Reviewerly/exHarmony huggingface dataset:
Document-Level Event Role Filler Extraction Using Key-Value Memory Network

Previous work has demonstrated that end-to-end neural sequence models work well for document-level event role filler extraction. However, the end-to-end neural network model suffers from the problem of not being able to utilize global information, resulting in incomplete extraction of document-level event arguments. This is because the inputs to BiLSTM are all single-word vectors with no input of contextual information. This phenomenon is particularly pronounced at the document level. To address this problem, we propose key-value memory networks to enhance document-level contextual information, and the overall model is represented at two levels: the sentence-level and document-level. At the sentence-level, we use BiLSTM to obtain key sentence information. At the document-level, we use a key-value memory network to enhance document-level representations by recording information about those words in articles that are sensitive to contextual similarity. We fuse two levels of contextual information by means of a fusion formula. We perform various experimental validations on the MUC-4 dataset, and the results show that the model using key-value memory networks works better than the other models.
Performance Evaluation of Machine Learning Algorithm in Various Datasets

Machine learning is one of the fast-growing areas of computer science, with far-reaching applications. There are several applications for machine learning. The most significant of which is supervised learning. Supervised learning is common in classification problems. In this study, frequently used twelve machine learning algorithms are considered: NB, LDA, LR, ANN, SVM, K-NN, HT, DT, C4.5, CART, RF and BB. We apply these algorithms on seven datasets. The main goal of this study was to evaluate the performance of the machine learning algorithms on both binary and multiple classification problems using a variety of performance metrics: accuracy, kappa statistic, precision, recall, specificity, F-measure, MAE, RMSE and MCC. Here, we found that RF algorithm proved to have the best performance in three out of seven datasets. But the other four algorithms: NN, NB, BB and LR also performed well.
A 2-Tier Bengali Dataset for Evaluation of Hard and Soft Classification Approaches

Document classification is an open problem in library, information, and computer sciences towards assigning documents to one or more classes. The interest of linguistic researchers in this domain has increased day by day due to interesting applications like language identification, readability assessment, sentiment analysis, spam filtering, etc. However, researchers focussing on natural language processing of resource-scaring languages have faced many hurdles due to the absence of benchmark datasets. Bengali is among the most-spoken resource-scaring or low-resource language. Although Bengali NLP researchers have endeavoured towards creating their own datasets, they are only useful for performance evaluation of their proposed document classification techniques only. Therefore, there is a gap in the literature on the availability of benchmark datasets. To overcome this barrier, this paper presents a benchmark dataset for Bengali document classification, which is publicly accessible and freely available. This dataset consists of a two-tier architecture, the first-tier for hard classification and the second-tier for soft classification techniques. Hard classification techniques follow supervised learning based models for the classification of documents, while on the other hand, soft classification techniques follow unsupervised learning based models for the clustering of documents. The proposed dataset consists of thirteen unique characteristics. This paper also introduces four new feature sets to evaluate the performance of the proposed dataset, namely: location revealing factor, part of speech tagging factor, relative frequency, and prominence factor.
Proof-of-concept of feasibility of human–machine peer learning for German noun vocabulary learning

The present study provides the first empiric evidence that the creation of human–machine peer learning (HMPL) couples can lead to an increase in the level of mastery of different competences in both humans and machines alike. The feasibility of the HMPL approach is demonstrated by means of Curriculum 1 whereby the human learner H gradually acquires a vocabulary of foreign language, while the artificial learner fine-tunes its ability to understand H's speech. The present study evaluated the feasibility of the HMPL approach in a proof-of-concept experiment that is composed of a pre-learn assessment, a mutual learning phase, and post-learn assessment components. Pre-learn assessment allowed us to estimate prior knowledge of foreign language learners by asking them to name visual cues corresponding to one among 100 German nouns. In a subsequent mutual learning phase, learners are asked to repeat the audio recording containing the label of a simultaneously presented word with the visual cue. After the mutual learning phase is over, the subjacent speech-to-text (STT) neural network fine-tunes its parameters and adapts itself to peculiar properties of H's voice. Finally, the exercise is terminated by the post-learn assessment phase. In both assessment phases, the number of mismatches between the expected answer and the answer provided by human and recognized by machine provides the metrics of the main evaluation. In the case of all six learners who participated in the proof-of-concept experiment, we observed an increase in the amount of matches between expected and predicted labels, which was caused both by an increase in human learner's vocabulary as well as by an increase in the recognition accuracy of machine's speech-to-text model. Therefore, the present study considers it reasonable to postulate that curricula could be drafted and deployed for different domains of expertise, whereby humans learn from AIs at the same time as AIs learn from humans.
Manifold-enhanced CycleGAN for facial expression synthesis

Facial expressions contain a great deal of information in inter-personal communication and social interaction and have a great impact on face recognition. This paper investigates expression synthesis methods based on deep learning and their performance in expression classification and face recognition. We propose a new synthesis method based on the active appearance model and CycleGAN. The eigendecomposition method is used to obtain the expression shape of a target subject through linear operations. An expression intensity coefficient is developed to control the dynamics of generated expressions. Subsequently, a generative model is introduced for the synthesis on textures. The identity loss is integrated into the traditional CycleGAN model, incorporated with the adversarial loss and the cycle consistency loss, to better preserve texture attributes and identity information of target subjects. Experiments and comparisons show that the proposed method offers marked improvements in verification of expressions and identities, as well as in generalization across databases.
Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation

With the progress of signal processing technology and the emergence of new system radars, the space electromagnetic environment becomes more and more complex, which puts forward higher requirements for the deinterleaving method of radar signals. Traditional signal deinterleaving algorithms rely heavily on manual experience threshold and have poor robustness. To address this problem, we designed an intelligent radar signal deinterleaving algorithm that was completed by encoding the frequency characteristic matrix and semantic segmentation network, named Sep-RefineNet. The frequency characteristic matrix can well construct the semantic features of different pulse streams of radar signals. The Sep-RefineNet semantic segmentation network can complete pixel-level segmentation of the frequency characteristic matrix and finally uses position decoding and verification to obtain the position in the original pulse stream to complete radar signals deinterleaving. The proposed method avoids the processing of threshold judgment and pulse sequence search in traditional methods. The results of the experiment show that this algorithm improves the deinterleaving accuracy and has a good against-noise ability of aliasing pulses and missing pulses.
Blockchain Orchestration and Transformation for Construction

Blockchain-related studies that focus on solving AECO (Architecture, Engineering, Construction and Operation) digital management environment issues, such as data protection and data ownership, show the projected benefits of Blockchain-based digital construction environments. However, adopting such technology will require a holistic approach to ensure it does not result in data redundancy, leading to digital system inefficiencies. This article studies the Blockchain construction synergies from the infrastructure point of view to understand its future in construction. The article visualises Blockchain infrastructure elements and fits them within the construction project’s digital environment. A novel framework for Blockchain orchestration and implementation and a blueprint for developing Blockchain applications for construction are presented. The proposed blueprint is then used to develop a Blockchain application using Hyperledger Firefly. The article builds on the previous literature and Blockchain applications on the Ethereum public Blockchain. The expected benefit of such a framework is providing a practical perspective on the implementation side of Blockchain in construction.
Editorial for pattern recognition letters special issue on face-based emotion understanding

Network pruning extends a promising prospect to neural network compression. However, applications of existing methods to various vision tasks, e.g., human pose estimation, are limited by the parameter distribution difference, shortage of general pruning strategies and high pruning cost. As a complex and widely used backbone network, HRNet is an effective benchmark for studying these issues. We propose an adaptive Hard channel pruning method with an Adaptive Compression Rate to replace manual experience in pruned model selection, called HACR. It first designs a hard criterion that takes γ and β to measure channel importance with the insight of the consistency relationship between the distribution adjustment of the channel and the final output distribution. Then, a partial pruning strategy is advanced for the complex structural network by only pruning channels without cross-layer integration and keeping the network infrastructure. Next, we adaptively predict an optimal compression rate based on the validation metric of unfine-tuned models, providing a reference for compression parameter selection. The processes of HACR are simplified to adaptive pruned model selection and fine-tuning with lower pruning cost. Furthermore, the numerical distribution of γ and β is used to extend HACR in common pruning task benchmarks, based on the a principle that lower level channels should be preferentially retained. We conduct HRNet pruning experiments on COCO2017 and MPII, and promote extension study on CIFAR-10 to verify the generality of strategy. The results show that HACR is effective on multiple tasks, e.g. HRNet-W48 reduces 58.2% Params and 50.1% FLOPS, and VGG-16 reduces 91.5% Params and 67.5% FLOPS with only 0.3% precision loss.
MSTIL: Multi-cue Shape-aware Transferable Imbalance Learning for effective graphic API recommendation

Application Programming Interface (API) recommendation based on graphs is a valuable task in the fields of data visualization and software engineering. However, this task was previously undefined until a recently published paper coining the task as Plot2API and utilizing a deep learning-based method named SPGNN. Compared to general image classification methods, this dedicated approach uses semantic parsing to exploit deep features and yields better performance. However, its performance declines sharply in unbalanced datasets, thus limiting its generalizability. To address this issue, we propose a method named Multi-cue Shape and software engineering-aware Transferable Imbalance Learning (MSTIL), consisting of three major components: Cross-Language Shape-Aware Plot Transfer Learning (CLSAPTL), Cross-Language API Semantic Similarity-based Data Augmentation (CLASSDA), and Imbalance Plot2API Learning (IPL). Motivated by the hierarchical classification of the graphs, CLSAPTL guides the model to learn the graphs' class hierarchy and thereby enabling the model to learn more transferable visual features. Given that a graph can be associated with multiple APIs and motivated by the fact that many APIs that exert similar functions in different languages have semantically similar names, CLASSDA leverages the samples of APIs with semantically similar names to assist in feature learning. Finally, inspired by the essence of softmax cross entropy loss, IPL alleviates the imbalances between positive and negative samples during training. We conduct our experiments on two public datasets. Extensive experimental results shows that MSTIL improves the performance of classic CNNs along with the state-of-the-art method, demonstrating its effectiveness. Specifically, MSTIL has an average relative mAP improvement of 12.94% across the models on all datasets.
Risks and threats of the digital age

The first steps on the way to the new century were marked by the almost instantaneous introduction of the latest information and communication technologies in all spheres of life and the desire to remove with their help the deep problems of the organization of our lives. We waited for it with incomprehensible thirst, and some invisible force pushed us to break the chains that connected the present with all the testaments of the past, which, however, have the meaning of real life. The world of digital technologies, in which we are immersed deeper, really changes our attitude to them and we should know that this is not only a new logical stage in the technological sphere of human development, but also the transformation of all legal, socio-political and, above all, scientific and educational reality. Unfortunately, there are still not many generally accepted and harmonized philosophical definitions and legal definitions, without which it is impossible to build a policy, however, digital technologies are already rapidly capturing bridgeheads for development, causing new risks and threats to which we must prepare an algorithm. Key words: information, communication, information and communication technologies, digitalization, digital society, education, national security.
A TL_FLAT Model for Chinese Text Datasets of UAV Power Systems: Optimization and Performance

The manufacturing processes of unmanned aerial vehicle (UAV) power systems generate large amounts of data and knowledge. The extraction of useful information or patterns from redundant data and knowledge texts has become a challenge in intelligent manufacturing. Unfortunately, graphics processing unit (GPU)-based parallel computing is limited, and the inference speeds of the available named entity recognition (NER) models for Chinese text datasets are low because they are mainly based on the long short-term memory (LSTM) algorithm. Herein, first, the flat-lattice transformer (FLAT) model was optimized by using a stochastic gradient descent with momentum (SGDM) optimizer and adjusting the model hyperparameters. Compared with the existing NER methods, the proposed optimization algorithm achieved better performance on the available dataset. Then, an NER method named the TL_FLAT model based on transfer learning and the abovementioned optimization model was introduced. Finally, a Chinese text dataset from a UAV power system created by the authors was used to validate the proposed method. The F1 score was 76.26%, the precision value was 76.98%, and the recall value was 75.56%, indicating that the TL_FLAT model was suitable for Chinese text entity recognition for UAV power systems.
Understanding the Effect of Outlier Items in E-commerce Ranking

Implicit feedback is an attractive source of training data in Learning to Rank (LTR). However, naively use of this data can produce unfair ranking policies originating from both exogenous and endogenous factors. Exogenous factors comes from biases in the training data, which can lead to rich-get-richer dynamics. Endogenous factors can result in ranking policies that do not allocate exposure among items in a fair way. Item exposure is a common components influencing both endogenous and exogenous factors which depends on not only position but also Inter-item dependencies. In this project, we focus on a specific case of these Inter-item dependencies which is the existence of an outlier in the list. We first define and formalize outlierness in ranking, then study the effects of this phenomenon on endogenous and exogenous factors. We further investigate the visual aspects of presentational features and their impact on item outlierness.
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization

Temporal difference (TD) learning with nonlinear function approximation (nonlinear TD learning for short) permits evaluating policies using neural networks, and is a core component in modern deep reinforcement learning. Though significant advances have been made to improve its effectiveness, little attention has been paid to the data privacy faced when applying it in real applications. To mitigate the privacy concerns in practical applications of nonlinear TD learning, in this paper, we consider preserving its privacy under the notion of differential privacy (DP). This problem is challenging since nonlinear TD learning is usually studied in the formulation of stochastic nonconvex-strongly-concave optimization to obtain finite-sample analysis, which requires simultaneously preserving privacy on both primal and dual sides. To this end, we adopt a single-timescale algorithm, which optimizes both sides using learning rates of the same order, to avoid unnecessary privacy costs. Further, we achieve a good trade-off between the privacy and utility guarantees by perturbing gradients on both sides using Gaussian noises with well-calibrated variances. Consequently, our algorithm achieves rigorous (ε,δ)-DP guarantee with the utility upper bounded by ~O((d/log(1/δ))1/8 (nε)1/4) where n is the trajectory length and d is the ambient dimension of the feature space. Extensive experiments conducted in OpenAI Gym validate the advantages of our algorithm.
Self-supervision and Controlling Techniques to Improve Counter Speech Generation

Hate speech is a challenging problem in today's online social media. One of the current solutions followed by different social media platforms is detecting hate speech using human-in-the-loop approaches. After detection, they moderate such hate speech by deleting the posts or suspending the users. While this approach can be a short-term solution for reducing the spread of hate, many researchers argue that it stifles freedom of expression. An alternate strategy that does not hamper freedom of expression is counterspeech. Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. This pipeline has two major challenges 1) How to improve the performance of generation without a large-scale dataset since building the dataset is costly 2) How to add control in the counter speech generation to make it more personalized. In this paper, we present our published and proposed research aimed at solving these two challenges.
Blockchain-Driven Supply Chain’s Financing and Coordination Strategy under Nash Bargaining Scheme

Blockchain-driven supply chain finance (SCF) has become an emerging supply chain financing mode. Compared with the traditional SCF mode, blockchain-driven SCF can shorten the transaction process and provide real trade data that cannot be tampered with, thereby improving the efficiency of capital operation. This article explores the financing and coordination problems of a blockchain-driven supply chain. Based on the three most common supply chain contracts (revenue-sharing contract, profit-sharing contract, and two-part tariff contract), we construct a comparative model of bank credit financing (BCF) and blockchain-driven SCF, discuss the optimal decision-making strategy of the supply chain, and quantitatively analyze the performance of supply chain under each mode. The results show the following: (i) there is a threshold for the usage rate of the blockchain-driven SCF platform; (ii) only when the platform usage rate is lower than the threshold, the blockchain-driven SCF mode which benefits both manufacturer and retailer is a better choice; (iii) the above results always hold if supply chain contracts can coordinate the supply chain in terms of quantity decisions; (iv) the blockchain-driven SCF mode is more efficient for supply chains which are less capital-constrained. This article provides a decision basis for the selection of supply chain financing channels and provides an idea for future research on blockchain-driven SCF.
SimSumIoT: A Platform for Simulating the Summarisation from Internet of Things

Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.
Who’s Who in Mineral Names: Marian Tredoux (1952–2019)

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSThanks are extended to Stefan Ansermet, associate researcher, photographer, and mineralogy assistant, Cantonal Museum of Geology, Lausanne, Switzerland, for use of the tredouxite image, and to Steven C. Chamberlain and John S. White for reviewing the manuscript. The late Marian Tredoux provided her photo. One of us (BC) acknowledges that this work was partly funded by the NRF (National Research Foundation) and CIMERA (the DSI-NRF Centre of Excellence for Integrated Mineral and Energy Resource Analysis). Any opinions, findings and conclusions or recommendations expressed are those of the author, and therefore the NRF and DSI-NRF CIMERA do not accept liability in regard thereto.Additional informationNotes on contributorsBruce CairncrossDr. Bruce Cairncross, a consulting editor of Rocks & Minerals, is an emeritus professor at the University of Johannesburg.Chris HarrisDr. Chris Harris is the Philipson Stow Professor of Mineralogy and Geology in the Department of Geological Sciences, University of Cape Town. Cape Town, South Africa.Frederick RoelofseDr. Frederick Roelofse is an associate professor in the Department of Geology at the University of the Free State, Bloemfontein, South Africa.Cornie Van HuyssteenDr. Cornie van Huyssteen is an emeritus professor in the Department of Soil, Crop and Climate Sciences at the University of the Free State, Bloemfontein, South Africa.