Reviewerly/exHarmony huggingface dataset:
Entity Concept-enhanced Few-shot Relation Extraction

Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs, due to limited samples and lack of knowledge.To address this problem, in this paper, we proposed a novel entity CONCEPTenhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction and boost the relations classification performance.Firstly, a conceptsentence attention module is developed to select the most appropriate concept from multiple concepts of each entity by calculating the semantic similarity between sentences and concepts.Secondly, a self-attention based fusion module is presented to bridge the gap of concept embedding and sentence embedding from different semantic spaces.Extensive experiments on the FSRE benchmark dataset FewRel have demonstrated the effectiveness and the superiority of the proposed ConceptFERE scheme as compared to the state-of-the-art baselines.Code is available at https://github.com/LittleGuoKe/ConceptFERE.
Taxonomy of Virtual and Augmented Reality Applications in Education

This article presents and analyzes existing taxonomies of virtual and augmented reality and demonstrates knowledge gaps and mixed terminology, which may cause confusion among educators, researchers, and developers. Several such occasions of confusion are presented. A methodology is then presented to construct a taxonomy of virtual reality and augmented reality applications based on a combination of: a faceted analysis approach for the overall design of the taxonomy; an existing taxonomy of educational objectives to derive the educational purpose; an information systems analysis to establish important facets of the taxonomy; and two systematic mapping studies to identify categories within each facet. Using this methodology, a new taxonomy is proposed and the implications of its facets (and combinations of facets) are demonstrated. The taxonomy focuses on technology used to provide the virtual or augmented reality as well as the content presented to the user, including the type of gamification and how it is operated. It also accommodates a large number of devices and approaches developed throughout the years and for multiple industries, and provides a way to categorize them in order to clarify communication between researchers, developers, and educators. Use of the taxonomy is then demonstrated in two case studies-a virtual reality chemical plant for use in chemical engineering education and an augmented reality dog for veterinary education.
WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning

Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it is natural to cast it as constrained reinforcement learning, where expected long-term costs of policies are constrained. However, it can be hazardous to set constraints on the expected safety signal without considering the tail of the distribution. For instance, in safety-critical domains, worst-case analysis is required to avoid disastrous results. We present a novel reinforcement learning algorithm called Worst-Case Soft Actor Critic, which extends the Soft Actor Critic algorithm with a safety critic to achieve risk control. More specifically, a certain level of conditional Value-at-Risk from the distribution is regarded as a safety measure to judge the constraint satisfaction, which guides the change of adaptive safety weights to achieve a trade-off between reward and safety. As a result, we can optimize policies under the premise that their worst-case performance satisfies the constraints. The empirical analysis shows that our algorithm attains better risk control compared to expectation-based methods.
WC-KNNG-PC: Watershed clustering based on k-nearest-neighbor graph and Pauta Criterion

Watershed clustering utilizes the concept of watershed algorithm to process clustering or cluster analyzes. The most attractive characteristic of this method is the capability to determine automatically the number of clusters from the data sets. However, in terms of the literature, the purposes of the original watershed clustering algorithm and the improved version are the detection of the clusters within two-dimensional linear data sets. In order to enable watershed clustering to deal with the dataset with multiple dimensions and nonlinear structures, we introduce k-nearest neighbor graph (KNNG), the shared nearest neighbor method and Pauta Criterion into watershed clustering to present a new watershed graph clustering with noise detection, WC-KNNG-PC. This approach first calculates a KNNG for the data sets, and then compute catchment basins (subclusters), basin immersions (connectivity between basins) and outliers. To prevent the merger of illegal subclusters, a maximum normalization stability factor, based on t-nearest neighbors and angle, MNSF, is proposed to detect the invalid basin immersions. Finally, a basin level similarity using median criterion is presented to merge the catchment basins to obtain the final clustering. Experiments on complex synthetic datasets and multidimensional real-world datasets have successfully demonstrated that the performance of the WC-KNNG-PC in clustering some various dimensional and complex datasets with heterogeneous density and diverse shapes.
How to identify class comment types? A multi-language approach for class comment classification

Most software maintenance and evolution tasks require developers to understand the source code of their software systems. Software developers usually inspect class comments to gain knowledge about program behavior, regardless of the programming language they are using. Unfortunately, (i) different programming languages present language-specific code commenting notations and guidelines; and (ii) the source code of software projects often lacks comments that adequately describe the class behavior, which complicates program comprehension and evolution activities. To handle these challenges, this paper investigates the different language-specific class commenting practices of three programming languages: Python, Java, and Smalltalk. In particular, we systematically analyze the similarities and differences of the information types found in class comments of projects developed in these languages. We propose an approach that leverages two techniques – namely Natural Language Processing and Text Analysis – to automatically identify class comment types, i.e., the specific types of semantic information found in class comments. To the best of our knowledge, no previous work has provided a comprehensive taxonomy of class comment types for these three programming languages with the help of a common automated approach. Our results confirm that our approach can classify frequent class comment information types with high accuracy for the Python, Java, and Smalltalk programming languages. We believe this work can help in monitoring and assessing the quality and evolution of code comments in different programming languages, and thus support maintenance and evolution tasks.
Design of UWB Antenna Based on Improved Deep Belief Network and Extreme Learning Machine Surrogate Models

In the design of conventional microwave devices, the parameters need to be continuously optimized to meet the desired targets, and the whole process is time-consuming and laborious.As a surrogate model, machine learning is an effective optimization method.However, in the modeling process, the highdimensional data processing and the complex nonlinear relationship between parameters is a problem to be solved.This paper proposes a deep learning model for designing UWB antennas, which determines the model structure of deep belief network (DBN) by particle swarm algorithm (PSO), and then combines DBN and extreme learning machine (ELM).The proposed model can obtain higher feature learning capability and nonlinear function approximation capability, and has been applied to the optimal design of the whole structure of the fractal antenna and the notch structure of the MIMO antenna, and its S-parameters are well fitted while meeting the requirements of the design targets.The DBN-ELM method obtains the good results when compared with common modeling methods using the same training samples (the root mean square error tested is 11.87% in the fractal antenna and 3.56% in the MIMO antenna).Overall, the proposed DBN-ELM model has higher predictive and generalization capabilities, which can also be used to model more complex antenna structures.
Temporal Knowledge Graph Completion Using Box Embeddings

Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp. Current approaches for TKGC primarily build on existing embedding models which are developed for static knowledge graph completion, and extend these models to incorporate time, where the idea is to learn latent representations for entities, relations, and timestamps and then use the learned representations to predict missing facts at various time steps. In this paper, we propose BoxTE, a box embedding model for TKGC, building on the static knowledge graph embedding model BoxE. We show that BoxTE is fully expressive, and possesses strong inductive capacity in the temporal setting. We then empirically evaluate our model and show that it achieves state-of-the-art results on several TKGC benchmarks
Neural Machine Translation using Recurrent Neural Network

In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer

There has been growing interest in parameterefficient methods to apply pre-trained language models to downstream tasks.Building on the PROMPTTUNING approach of Lester et al. ( 2021), which learns task-specific soft prompts to condition a frozen pre-trained model to perform different tasks, we propose a novel prompt-based transfer learning approach called SPOT: Soft Prompt Transfer.SPOT first learns a prompt on one or more source tasks and then uses it to initialize the prompt for a target task.We show that SPOT significantly boosts the performance of PROMPT-TUNING across many tasks.More remarkably, across all model sizes, SPOT matches or outperforms standard MODELTUNING (which finetunes all model parameters) on the SUPER-GLUE benchmark, while using up to 27,000× fewer task-specific parameters.To understand where SPOT is most effective, we conduct a large-scale study on task transferability with 26 NLP tasks in 160 combinations, and demonstrate that many tasks can benefit each other via prompt transfer.Finally, we propose an efficient retrieval approach that interprets task prompts as task embeddings to identify similar tasks and predict the most transferable source tasks for a novel target task.
Improving graph neural network for session-based recommendation system via non-sequential interactions

In the absence of user profile information, recommender systems have to only rely on current session information for recommendation. E-commerce sites may use transitions between interactions in each session to improve recommendation. This situation is known as the session-based recommendation. It can be challenging due to the limited information and the uncertain user behavior. Recurrent Neural Networks (RNN) have become the state-of-the-art models for session-based recommendation due to their ability to model long sequences. Although powerful, RNN-based models suffer from learning complex transition between the interactions. To mitigate it, Graph Neural Networks (GNN) have been proposed for session-based recommendation. However, different sequences of interactions may lead to the same outcome especially on E-commerce sites, hence non-sequential interactions between items of the current session may improve the performance of a recommender system. To learn both the sequential and non-sequential transition interactions between the items in the current session, we proposed a GNN based model named GRASER. Specifically, the proposed model first learns the non-sequential and then the sequential transition interactions between the items of the current session using GNN in an end-to-end manner. Extensive experiments were carried out on two datasets: Yoochoose from the RecSys Challenge 2015 and Diginetica from CIKM Cup 2016. The results showed that the proposed model outperforms the other state-of-the-art models by 11% and 10% on [email protected] on Yoochoose and Diginetica datasets respectively.
Standardization of the Distributed Ledger Technology cybersecurity stack for power and energy applications

The global trend toward integration of distributed energy resources is opening doors to advanced, complex, and distributed marketplaces. Such advanced ecosystems, where utility-owned and non utility-owned assets can contribute toward grid operations, generally require distributed communication and grid architectures. We posit the potential of using Distributed Ledger Technologies (DLTs) in supporting such applications, although their full potential has not been fully used, for example in designing long-term scalable solutions in operational technology applications. This is partly due to the lack of standardization across and between different DLTs, as well as other supporting building blocks (e.g., communication protocols). This paper attempts to address this gap by proposing a DLT cybersecurity stack specifically designed for researchers, DLT technology developers, and end users (such as utilities). The DLT cybersecurity stack has been notionally mapped to related cybersecurity components, namely the Open Systems Interconnection (OSI) model, the Transmission Control Protocol/Internet Protocol (TCP/IP) suite, and existing Smart Grid architecture frameworks. In addition, the paper discusses several cybersecurity implications, and demonstrates the potential uses of the DLT cybersecurity stack through multiple power and energy use cases. It is important to note that the stack can be also applied to the DLT use cases that are outside the power and energy domain. This work was performed by the Cybersecurity Task Force under the IEEE P2418.5 Blockchain for Energy Standard working group that part of the IEEE Power and Energy Society’s Smart Buildings, Loads, and Customer Systems (SBLC) technical committee.
Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey

The rapid evolution of the Internet of Things (IoT) paradigm during the last decade has lead to its adoption in critical infrastructure.However, the multitude of benefits that are derived from the IoT paradigm are short-lived due to the exponential rise in the associated security and privacy threats.Adversaries carry out privacy-oriented attacks to gain access to the sensitive and confidential data of critical infrastructure for various self-centered, political and commercial gains.In the past, researchers have employed several privacy preservation approaches including cryptographic encryption and k-anonymity to secure IoT-enabled critical infrastructure.However, for various reasons, those proposed solutions are not well suited for modern IoT-enabled critical infrastructure.Therefore, Dwork's differential privacy has emerged as the most viable privacy preservation strategy for IoT-enabled critical infrastructure.This paper provides a comprehensive and extensive survey of the application and implementation of differential privacy in four major application domains of IoT-enabled critical infrastructure: Smart Grids (SGs), Intelligent Transport Systems (ITSs), healthcare and medical systems, and Industrial Internet of Things (IIoT).Finally, we discuss some promising future research directions in differential privacy for IoT-enabled critical infrastructure.
Structural Adapters in Pretrained Language Models for AMR-to-Text Generation

Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.However, efficiently encoding the graph structure in PLMs is challenging because such models were pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge.In this paper, we propose STRUCTADAPT, an adapter method to encode graph structure into PLMs.Contrary to prior work, STRUCTADAPT effectively models interactions among the nodes based on the graph connectivity, only training graph structure-aware adapter parameters.In this way, we incorporate task-specific knowledge while maintaining the topological structure of the graph.We empirically show the benefits of explicitly encoding graph structure into PLMs using STRUCTADAPT, outperforming the state of the art on two AMR-to-text datasets, training only 5.1% of the PLM parameters. 1
A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets

Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
Blockchain Applications in Education: A Systematic Literature Review

Blockchain is one of the latest technologies attracting increasing attention from different actors in diverse fields, including the educational sector. The objective of this study is to offer an overview of the current state of the art related to blockchain in education that may serve as a reference for future initiatives in this field. For this, a systematic review of reference journals was carried out. Eleven databases were systematically searched and eligible papers that focused on blockchain in education that made significant contributions, and not only generic statements about the topic, were selected. As a result, 28 articles were analyzed. Lack of precision, and selection and analysis bias were then minimized by involving three researchers. The analysis of the selected papers provided invaluable insight and answered the research questions posed about the current state of the application of blockchain in education, about which of its characteristics can benefit this sector, and about the challenges that must be addressed. Blockchain may become a relevant technology in the educational field, and therefore many proofs of concept are being developed. However, there are still some relevant technological, regulatory and academic issues to be addressed to pave the way for the mainstream adoption of this technology.
Robotic Herding of Farm Animals Using a Network of Barking Aerial Drones

This paper proposes a novel robotic animal herding system based on a network of autonomous barking drones. The objective of such a system is to replace traditional herding methods (e.g., dogs) so that a large number (e.g., thousands) of farm animals such as sheep can be quickly collected from a sparse status and then driven to a designated location (e.g., a sheepfold). In this paper, we particularly focus on the motion control of the barking drones. To this end, a computationally efficient sliding mode based control algorithm is developed, which navigates the drones to track the moving boundary of the animals’ footprint and enables the drones to avoid collisions with others. Extensive computer simulations, where the dynamics of the animals follow Reynolds’ rules, show the effectiveness of the proposed approach.
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right

Large language models have shown promising results in zero-shot settings (Brown et al., 2020;Radford et al., 2019).For example, they can perform multiple choice tasks simply by conditioning on a question and selecting the answer with the highest probability.However, ranking by string probability can be problematic due to surface form competition-wherein different surface forms compete for probability mass, even if they represent the same underlying concept in a given context, e.g."computer" and "PC."Since probability mass is finite, this lowers the probability of the correct answer, due to competition from other strings that are valid answers (but not one of the multiple choice options).We introduce Domain Conditional Pointwise Mutual Information, an alternative scoring function that directly compensates for surface form competition by simply reweighing each option according to its a priori likelihood within the context of a specific task.It achieves consistent gains in zero-shot performance over both calibrated (Zhao et al., 2021) and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets.