300 ns), and furthermore require performance predictability. All of these capabilities are far in excess of today's commercial serverless offerings and represent interesting research challenges.
Learning from Physics Experiments with Quantum Computers: Applications in Muon Spectroscopy

Computational physics is an important tool for analysing, verifying, and -- at times -- replacing physical experiments. Nevertheless, simulating quantum systems and analysing quantum data has so far resisted an efficient classical treatment in full generality. While programmable quantum systems have been developed to address this challenge, the resources required for classically intractable problems still lie beyond our reach. In this work, we consider a new target for quantum simulation algorithms; analysing the data arising from physics experiments -- specifically, muon spectroscopy experiments. These experiments can be used to probe the quantum interactions present in condensed matter systems. However, fully analysing their results can require classical computational resources scaling exponentially with the simulated system size, which can limit our understanding of the studied system. We show that this task may be a natural fit for the coming generations of quantum computers. We use classical emulations of our quantum algorithm on systems of up to 29 qubits to analyse real experimental data, and to estimate both the near-term and error corrected resources required for our proposal. We find that our algorithm exhibits good noise resilience, stemming from our desire to extract global parameters from a fitted curve, rather than targeting any individual data point. In some respects, our resource estimates go further than some prior work in quantum simulation, by estimating the resources required to solve a complete task, rather than just to run a given circuit. Taking the overhead of observable measurement and calculating multiple datapoints into account, we find that significant challenges still remain if our algorithm is to become practical for analysing muon spectroscopy data.
Swimmer Stroke Rate Estimation from Overhead Race Video

In this work, we propose a swimming analytics system for automatically determining swimmer stroke rates from overhead race video (ORV). General ORV is defined as any footage of swimmers in competition, taken for the purposes of viewing or analysis. Examples of this are footage from live streams, broadcasts, or specialized camera equipment, with or without camera motion. These are the most typical forms of swimming competition footage. We detail how to create a system that will automatically collect swimmer stroke rates in any competition, given the video of the competition of interest. With this information, better systems can be created and additions to our analytics system can be proposed to automatically extract other swimming metrics of interest.
Early Prediction of Electronics Engineering Licensure Examination Performance using Random Forest

Graduate's success on licensure examinations has a significant impact on various facets of a higher educational institution. Using a comprehensive data mining process, this research compared the accuracy of multiple classification algorithms to determine predictors of students' professional certification performance. The Random Forest model achieved the best cross-validated accuracy score of 92.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> based on the evaluation data. A model inspection method of permutation feature importance was used to uncover information from 500 graduates of Southern Luzon State University's electronics engineering program from 2014 to 2019. Among the 33 variables examined, the verbal reasoning or reading comprehension ability of students unveils a clear attribution with their licensure test results along with ratings from different courses in mathematics, professional, and electrical circuits. Thus, the data-driven information can be used to develop programs, initiatives, and techniques to improve success on the electronics engineering licensure examinations.
CENTIME: A Direct Comprehensive Traffic Features Extraction for Encrypted Traffic Classification

With the rapid development of the network, encrypted traffic classification plays a vital role in guaranteeing the quality of network services and ensuring the security of the network. Recent studies show that machine learning approaches based on statistical features and raw traffic sessions are effective for this task. However, the performance of the statistical-based approaches largely depends on the quality of the features. Experts need to design different features for different encrypted traffic classification tasks, which is time-consuming. Meanwhile, the raw traffic-based approach needs to uniformize the traffic size; this will cause the loss of information about the overall structure of the network traffic; for example, we do not know the time from the first packet to the last packet in a session. This paper proposes the CENTIME, which can extract comprehensive information based on ResNet and AutoEncoder to identify encrypted traffic. ResNet is used to extract information from uniformized traffic, and AutoEncoder is used to encode statistical features. The statistical features are used to compensate for the information loss caused by traffic uniformization. They only need to be designed once rather than be designed separately for different tasks. Moreover, the pooling layers are removed, and 1D convolution layers are used to help CENTIME make more effective use of raw traffic information. We evaluate the CENTIME on the public dataset “ISCX VPN-nonVPN”, and the results demonstrate the CENTIME outperforms the state-of-the-art encrypted traffic classification methods. More importantly, comprehensive traffic features generated in the CENTIME can represent different classes of traffic well.
Are Mobile DNN Accelerators Accelerating DNNs?

Deep neural networks (DNNs) are running on many mobile and embedded devices with the goal of energy efficiency and highest possible performance. However, DNN workloads are getting more computationally intensive, and simultaneously their deployment is ever-increasing. This has led to the creation of many purpose-built low-power neural accelerators to replace or augment traditional mobile CPUs and GPUs. In this work, we provide an in-depth study of one set of commercially-available mobile accelerators, the Intel Neural Compute Sticks (NCS). We perform a systematic measurement study of the latency and energy of this accelerator under a variety of DNNs including convolutional neural networks (CNNs) for vision tasks and attention-based Transformer models for NLP tasks. We compare to the mobile processors (CPU, GPU, and DSP) on a smartphone and a mobile board. Our study shows commercial mobile accelerators like NCS are not ready yet to provide the performance as claimed. We also point out directions in optimizing the model architectures to better suit these accelerators.
Analysis of Smart Contract Abstraction in Decentralized Blockchain Based Stock Exchange

Stock markets have a centralized structure that has a number of intermediaries and operational trade policies contributing to high transaction times. Blockchain has the capability to optimize market transactions using automation with high security to create a peer-to-peer trading environment. It reduces operational risk by enabling transparency, certitude and interoperability in fragmented market systems to eliminate the need for third party regulators to a large extent. In this paper, a decentralized stock exchange system is implemented with Distributed Ledger Technology (DLT) on Ethereum for executing trades by separating concerns into three different smart contracts: buyer, seller and exchange. The self-enforcing smart contracts used are highly flexible and optimized for parallel operation due to functional abstraction. The multi-contract model is compared to a single contract model, which handles all three aspects within the same contract, by executing sample trading data from NASDAQ. Transaction fees for the miner at 161 Gwei is 27.96% lesser for the single-contract system and 98.75% lesser for the multi-contract system than the brokerage fees of traditional traders for the same transactions. Experimental results indicate that the separation of concern results in transaction costs being 98.26% lower and transaction time being 28.70% lower than having a single contract.