Reviewerly/exHarmony huggingface dataset:
Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them.It divides the joint extraction into two sub-tasks, first detecting entity spans and identifying entity relations type simultaneously.To consider the complete interaction between entities and relations, we propose a novel relation-aware attention mechanism to obtain the relation representation between two entity spans.Therefore, a complete graph is constructed based on all extracted entity spans where the nodes are entity spans and the edges are relation representation.Besides, we improve original GCN to utilize both adjacent node features and edge information when encoding node feature.Experiments are conducted on two public datasets and our model outperforms all baseline methods.
Personalized Employee Training Course Recommendation with Career Development Awareness

As a major component of strategic talent management, learning and development (L&D) aims at improving the individual and organization performances through planning tailored training for employees to increase and improve their skills and knowledge. While many companies have developed the learning management systems (LMSs) for facilitating the online training of employees, a long-standing important issue is how to achieve personalized training recommendations with the consideration of their needs for future career development. To this end, in this paper, we propose an explainable personalized online course recommender system for enhancing employee training and development. A unique perspective of our system is to jointly model both the employees’ current competencies and their career development preferences in an explainable way. Specifically, the recommender system is based on a novel end-to-end hierarchical framework, namely Demand-aware Collaborative Bayesian Variational Network (DCBVN). In DCBVN, we first extract the latent interpretable representations of the employees’ competencies from their skill profiles with autoencoding variational inference based topic modeling. Then, we develop an effective demand recognition mechanism for learning the personal demands of career development for employees. In particular, all the above processes are integrated into a unified Bayesian inference view for obtaining both accurate and explainable recommendations. Finally, extensive experimental results on real-world data clearly demonstrate the effectiveness and the interpretability of DCBVN, as well as its robustness on sparse and cold-start scenarios.
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction

Deep neural models for relation extraction tend to be less reliable when perfectly labeled data is limited, despite their success in label-sufficient scenarios. Instead of seeking more instance-level labels from human annotators, here we propose to annotate frequent surface patterns to form labeling rules. These rules can be automatically mined from large text corpora and generalized via a soft rule matching mechanism. Prior works use labeling rules in an exact matching fashion, which inherently limits the coverage of sentence matching and results in the low-recall issue. In this paper, we present a neural approach to ground rules for RE, named Nero, which jointly learns a relation extraction module and a soft matching module. One can employ any neural relation extraction models as the instantiation for the RE module. The soft matching module learns to match rules with semantically similar sentences such that raw corpora can be automatically labeled and leveraged by the RE module (in a much better coverage) as augmented supervision, in addition to the exactly matched sentences. Extensive experiments and analysis on two public and widely-used datasets demonstrate the effectiveness of the proposed Nero framework, comparing with both rule-based and semi-supervised methods. Through user studies, we find that the time efficiency for a human to annotate rules and sentences are similar (0.30 vs. 0.35 min per label). In particular, Nero’s performance using 270 rules is comparable to the models trained using 3,000 labeled sentences, yielding a 9.5x speedup. Moreover, Nero can predict for unseen relations at test time and provide interpretable predictions. We release our code1 to the community for future research.
Servicing Your Requirements: An FCA and RCA-Driven Approach for Semantic Web Services Composition

The evolution of Service-Oriented Computing (SOC) provides more efficient software development methods for building and engineering new value-added service-based applications.SOC is a computing paradigm that relies on Web services as fundamental elements.Research and technical advancements in Web services composition have been considered as an effective opportunity to develop new service-based applications satisfying complex requirements rapidly and efficiently.In this paper, we present a novel approach enhancing the composition of semantic Web services.The novelty of our approach, as compared to others reported in the literature, rests on: i) mapping user's/organization's requirements with Business Process Modeling Notation (BPMN) and semantic descriptions using ontologies, ii) considering functional requirements and also different types of non-functional requirements, such as quality of service (QoS), quality of experience (QoE), and quality of business (QoBiz), iii) using Formal Concept Analysis (FCA) technique to select the optimal set of Web services, iv) considering composability levels between sequential Web services using Relational Concept Analysis (RCA) technique to decrease the required adaptation efforts, and finally, v) validating the obtained service-based applications by performing an analytical technique, which is the monitoring.The approach experimented on an extended version of the OWLS-TC dataset, which includes more than 10830 Web services descriptions from various domains.The obtained results demonstrate that our approach allows to successfully and effectively compose Web services satisfying different types of user's functional and non-functional requirements.
RemoteEye: An open-source high-speed remote eye tracker

The increasing employment of eye-tracking technology in different application areas and in vision research has led to an increased need to measure fast eye-movement events. Whereas the cost of commercial high-speed eye trackers (above 300 Hz) is usually in the tens of thousands of EUR, to date, only a small number of studies have proposed low-cost solutions. Existing low-cost solutions however, focus solely on lower frame rates (up to 120 Hz) that might suffice for basic eye tracking, leaving a gap when it comes to the investigation of high-speed saccadic eye movements. In this paper, we present and evaluate a system designed to track such high-speed eye movements, achieving operating frequencies well beyond 500 Hz. This includes methods to effectively and robustly detect and track glints and pupils in the context of high-speed remote eye tracking, which, paired with a geometric eye model, achieved an average gaze estimation error below 1 degree and average precision of 0.38 degrees. Moreover, average undetection rate was only 0.33%. At a total investment of less than 600 EUR, the proposed system represents a competitive and suitable alternative to commercial systems at a tiny fraction of the cost, with the additional advantage that it can be freely tuned by investigators to fit their requirements independent of eye-tracker vendors.
Model agnostic interpretability of rankers via intent modelling

A key problem in information retrieval is understanding the latent intention of a user's under-specified query. Retrieval models that are able to correctly uncover the query intent often perform well on the document ranking task. In this paper we study the problem of interpretability for text based ranking models by trying to unearth the query intent as understood by complex retrieval models.
Fuzzy Density Peaks Clustering

As an exemplar-based clustering method, the well-known density peaks clustering (DPC) heavily depends on the computation of kernel-based density peaks, which incurs two issues: first, whether kernel-based density can facilitate a large variety of data well, including cases where ambiguity and uncertainty of the assignment of the data points to their clusters may exist, and second, whether the concept of density peaks can be interpreted and manipulated from the perspective of soft partitions (e.g., fuzzy partitions) to achieve enhanced clustering performance. In this article, in order to provide flexible adaptability for tackling ambiguity and uncertainty in clustering, a new concept of fuzzy peaks is proposed to express the density of a data point as the fuzzy-operator-based coupling of the fuzzy distances between a data point and its neighbors. As a fuzzy variant of DPC, a novel fuzzy density peaks clustering (FDPC) method FDPC based on fuzzy operators (especially S-norm operators) is accordingly devised along with the same algorithmic framework of DPC. With an appropriate choice of a fuzzy operator with its associated tunable parameter for a clustering task, FDPC can indeed inherit the advantage of fuzzy partitions and simultaneously provide flexibility in enhancing clustering performance. The experimental results on both synthetic and real data sets demonstrate that the proposed method outperforms or at least remains comparable to the comparative methods in clustering performance by choosing appropriate parameters in most cases.
A Sense of Place: VR Journalism and Emotional Engagement

Virtual reality (VR) and other immersive technologies introduce new opportunities for emotionally compelling narratives and user agency. Virtually mediated environments lie at the heart of immersive journalism (IJ) experiences, foregrounding a sense of presence and bridging the connection between the user and the character. Mediated environments in VR stories provide more than a setting since the user can interact with and respond to the surroundings. Drawing on the theory of spatial narrative, documentary and cinema literature and studies on media morality, this article examines the meaning of place in VR news stories and its ability to engage the user with the story. This study contributes to the discussion of creating and communicating places in journalism studies by examining spatial storytelling in immersive news stories, which are available in the NYT VR smartphone application. This paper argues that spatial storytelling eventually affects what is experienced and how it is experienced either by demonstrating the circumstances with aesthetical elements or via the selection of spaces.
High-Speed Post-Processing in Continuous-Variable Quantum Key Distribution Based on FPGA Implementation

In a continuous-variable quantum key distribution (CV-QKD) system, the computation speed of the post-processing procedure, including information reconciliation (IR) and privacy amplification (PA), inevitably affects the practical secret key rate. IR and PA can be implemented in parallel using low-density parity-check (LDPC) codes and hash functions, respectively. We achieve high-speed hardware-accelerated post-processing procedure for Gaussian symbols on a field-programmable gate array (FPGA) by taking advantage of its superior parallel processing ability. To this end, the sum-product algorithm decoders and a modified LDPC codes construction algorithm adapted to FPGA's characteristics are developed and employed. Two different structures including multiplexing and non-multiplexing are designed to achieve the trade-off between the speed and area of FPGAs, so that an optimal scheme can be adopted according to the requirement of a practical system. Simulation results show that the maximum throughput can reach 100 M symbols/s. We verified the correctness of the post-processing procedures when implemented on the Xilinx VC709 evaluation board, which is populated with the Virtex-7 XC7VX690T FPGA and provided some possible solutions to obtain better performance when more advanced FPGAs are available. The scheme can be applied readily for real-time key extraction and effectively reduce power consumption of the CV-QKD system.
Evaluation of ERA5 and MERRA2 reanalyses to estimate solar irradiance using ground observations over Indonesia region

This study examines the feasibility of using reanalyses datasets to estimate solar irradiance over the Indonesia region. Solar irradiance from two reanalyses, the ERA5 from ECMWF and MERRA2 from NASA's GMAO were evaluated using the quality-controlled global horizontal irradiance obtained from the ASRS network, for a one-year period. The evaluation is done on an hourly and monthly value basis. Statistical evaluation and data visualizations were used to evaluate both datasets. The biases under different levels of cloudiness were also assessed. The hourly solar irradiance estimated using the ERA5 reanalysis shows positive biases against the observed hourly solar irradiance, except in Bengkulu station (mean bias error ranges from -7 to 34 W/m2). On the other hand, lower estimates are shown by the MERRA2 reanalysis (mean bias error ranges from -40 to 1 W/m2). The evaluation over the monthly solar radiation also presents the same result; ERA5 overestimates the monthly solar radiation in most stations, whereas MERRA2 tends to underestimate the monthly solar radiation. The clearness index over Indonesia region varies from 0.1 - 0.7, which indicates the common cloudy condition over this area. On clear sky condition, or lower clearness index, the observed solar irradiance tends to be underestimated, whilst on cloudy days the estimates from both reanalyses tend to be higher than the observed ones. In general, the estimates from ERA5 show better agreement with the ground observations. The information about biases can be used to correct-bias the estimates. Cloud cover affects the biases of those reanalysis datasets and this finding should be kept in mind in further usage of the reanalysis dataset.
Federated Multi-view Matrix Factorization for Personalized Recommendations

We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is able to learn the multi-view model without transferring the user's personal data to a central server. As far as we are aware this is the first federated model to provide recommendations using multi-view matrix factorization. The model is rigorously evaluated on three datasets on production settings. Empirical validation confirms that federated multi-view matrix factorization outperforms simpler methods that do not take into account the multi-view structure of the data, in addition, it demonstrates the usefulness of the proposed method for the challenging prediction tasks of cold-start federated recommendations.
Motion Planning Approach Considering Localization Uncertainty

Localization plays an important role in autonomous driving since a high level of accuracy in vehicle localization is indispensable for a safe navigation. Most of the motion planning approaches in the literature assume negligible uncertainty in vehicle localization. However, the accuracy of localization systems can be low by design or even can drop depending on the environment in some cases. In these situations, the localization uncertainty can be taken into consideration in motion planning to increase the system reliability. Accordingly, this work presents two main contributions: (i) a probabilistic occupancy grid-based approach for localization uncertainty propagation, and (ii) a motion planning strategy that relies on such occupancy grid. Thus, the proposed motion planning solution for automated driving is able to generate safe human-like trajectories in real time while considering the localization uncertainty, ego-vehicle constrains and obstacles. In order to validate the proposed algorithms, several experiments have been conducted in a real environment.
Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I &amp; Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.
Multi-View K-Means Clustering With Adaptive Sparse Memberships and Weight Allocation

Recently, many real-world applications exploit multi-view data, which is collected from diverse domains or obtained from various feature extractors and reflect different properties or distributions of the data. In this work, a novel unsupervised multi-view framework is proposed to cluster such data. The proposed method, called Multi-View clustering with Adaptive Sparse Memberships and Weight Allocation (MVASM), pays more attention to constructing a common membership matrix with proper sparseness over different views and learns the centroid matrix and its corresponding weight of each view. Concretely, MVASM method attempts to learn a common and flexible sparse membership matrix to indicate the clustering, which explores the underlying consensus information of multiple views, and solves the multiple centroid matrices and weights to utilize the view-specific information and further modifies the above-mentioned membership matrix. In addition, the theoretical analysis, including the determination of the power exponent parameter, convergence analysis, and complexity analysis are also presented. Compared to the state-of-the-art methods, the proposed method improves the performance of clustering on different public datasets and demonstrates its reasonability and superiority.
Intrusion Detection and Prevention using Honeypot Network for Cloud Security

With the rapid increase in the number of users, there is a rise in issues related to hardware failure, web hosting, space and memory allocation of data, which is directly or indirectly leading to the loss of data. With the objective of providing services that are reliable, fast and low in cost, we turn to cloud-computing practices. With a tremendous development in this technology, there is ever increasing chance of its security being compromised by malicious users. A way to divert malicious traffic away from systems is by using Honeypot. It is a colossal strategy that has shown signs of improvement in security of systems. Keeping in mind the various legal issues one may face while deploying Honeypot on third-party cloud vendor servers, the concept of Honeypot is implemented in a file-sharing application which is deployed on cloud server. This paper discusses the detection attacks in a cloud-based environment as well as the use of Honeypot for its security, thereby proposing a new technique to do the same.
A viral theory of post-truth

There is an ecology of bad ideas, just as there is an ecology of weeds, and it is characteristic of the system that basic error propagates itself.–Gregory Bateson, Steps Towards an Ecology of Mind ...
Analysis and Design of First-Order Distributed Optimization Algorithms Over Time-Varying Graphs

This work concerns the analysis and design of distributed first-order optimization algorithms over time-varying graphs. The goal of such algorithms is to optimize a global function that is the average of local functions using only local computations and communications. Several different algorithms have been proposed that achieve linear convergence to the global optimum when the local functions are strongly convex. We provide a unified analysis that yields the worst-case linear convergence rate as a function of the condition number of the local functions, the spectral gap of the graph, and the parameters of the algorithm. The framework requires solving a small semidefinite program whose size is fixed; it does not depend on the number of local functions or the dimension of their domain. The result is a computationally efficient method for distributed algorithm analysis that enables the rapid comparison, selection, and tuning of algorithms. Finally, we propose a new algorithm, which we call SVL, that is easily implementable and achieves a faster worst-case convergence rate than all other known algorithms.
SMAN: Stacked Multimodal Attention Network for Cross-Modal Image–Text Retrieval

This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods.