Reviewerly/exHarmony huggingface dataset:
LELNER: A Lightweight and Effective Low-resource Named Entity Recognition model

Named entity recognition aims to find the target entity from the input sentence and determine the category it belongs to. Low-resource means that the training data used by the model is scarce. In order to improve the performance of the model with a small quantity of labeled data, previous works propose the concept of the trigger and introduce trigger information. Despite their excellent performance, these methods still have shortcomings in trigger representation generation, information fusion and model training. To remedy these deficiencies, this paper proposes the LELNER model including information interaction module and information fusion network. The information interaction module realizes the interaction between trigger and sentence, which leads to trigger representation containing more entity information. The information fusion network perfectly merges the trigger representation into the sentence sequence. As a result, the network has a better fusion effect than previous nonlinear fusion methods. In terms of model training, this paper designs a one-step training method to replace the two-step training method used in previous models, which provides convenience for the entire training process. Experimental results show that the proposed LELNER model achieves state-of-the-art results on three public datasets BC5CDR, CONLL and SemEval-lap.1
Watch The Videos Whenever You Have Time

We present a video-based approach for collecting feedback on virtual reality (VR) prototypes. While developing a high-fidelity VR prototype to help neurologists analyze seizure propagation information for brain surgery planning, our neurologist collaborators' limited availability reduced opportunities for them to give feedback on critical design decisions. In response, we developed a remote feedback process in which developers created videos of the VR design process and used these to ground iterative input from neurologist collaborators. We describe our approach and detail opportunities and challenges for video-based feedback to play a role in future VR prototyping.
Locality and priority in Auction-based resource allocation in blockchain network

Blockchain is very popular as a decentralized and secure platform. Blockchains usually use a proof-of-work mechanism and cryptographic puzzle to verify network transactions. This operation is called block mining. Due to limited local computing resources, likes smartphones, miners load computational tasks into resources available on cloud/fog servers. Therefore, we can consider it as a market that includes computational resources and miners. Then, it is natural to looking for an efficient resource allocation policy through an auction-based mechanism. In this paper, we introduce the issue of priority and locality to algorithms in this realm using an efficient resource allocation algorithm. The idea of implemented priority algorithms is allocating resources to VIP customers, which have more priority than other customers. In addition, the goal of implemented locality algorithms is to reduce the time delay of information exchange in the data transmission network by bringing resources closer to customers. We also discuss the issue of social welfare and time complexity. Our evaluations indicate that our method does a better job compared to the similar work in the literature.
Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder

Due to recent advances in sensing technologies, response measurements of various sensors are frequently used for system monitoring purposes. However, response data are often affected by some contextual variables, such as equipment settings and time, resulting in different patterns, even when the system is in the normal state. In this case, anomaly detection methods that do not consider contextual variables may be unable to distinguish between abnormal and normal patterns of the response data affected by the contextual variables. Motivated by this problem, we propose a method for contextual anomaly detection, particularly in the case where the response and contextual variables are both high-dimensional and complex. The proposed method is based on Variational AutoEncoders (VAEs), which are neural-network-based generative models suitable for modeling high-dimensional and complex data. The proposed method combines two VAEs: one for response variables and the other for contextual variables. Specifically, in the latent space of the VAE for contextual variables, we model the latent variables using a Dirichlet process Gaussian mixture model. Consequently, the effects of the contextual variables can be modeled using several clusters, each representing a different contextual environment. The latent contextual variables are then used as additional inputs to the other VAE’s decoder for reconstructing response data from their latent representations. We then detect the anomalies based on the negative reconstruction loss of a new response observation. The effectiveness of the proposed method is demonstrated using several benchmark datasets and a case study based on a global tire company.
A Study on the Impact of Digital Finance on Regional Productivity Growth Based on Artificial Neural Networks

The relationship between financial development and economic growth has become a hot topic in recent years and for China, which is undergoing financial liberalisation and policy reform, the efficiency of the use of digital finance and the deepening of the balance between quality and quantity in financial development are particularly important for economic growth. This paper investigates the utility of digital finance and financial development on total factor productivity in China using interprovincial panel data decomposing financial development into financial scale and financial efficiency; an interprovincial panel data model is used to explore the utility of digital finance on total factor productivity. This involves the collection and preprocessing of financial data, including feature engineering, and the development of an optimised predictive model. We preprocess the original dataset to remove anomalous information and improve data quality. This work uses feature engineering to select relevant features for fitting and training the model. In this process, the random forest algorithm is used to effectively avoid overfitting problems and to facilitate the dimensionality reduction of the relevant features. In determining the model to be used, the random forest regression model was chosen for training. The empirical results show that digital finance has contributed to productivity growth but is not efficiently utilised; China should give high priority to improving financial efficiency while promoting financial expansion; rapid expansion of finance without a focus on financial efficiency will not be conducive to productivity growth.
Architecture for ontology-supported multi-context reasoning systems

Modern smart systems such as those needed for Industry 4.0 integrate data from various sources and increasingly require that data be contextualized with domain knowledge. The integration and contextualization of data allows for the advanced reasoning needed to generate knowledge grounded in the data under consideration. In this paper, we propose an architecture for an ontology-supported multi-context reasoning system which inherently supports a number of desired system qualities including data transparency, system interactivity, and graceful aging. The architecture is inspired by the Presentation–Abstraction–Control architecture style, which is an interaction-based architecture. Our architecture uses a two level hierarchy with three agents and can incorporate and utilize multiple contexts. It is flexible, supporting an interface between data and users, highly interactive, and easily maintained. The evolution of data is isolated to a single component of the system and therefore does not cascade to several others. A domain of application can be easily determined by the use of archetypes and domain-specification components. Our architecture is demonstrated using a case study involving data from the city of San Francisco.
User dynamic topology-information-based matrix factorization for e-government recommendation

Today, there is a growing tendency to integrate all public services on e-government websites, a trend that increases the risk of information overload. Matrix factorization (MF)-based recommendation models can effectively predict the user preference for an item, thus demonstrating their superiority in alleviating information overload problems. However, factors such as poor latent representations and the linear interaction function limit the further performance of these models To address these issues, we propose a novel method named user dynamic topology-information-based matrix factorization (User-DTMF). User-DTMF contains two designed modules: the dynamic topology feature learning module and the interactive preference learning module. The former first constructs the user dynamic topology sequence based on user historical behaviors. A convolutional neural network (CNN) is then used to extract features from the sequence. To obtain the final representations of users and items, the latter module uses MF and a fully connected (FC) neural network to model the dynamic topology features and static user-item interactive information. Next, another FC layer, which can be regarded as a nonlinear interaction function, is used to predict the probability of a user clicking on an item. Comprehensive experiments on a real dataset show that User-DTMF outperforms the best benchmark method by a margin of 1.68% and 3.05% in terms of Recall@10 and NDCG@10, respectively.
Source language difficulties in learner translation

Abstract This study uses an error-annotated, mass-media subset of a sentence-aligned, multi-parallel learner translator corpus to reveal source-language items that are challenging in English–Russian translation. Our data includes multiple translations of the most challenging source sentences, drawn from a large collection of student translations on the basis of error statistics. This sample was subjected to manual contrastive-comparative analysis, which resulted in a list of English items that were difficult for students. The outcome of the analysis was compared to the topics discussed in translation textbooks that are recommended for BA and specialist-degree students in Russia. We discuss items that deserve more prominence in training as well as items that call for improvements to traditional learning activities. This study presents evidence that a more empirically motivated design of the practical translation syllabus as part of translator education is required.
A Study on the Performance of Recurrent Neural Network based Models in Maithili Part of Speech Tagging

This article presents our effort in developing a Maithili Part of Speech (POS) tagger. Substantial effort has been devoted to developing POS taggers in several Indian languages, including Hindi, Bengali, Tamil, Telugu, Kannada, Punjabi, and Marathi; but Maithili did not achieve much attention from the research community. Maithili is one of the official languages of India, with around 50 million native speakers. So, we worked on developing a POS tagger in Maithili. For the development, we use a manually annotated in-house Maithili corpus containing 56,126 tokens. The tagset contains 27 tags. We train a conditional random fields (CRF) classifier to prepare a baseline system that achieves an accuracy of 82.67%. Then, we employ several recurrent neural networks (RNN)-based models, including Long-short Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM with a CRF layer (LSTM-CRF), and GRU with a CRF layer (GRU-CRF) and perform a comparative study. We also study the effect of both word embedding and character embedding in the task. The highest accuracy of the system is 91.53%.
A context-enhanced sentence representation learning method for close domains with topic modeling

Sentence representation approaches have been widely used and proven to be effective in many text modeling tasks and downstream applications. Many recent proposals are available on learning sentence representations based on deep neural frameworks. However, these methods are pre-trained in open domains and depend on the availability of large-scale data for model fitting. As a result, they may fail in some special scenarios, where data are sparse and embedding interpretations are required, such as legal, medical, or technical fields. In this paper, we present an unsupervised learning method to exploit representations of sentences for some closed domains via topic modeling. We reformulate the inference process of the sentences with the corresponding contextual sentences and the associated words, and propose an effective context-enhanced process called the bi-Directional Context-enhanced Sentence Representation Learning (bi-DCSR). This method takes advantage of the semantic distributions of the nearby contextual sentences and the associated words to form a context-enhanced sentence representation. To support the bi-DCSR, we develop a novel Bayesian topic model to embed sentences and words into the same latent interpretable topic space called the Hybrid Priors Topic Model (HPTM). Based on the defined topic space by the HPTM, the bi-DCSR method learns the embedding of a sentence by the two-directional contextual sentences and the words in it, which allows us to efficiently learn high-quality sentence representations in such closed domains. In addition to an open-domain dataset from Wikipedia, our method is validated using three closed-domain datasets from legal cases, electronic medical records, and technical reports. Our experiments indicate that the HPTM significantly outperforms on language modeling and topic coherence, compared with the existing topic models. Meanwhile, the bi-DCSR method does not only outperform the state-of-the-art unsupervised learning methods on closed domain sentence classification tasks, but also yields competitive performance compared to these established approaches on the open domain. Additionally, the visualizations of the semantics of sentences and words demonstrate the interpretable capacity of our model.
Improved Decision-Making through a DEMATEL and Fuzzy Cognitive Maps-Based Framework

The decision-making process is highly demanding. There has been an increasing tendency to incorporate human thinking, individual experience about a problem, and pure mathematical approaches. Here, a novel integrated approach is investigated and proposed to develop an advanced hybrid decision-support system based on the decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps (FCMs). Indeed, knowledge acquisition and elicitation may present distortions and difficulties finding a consensus and an interpretation. Thus, the proposed combined approach aims to examine in depth the potential to improve FCMs’ outcomes by integrating FCM with the DEMATEL approach. The combined methodology achieves at avoiding some of the drawbacks, such as the lack of a standardized FCM theoretical model. Thus, it provides advanced comparative analysis and results in better interpretation of the decision-making process. It is highlighted that the traditional FCM approach does not allow distinguishing the whole number of defined scenarios, in contrast to the hybrid one presented here, which increases the ability of users to make correct decisions. Combining the two approaches provides new capabilities to FCMs in grouping experts’ knowledge, while the DEMATEL approach contributes to refining the strength of concepts’ connections.
Sentiment Analysis: a Cognitive Perspective

With the phenomenal advancement of Intelligent systems, understanding natural languages and processing natural language has become a crucial task. This has found its application in various domains like conversational AI (Artificial Intelligence), social media analysis, text analytics, etc. Sentiment Analysis emerged as an important Natural Language Processing (NLP) which aims to classify a natural language text based on its intent polarity. Natural Languages are comprised of varied linguistic expressions. Identifying, representing, and reasoning these expressions are necessary for a better interpretation of the sentiment conveyed in the text. In this concept paper, we put forth a detailed taxonomy of Sentiment Analysis, which categorizes possible sentiment expressions that exist in a natural language. It identifies the various sentiment components that are vital in extracting the idea, feeling, opinion, etc. from the text. The paper suggests the use of these classifications in various domains, where sentiment analysis is applied. We also put forth the proposed augmentation in the existing NLP pipeline activities to efficiently perform granular sentiment analysis. The concepts and discussions are presented with reference to the Hindi language, owing to its popularity in Indian Subcontinent. The sentiment taxonomy will be a stepping stone for developing in-depth fine-grained Sentiment Analysis.
Population Symmetrization in Genetic Algorithms

The paper presents a memetic modification of the classical genetic algorithm by introducing a cyclic symmetrization of the population, symmetrizing the parental points around the current population leader. Such an operator provides a more spherical distribution of the population around the current leader, which significantly improves exploitation. The proposed algorithm was described, illustrated by examples, and theoretically analyzed. Its effectiveness was examined using a recognized benchmark, which includes the continuous functions test set on a multidimensional cube, to be minimized.
An Evolutionary Computation Approach for Twitter Bot Detection

Bot accounts are automated software programs that act as legitimate human profiles on social networks. Identifying these kinds of accounts is a challenging problem due to the high variety and heterogeneity that bot accounts exhibit. In this work, we use genetic algorithms and genetic programming to discover interpretable classification models for Twitter bot detection with competitive qualitative performance, high scalability, and good generalization capabilities. Specifically, we use a genetic programming method with a set of primitives that involves simple mathematical operators. This enables us to discover a human-readable detection algorithm that exhibits a detection accuracy close to the top state-of-the-art methods on the TwiBot-20 dataset while providing predictions that can be interpreted, and whose uncertainty can be easily measured. To the best of our knowledge, this work is the first attempt at adopting evolutionary computation techniques for detecting bot profiles on social media platforms.
The adoption of digital technology for South Africa’s 2021 municipal elections, and prospects for the future

This article / study reflects on the digital technologies and online processes adopted for conducting South Africa’s 2021 municipal elections, and the degree to which this encourages the future use of additional digital technologies. It utilises theoretical perceptions of politics and technology to analyse the perspectives of politicians, IEC officials and voters. It discusses the Independent Electoral Commission’s (IEC) use of voter management devices (VMDs) and online voter facilities, which resolved the perennial challenge of double voting. It shows that, due to COVID-19, voter apathy and possibly manual voting, the voter turnout was low. It argues that technological progress made during the 2021 municipal elections should be used to suppress fears over the consequences of electronic voting. Limited access to the internet and electricity blackouts continue to limit the use of digital technology and online process in managing South African elections. However, the IEC could adopt parallel e-voting and manual voting processes, as it did successfully for voter registration during the 2021 municipal elections.
Hyperspherically regularized networks for self-supervision

Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative sampling associated with such methods. However, we empirically find that the image representations produced under the BYOL's self-distillation paradigm are poorly distributed in representation space compared to contrastive methods. This work empirically demonstrates that feature diversity enforced by contrastive losses is beneficial to image representation uniformity when employed in BYOL, and as such, provides greater inter-class representation separability. Additionally, we explore and advocate the use of regularization methods, specifically the layer-wise minimization of hyperspherical energy (i.e. maximization of entropy) of network weights to encourage representation uniformity. We show that directly optimizing a measure of uniformity alongside the standard loss, or regularizing the networks of the BYOL architecture to minimize the hyperspherical energy of neurons can produce more uniformly distributed and therefore better performing representations for downstream tasks.
bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition

This paper proposes a new mixed-integer programming (MIP) formulation to optimize split rule selection in the decision tree induction process and develops an efficient search algorithm that is able to solve practical instances of the MIP model faster than commercial solvers. The formulation is novel for it directly maximizes the Gini reduction, an effective split selection criterion that has never been modeled in a mathematical program for its nonconvexity. The proposed approach differs from other optimal classification tree models in that it does not attempt to optimize the whole tree; therefore, the flexibility of the recursive partitioning scheme is retained, and the optimization model is more amenable. The approach is implemented in an open-source R package named bsnsing. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared with trees trained by other decision tree codes including the rpart, C50, party, and tree packages in R. Compared with other optimal decision tree packages, including DL8.5, OSDT, GOSDT, and indirectly more, bsnsing stands out in its training speed, ease of use, and broader applicability without losing in prediction accuracy. History: Accepted by RamRamesh, Area Editor for Data Science &amp; Machine Learning. Funding: This work was supported by the National Science Foundation Division of Civil, MechanicalandManufacturing Innovation [Grant 1944068]. Supplemental Material: Data are available at https://doi.org/10.1287/ijoc.2022.1225 .
STUDY ON THE DEVELOPMENT OF REINFORCED CONCRETE IN THE DEPARTMENT OF ARCHITECTURE OF JAPAN FROM THE ENDING OF RUSSO-JAPANESE WAR TO GREAT KANTO EARTHQUAKE (1906-1923)

Based on inquiries into certain original materials, the development of reinforced concrete is conceived in two aspects. 1. The theories had reached a high level in the middle of the 1910s, when a peak of the popularization of reinforced concrete also existed. During the 1910s, the construction technologies developed rapidly, while the experiments were mainly introduced from overseas. 2. Around the end of the 1910s, most of the buildings of reinforced concrete were constructed in frame structure, with evolvement of bar arrangement. However, the mechanical model was still simply supported system, with limited consideration of rigid joint.