Reviewerly/exHarmony huggingface dataset:
Incremental Missing-Data Imputation for Evolving Fuzzy Granular Prediction

Missing values are common in real-world data stream applications. This article proposes a modified evolving granular fuzzy-rule-based model for function approximation and time-series prediction in an online context, where values may be missing. The fuzzy model is equipped with an incremental learning algorithm that simultaneously imputes missing data and adapts model parameters and structure over time. The evolving fuzzy granular predictor (eFGP) handles single and multiple missing values on data samples by developing reduced-term consequent polynomials and utilizing time-varying granules. Missing at random (MAR) and missing completely at random (MCAR) values in nonstationary data streams are approached. Experiments to predict monthly weather conditions, the number of bikes hired on a daily basis, and the sound pressure on an airfoil from incomplete data streams show the usefulness of eFGP models. Results were compared with those of state-of-the-art fuzzy and neuro-fuzzy evolving modeling methods. A statistical hypothesis test shows that eFGP outperforms other evolving intelligent methods in online MAR and MCAR settings, regardless of the application.
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited

Transition-based and graph-based dependency parsers have previously been shown to have complementary strengths and weaknesses: transition-based parsers exploit rich structural features but suffer from error propagation, while graph-based parsers benefit from global optimization but have restricted feature scope.In this paper, we show that, even though some details of the picture have changed after the switch to neural networks and continuous representations, the basic trade-off between rich features and global optimization remains essentially the same.Moreover, we show that deep contextualized word embeddings, which allow parsers to pack information about global sentence structure into local feature representations, benefit transition-based parsers more than graph-based parsers, making the two approaches virtually equivalent in terms of both accuracy and error profile.We argue that the reason is that these representations help prevent search errors and thereby allow transitionbased parsers to better exploit their inherent strength of making accurate local decisions.We support this explanation by an error analysis of parsing experiments on 13 languages.* We
Automatic Argument Quality Assessment - New Datasets and Methods

We explore the task of automatic assessment of argument quality.To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data.Each argument was explicitly and carefully annotated for its quality.In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair.In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.We release the labeled datasets to the community.Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification.In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.
An Improved Neural Baseline for Temporal Relation Extraction

Determining temporal relations (e.g., before or after) between events has been a challenging natural language understanding task, partly due to the difficulty to generate large amounts of high-quality training data.Consequently, neural approaches have not been widely used on it, or showed only moderate improvements.This paper proposes a new neural system that achieves about 10% absolute improvement in accuracy over the previous best system (25% error reduction) on two benchmark datasets.The proposed system is trained on the stateof-the-art MATRES dataset and applies contextualized word embeddings, a Siamese encoder of a temporal common sense knowledge base, and global inference via integer linear programming (ILP).We suggest that the new approach could serve as a strong baseline for future research in this area.
The impact of entrepreneurial leadership and learning orientation on organizational performance of SMEs: The mediating role of innovation capacity

Kuwait's SMEs contribute to the economic growth of the country by creating productive jobs for Kuwaiti youth and professionals and improving income diversification.SMEs in Kuwait are faced with numerous challenges and these problems must be addressed to achieve and ensure longer-term success and better performance.This paper seeks to examine the relationship between the determinants of organizational performance such as entrepreneurial leadership (EL), learning orientation (LO), and innovation capacity (IC) of Kuwaiti's Small and Medium Enterprises (SMEs).The study used a quantitative method.A survey questionnaire was administered to gather the data, where the useable data was obtained from 384 of the 500 SME owners and CEOs in Kuwait.The outcomes of this study indicate that entrepreneurial leadership and learning orientation had positive and significant implication on organizational performance.Moreover, the innovation capacity is also found to be a significant mediator in the relationships.
Decentralized Construction of Knowledge Graphs for Deep Recommender Systems Based on Blockchain-Powered Smart Contracts

Since first coined by Google in 2012, knowledge graph has received extensive attention from both industry and academia, and has been widely used in many scenarios with success, e.g.information retrieval, online recommendation, question-answering, and so on.However, traditional centralized construction of knowledge graph faces many challenges, such as laborious and time-consuming, vulnerable to manipulation or tampering, lacking scrutiny, among others.Therefore, in this paper, we propose a novel decentralized knowledge graph construction method by means of crowdsourcing, and the business logic of crowdsourcing is implemented by blockchain-powered smart contracts to guarantee the transparency, integrity, and auditability.On this basis, the decentralized knowledge graph is used for a deep recommender system, and case studies validate the effectiveness of the system.This paper is aimed at providing a novel decentralized approach for constructing knowledge graph and serving as reference and guidance for future research and practical applications of knowledge graph.
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification

Aspect-level sentiment classification is a crucial task for sentiment analysis, which aims to identify the sentiment polarities of specific targets in their context.The main challenge comes from multi-aspect sentences, which express multiple sentiment polarities towards different targets, resulting in overlapped feature representation.However, most existing neural models tend to utilize static pooling operation or attention mechanism to identify sentimental words, which therefore insufficient for dealing with overlapped features.To solve this problem, we propose to utilize capsule network to construct vector-based feature representation and cluster features by an EM routing algorithm.Furthermore, interactive attention mechanism is introduced in the capsule routing procedure to model the semantic relationship between aspect terms and context.The iterative routing also enables encoding sentence from a global perspective.Experimental results on three datasets show that our proposed model achieves state-of-the-art performance.
Roadmap on material-function mapping for photonic-electronic hybrid neural networks

The state-of-the-art hardware in artificial neural networks is still affected by the same capacitive challenges known from electronic integrated circuits. Unlike other emerging electronic technologies, photonics provides low-delay interconnectivity suitable for node-distributed non-von Neumann architectures, relying on dense node-to-node communication. Here, we provide a roadmap to pave the way for emerging hybridized photonic-electronic neural networks by taking a detailed look into a single node perceptron. We discuss how it can be realized in hybrid photonic-electronic heterogeneous technologies. Furthermore, we assess that electro-optic devices based on phase change or strong carrier dispersive effects could provide a viable path for both the perceptron “weights” and the nonlinear activation function in trained neural networks, while simultaneously being foundry process-near materials. This study also assesses the advantages of using nonlinear optical materials as efficient and instantaneous activation functions. We finally identify several challenges that, if solved, could accelerate the adoption of such heterogeneous integration strategies of emerging memory materials into integrated photonics platforms for near real-time responsive neural networks.
Hyper-Pairing Network for Multi-phase Pancreatic Ductal Adenocarcinoma Segmentation

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with an overall five-year survival rate of 8%. Due to subtle texture changes of PDAC, pancreatic dual-phase imaging is recommended for better diagnosis of pancreatic disease. In this study, we aim at enhancing PDAC automatic segmentation by integrating multi-phase information (i.e., arterial phase and venous phase). To this end, we present Hyper-Pairing Network (HPN), a 3D fully convolution neural network which effectively integrates information from different phases. The proposed approach consists of a dual path network where the two parallel streams are interconnected with hyper-connections for intensive information exchange. Additionally, a pairing loss is added to encourage the commonality between high-level feature representations of different phases. Compared to prior arts which use single phase data, HPN reports a significant improvement up to 7.73% (from 56.21% to 63.94%) in terms of DSC.
AIoT Bench: Towards Comprehensive Benchmarking Mobile and Embedded Device Intelligence

Due to increasing amounts of data and compute resources, the deep learning achieves many successes in various domains. Recently, researchers and engineers make effort to apply the intelligent algorithms to the mobile or embedded devices. In this paper, we propose a benchmark suite, AIoT Bench, to evaluate the AI ability of mobile and embedded devices. Our benchmark (1) covers different application domains, e.g. image recognition, speech recognition and natural language processing; (2) covers different platforms, including Android and Raspberry Pi; (3) covers different development frameworks, including TensorFlow and Caffe2; (4) offers both end-to-end application workloads and micro workloads.
Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation

We propose Mask SSD, an efficient and effective approach to address the challenging instance segmentation task. Based on a single-shot detector, Mask SSD detects all instances in an image and marks the pixels that belong to each instance. It consists of a detection subnetwork that predicts object categories and bounding box locations, and an instance-level segmentation subnetwork that generates the foreground mask for each instance. In the detection subnetwork, multi-scale and feedback features from different layers are used to better represent objects of various sizes and provide high-level semantic information. Then, we adopt an assistant classification network to guide per-class score prediction, which consists of objectness prior and category likelihood. The instance-level segmentation subnetwork outputs pixel-wise segmentation for each detection while providing the multi-scale and feedback features from different layers as input. These two subnetworks are jointly optimized by a multi-task loss function, which renders Mask SSD direct prediction on detection and segmentation results. We conduct extensive experiments on PASCAL VOC, SBD, and MS COCO datasets to evaluate the performance of Mask SSD. Experimental results verify that as compared with state-of-the-art approaches, our proposed method has a comparable precision with less speed overhead.
Software-Defined Infrastructure for Decentralized Data Lifecycle Governance: Principled Design and Open Challenges

Exploring and mining the explosive burst of "big data" has already generated a lot of innovative applications, especially the recent advances of AI applications, and thus produced big values to the human society and civilization. However, due to the centralized patterns of data governance activities, including creation, sharing, exchange, management, analytics, tracing, and accounting, the potential values of big data distributed on the Internet are far away from being adequately explored. The recent announcement of data protection policies/laws such as GDPR makes the problem even more challenging. We are now at a moment of truth where the data governance infrastructure should be reconsidered and redesigned. In this paper, we propose a software-defined infrastructure design in a decentralized fashion: data owners are able to implement and deploy their own rules to the application systems where the data are produced for further governance activities. Such a fashion is quite similar to the popular software-defined networking where users are allowed to deploy rules of switches and customize the use. Our principled infrastructure design can radically reform the current data governance activities into a decentralized topology. On the one hand, data can be separated from the application that generates the data, and data owners can have the full rights to decide where their data should be stored and how the data can be shared. On the other hand, data users can search, discover, integrate, and analyze the data from various data sources according to their application requirements and scenarios. As a result, we argue that our infrastructure can establish a new generation of responsive decentralized data governance that can promote the innovation of linking data to better adapt the open environment and diverse user requirements. With this perspective, we briefly discuss some key insights and enumerate several related new technologies and open challenges.
Learnability and semantic universals

One of the great successes of the application of generalized quantifiers to natural language has been the ability to formulate robust semantic universals. When such a universal is attested, the question arises as to the source of the universal. In this paper, we explore the hypothesis that many semantic universals arise because expressions satisfying the universal are easier to learn than those that do not. While the idea that learnability explains universals is not new, explicit accounts of learning that can make good on this hypothesis are few and far between. We propose a model of learning — back-propagation through a recurrent neural network — which can make good on this promise. In particular, we discuss the universals of monotonicity, quantity, and conservativity and perform computational experiments of training such a network to learn to verify quantifiers. Our results are able to explain monotonicity and quantity quite well. We suggest that conservativity may have a different source than the other universals. BibTeX info
Efficient processing of GRU based on word embedding for text classification

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM,Â the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate.
Operational framework for recent advances in backtracking search optimisation algorithm: A systematic review and performance evaluation

Backtracking search optimisation algorithm (BSA) is a commonly used meta-heuristic optimisation algorithm and was proposed by Civicioglu in 2013. When it was first used, it exhibited its strong potential for solving numerical optimisation problems. Additionally, the experiments conducted in previous studies demonstrated the successful performance of BSA and its non-sensitivity toward the several types of optimisation problems. This success of BSA motivated researchers to work on expanding it, e.g., developing its improved versions or employing it for different applications and problem domains. However, there is a lack of literature review on BSA; therefore, reviewing the aforementioned modifications and applications systematically will aid further development of the algorithm. This paper provides a systematic review and meta-analysis that emphasise on reviewing the related studies and recent developments on BSA. Hence, the objectives of this work are two-fold: (i) First, two frameworks for depicting the main extensions and the uses of BSA are proposed. The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it. (ii) Second, the experiments conducted in this study fairly compare the analytical performance of BSA with four other competitive algorithms: differential evolution (DE), particle swarm optimisation (PSO), artificial bee colony (ABC), and firefly (FF) on 16 different hardness scores of the benchmark functions with different initial control parameters such as problem dimensions and search space. The experimental results indicate that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces. This study can act as a systematic and meta-analysis guide for the scholars who are working on improving BSA.
High-resolution real-time 360° 3D model reconstruction of a handheld object with fringe projection profilometry

The digitization of the complete shape of real objects has essential applications in fields of intelligent manufacturing, industrial detection, and reverse modeling. In order to build the full geometric models of rigid objects, the object must be moved relative to the measurement system (or the scanner must be moved relative to the object) to obtain and integrate views of the object from all sides, which not only complicates the system configuration but makes the whole process time-consuming. In this Letter, we present a high-resolution real-time 360&#x00B0; three-dimensional (3D) model reconstruction method that allows one to rotate an object manually and see a continuously updated 3D model during the scanning process. A multi-view fringe projection profilometry system acquires high-precision depth information about a handheld object from different perspectives and, meanwhile, the multiple views are aligned and merged together in real time. Our system employs stereo phase unwrapping and an adaptive depth constraint that can unwrap the phase of dense fringe images robustly without increasing the number of captured patterns. We then develop an efficient coarse-to-fine registration strategy to match the 3D surface segments rapidly. Our experiments demonstrate that our method can reconstruct the high-precision complete 3D model of complex objects under arbitrary rotation without any instrument assist and expensive pre/post-processing.
The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems

Machine learning and artificial intelligence algorithms can assist human decision making and analysis tasks. While such technology shows promise, willingness to use and rely on intelligent systems may depend on whether people can trust and understand them. To address this issue, researchers have explored the use of explainable interfaces that attempt to help explain why or how a system produced the output for a given input. However, the effects of meaningful and meaningless explanations (determined by their alignment with human logic) are not properly understood, especially with users who are non-experts in data science. Additionally, we wanted to explore how explanation inclusion and level of meaningfulness would affect the user’s perception of accuracy. We designed a controlled experiment using an image classification scenario with local explanations to evaluate and better understand these issues. Our results show that whether explanations are human-meaningful can significantly affect perception of a system’s accuracy independent of the actual accuracy observed from system usage. Participants significantly underestimated the system’s accuracy when it provided weak, less human-meaningful explanations. Therefore, for intelligent systems with explainable interfaces, this research demonstrates that users are less likely to accurately judge the accuracy of algorithms that do not operate based on human-understandable rationale.
Benchmarking discrete optimization heuristics with IOHprofiler

Automated benchmarking environments aim to support researchers in understanding how different algorithms perform on different types of optimization problems. Such comparisons provide insights into the strengths and weaknesses of different approaches, which can be leveraged into designing new algorithms and into the automation of algorithm selection and configuration. With the ultimate goal to create a meaningful benchmark set for iterative optimization heuristics, we have recently released IOHprofiler, a software built to create detailed performance comparisons between iterative optimization heuristics. With this present work we demonstrate that IOHprofiler provides a suitable environment for automated benchmarking. We compile and assess a selection of 23 discrete optimization problems that subscribe to different types of fitness landscapes. For each selected problem we compare performances of twelve different heuristics, which are as of now available as baseline algorithms in IOHprofiler. We also provide a new module for IOHprofiler which extents the fixed-target and fixed-budget results for the individual problems by ECDF results, which allows one to derive aggregated performance statistics for groups of problems.