Reviewerly/exHarmony huggingface dataset:
Downstream Model Design of Pre-trained Language Model for Relation Extraction Task

Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. However, at present, their performance still fails to reach a good level due to the existence of complicated relations. On the other hand, recently proposed pre-trained language models (PLMs) have achieved great success in multiple tasks of natural language processing through fine-tuning when combined with the model of downstream tasks. However, original standard tasks of PLM do not include the relation extraction task yet. We believe that PLMs can also be used to solve the relation extraction problem, but it is necessary to establish a specially designed downstream task model or even loss function for dealing with complicated relations. In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction. Experiments have shown that our method significantly exceeded the current optimal baseline models across multiple public datasets of relation extraction.
Joint Deep Recommendation Model Exploiting Reviews and Metadata Information

User-generated product reviews contain a lot of valuable information including users' opinions on products and product features that is not fully exploited by the current recommendation models. Similarly, the metadata information related to the products, about the reviews and about the users who have written the reviews has rarely been exploited for recommender systems. These heterogeneous information sources have the potential to alleviate the cold start and sparsity problems and improve the quality of recommendations. In this paper, we present a joint deep recommendation model (JDRM) that consists of two parallel neural networks, learning lower-order feature interactions of users and items separately and higher-order feature interactions jointly using a shared last layer. Each of the networks is further composed of two sub-networks. One of the sub-networks focus on exploiting product reviews (of user/item) and the other sub-network learns user preferences/items properties leveraging metadata information along with the ratings. The learned latent features in each network are concatenated, thus producing the user and item latent feature vectors. We combine the two networks by introducing a shared layer on the top, which is a dense fully connected layer used to learn higher level latent features obtained from the two networks and produces final ratings. Extensive experiments on real-world datasets demonstrate that JDRM significantly outperforms state of the art recommendation models.
Self-Net: Lifelong Learning via Continual Self-Modeling

Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) store a new network (or an equivalent number of parameters) for each new task, (2) store training data from previous tasks, or (3) restrict the network's ability to learn new tasks. To address these issues, we propose a novel framework, Self-Net, that uses an autoencoder to learn a set of low-dimensional representations of the weights learned for different tasks. We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights. Self-Net can incorporate new tasks over time with little retraining, minimal loss in performance for older tasks, and without storing prior training data. We show that our technique achieves over 10X storage compression in a continual fashion, and that it outperforms state-of-the-art approaches on numerous datasets, including continual versions of MNIST, CIFAR10, CIFAR100, Atari, and task-incremental CORe50. To the best of our knowledge, we are the first to use autoencoders to sequentially encode sets of network weights to enable continual learning.
BBA-NET: A Bi-Branch Attention Network For Crowd Counting

In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the algorithm. To this end, we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce false responses. iii) A new density map generation method combining geometric adaptation and Voronoi split is introduced. Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map. Extensive experiments performed on two public datasets show that our method achieves a lower crowd counting error compared to other state-of-the-art methods.
Transient-Based Internet of Things Emitter Identification Using Convolutional Neural Networks and Optimized General Linear Chirplet Transform

This letter investigates the application of the General Linear Chirplet Transform (GLCT), a time-frequency representation recently proposed in the literature, in combination with Convolutional Neural Network (CNN) for the identification of Internet of Things Internet of Things (IOT) wireless devices. The identification is performed by analyzing the radio frequency emissions from the IOT devices during transmission. We apply the novel combination of CNN and GLCT (CNN-GLCT) to the transient portions of the radio frequency emissions by proposing an optimization procedure for GLCT which is specifically designed for emitter identification. Our empirical results show that this combination provides a superior identification performance as compared to other Deep CNN approaches available in the literature, as well as to approaches based on shallow machine learning methods, like SVM and KNN, especially under low Signal Noise Ratio (SNR) and fading conditions.
The potential of integrating blockchain technology into smart sustainable city development

Abstract The rise of global urbanisation has led to massive pressures on resources such as food, water, infrastructure, and energy demand to support growing populations. It brings adverse impacts on the liveable condition and economic growth of a country if this problem remains unsolved. Smart city is a potential solution to address the challenges of urbanisation by leveraging the technological breakthrough such as internet of things (IoT), Artificial Intelligence (AI), machine learning, big data, and cloud computing to facilitate scarce resources planning and management. With numerous connected devices and vast communication networks, it poses a challenges of security threat which cannot be addressed by the conventional cybersecurity solutions. Blockchain offers a solution in securing the huge numbers of connected devices in smart city network. The application of blockchain technology is leading in the banking and financial industry. However, the uses and implementations in smart city have emerged in recent years. The combination of blockchain technology and smart city has offered a great potential for sustainable development. Thus, it is imperative to discuss the potential of these two elements in making the city safer and sustainable. This paper explores how the blockchain technology application can help in managing smart city and achieve sustainability. The findings revealed that there are five key areas of blockchain application in smart city which are smart governance, smart mobility, smart asset, smart utility and smart logistic. A framework for smart sustainable city with blockchain technology is presented as an outcome of this study. It gives a clear overview for the policy makers and regulators of how blockchain supports within smart city framework. It facilitates the transition towards smart and sustainable cities through the use of blockchain.
Early-Stage Neural Network Hardware Performance Analysis

The demand for running NNs in embedded environments has increased significantly in recent years due to the significant success of convolutional neural network (CNN) approaches in various tasks, including image recognition and generation. The task of achieving high accuracy on resource-restricted devices, however, is still considered to be challenging, which is mainly due to the vast number of design parameters that need to be balanced. While the quantization of CNN parameters leads to a reduction of power and area, it can also generate unexpected changes in the balance between communication and computation. This change is hard to evaluate, and the lack of balance may lead to lower utilization of either memory bandwidth or computational resources, thereby reducing performance. This paper introduces a hardware performance analysis framework for identifying bottlenecks in the early stages of CNN hardware design. We demonstrate how the proposed method can help in evaluating different architecture alternatives of resource-restricted CNN accelerators (e.g., part of real-time embedded systems) early in design stages and, thus, prevent making design mistakes.
Breast Ultrasound Image Classification Using a Pre-Trained Convolutional Neural Network

Breast ultrasound (BUS) imaging is commonly used for breast cancer diagnosis, but the interpretation of BUS images varies based on the radiologist's experience. Computer-aided diagnosis (CAD) systems have been proposed to provide the radiologist with an objective, computer-based classification of BUS images. Nevertheless, the majority of these systems are based on handcrafted features that are designed manually to quantify the tumor. Hence, the accuracy of these CAD systems depends on the capability of the handcrafted features to differentiate between benign and malignant tumors. Convolutional neural networks (CNNs) provide a promising approach to improve the classification of BUS images due to their ability to achieve data-driven extraction of objective, accurate, and generalizable image representations. However, the limited size of the available BUS image databases might restrict the capability of training the CNNs from scratch. To address this limitation, we investigate the use of two approaches, namely the deep features extraction approach and transfer learning approach, to enable the use of a pre-trained CNN model to achieve accurate classification of BUS images. The results show that the deep features extraction approach outperforms the transfer learning approach. Moreover, the results indicate that the extraction of deep features from the pre-trained CNN model, which is combined with effective features selection, has enabled accurate BUS image classification with accuracy, sensitivity, and specificity values of 93.9%, 95.3%, and 92.5%, respectively. These results suggest the feasibility of combining deep features extracted from pre-trained CNN models with effective features selection algorithms to achieve accurate BUS image classification.
Care organising technologies and the post-phenomenology of care: An ethnographic case study

Care organising technologies are software applications that are intended primarily for informal carers, to help organise, document and coordinate caring work. These may be purchased privately or provided as part of state support. Take-up to date remains low. Based on empirical case studies of three such technologies and drawing on post-phenomenology and political science, we examined people's experience of caring when caring technologies find a way into their lives. Our findings show how care organising technologies have evolved in a political context that assumes informal support will supplement and sometimes substitute for state support. Technologies were largely designed to foreground the technical and organisational aspects of care such as planning meals, coordinating medication, and allocating and monitoring tasks among carers. For carers, the result was often a flattening of the landscape of care such that the socio-emotional work of caring was rendered invisible and relations between cared-for and caregiver were configured in narrow transactional terms. For a small number of carers, the focus on tasks was out of tune with their (often emotionally charged) experiences of care and led to active rejection of the technology. However, we also found examples of caregivers and the individuals they cared for using technologies adaptively to facilitate and embed existing care relationships. In these examples, the material/technical, socio-emotional and bodily aspects of caring were interwoven with the situated context of close, unique and evolving relationships. We conclude that the design and development of caring technologies would benefit by being informed by a broader orientation of caring as a relational practice.
Analysis of stochastic gradient descent in continuous time

Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional. In this work, we introduce the stochastic gradient process as a continuous-time representation of stochastic gradient descent. The stochastic gradient process is a dynamical system that is coupled with a continuous-time Markov process living on a finite state space. The dynamical system -- a gradient flow -- represents the gradient descent part, the process on the finite state space represents the random subsampling. Processes of this type are, for instance, used to model clonal populations in fluctuating environments. After introducing it, we study theoretical properties of the stochastic gradient process: We show that it converges weakly to the gradient flow with respect to the full target function, as the learning rate approaches zero. We give conditions under which the stochastic gradient process with constant learning rate is exponentially ergodic in the Wasserstein sense. Then we study the case, where the learning rate goes to zero sufficiently slowly and the single target functions are strongly convex. In this case, the process converges weakly to the point mass concentrated in the global minimum of the full target function; indicating consistency of the method. We conclude after a discussion of discretisation strategies for the stochastic gradient process and numerical experiments.
Review and Improvement of the Finite Moment Problem

Background: This paper reviews the Particle Size Distribution (PSD) problem in detail. Mathematically, the problem faced while recovering a function from a finite number of its geometric moments has been discussed with the help of the Spline Theory. Undoubtedly, the splines play a major role in the theory of interpolation and approximation in many fields of pure and applied sciences. B-Splines form a practical basis for the piecewise polynomials of the desired degree. A high degree of accuracy has been obtained in recovering a function within the first ten to fifteen geometric moments. The proposed approximation formula has been tested on several types of synthetic functions. This work highlights some advantages, such as the use of a practical basis for the approximating space, the exactness of computing the moments of these basis functions and the reduction of the size along with an appropriate transformation of the resulting linear system for stability. Objective: The aim is to recover a function from a finite number of its geometric moments. Methods: The main tool is the Spline Theory. Undoubtedly, the role of splines in the theory of interpolation and approximation in many fields of pure and applied sciences has been well- established. B-Splines form a practical basis for the piecewise polynomials of the desired degree. Results: A high degree of accuracy has been obtained in recovering a function within the first ten to fifteen geometric moments. The proposed approximation formula is tested on several types of synthetic functions. Conclusion: This work highlights some advantages, such as the use of a practical basis for the approximating space, the exactness of computing the moments of these basis functions and the reduction of the size along with the data transformation of the resulting linear system for stability.
Context-Oriented Behavioral Programming

Modern systems require programmers to develop code that dynamically adapts to different contexts, leading to the evolution of new context-oriented programming languages. These languages introduce new software-engineering challenges, such as: how to maintain the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more. This paper introduces Context-Oriented Behavioral Programming (COBP) — a novel paradigm for developing context-aware systems, centered on natural and incremental specification of context-dependent behaviors. As the name suggests, we combine behavioral-programming (BP) — a scenario-based modeling paradigm — with context idioms that explicitly specify when scenarios are relevant and what information they need. The core idea is to connect the behavioral model with a data model that represents the context, allowing an intuitive connection between the models via update and select queries. Combining behavioral-programming with context-oriented programming brings the best of the two worlds, solving issues that arise when using each of the approaches in separation. We begin with providing abstract semantics for COBP and two implementations for the semantics, laying the foundations for applying reasoning algorithms to context-aware behavioral programs. Next, we exemplify the semantics with formal specifications of systems, including a variant of Conway’s Game of Life. Then, we provide two case studies of real-life context-aware systems (one in robotics and another in IoT) that were developed using this tool. Throughout the examples and case studies, we provide design patterns and a methodology for coping with the above challenges. The case studies show that the proposed approach is applicable for developing real-life systems, and presents measurable advantages over the alternatives — behavioral programming alone and context-oriented programming alone. We present a paradigm allowing programmers and system engineers to capture complex context-dependent requirements and align their code with such requirements.
Using Feature Fusion and Parameter Optimization of Dual-input Convolutional Neural Network for Face Gender Recognition

In recent years, convolutional neural networks (CNNs) have been successfully used in image recognition and image classification. General CNNs only use a single image as feature extraction. If the quality of the obtained image is not good, it is easy to cause misjudgment or recognition error. Therefore, this study proposes the feature fusion of a dual-input CNN for the application of face gender classification. In order to improve the traditional feature fusion method, this paper also proposes a new feature fusion method, called the weighting fusion method, which can effectively improve the overall accuracy. In addition, in order to avoid the parameters of the traditional CNN being determined by the user, this paper uses a uniform experimental design (UED) instead of the user to set the network parameters. The experimental results show that in the dual-input CNN experiment, average accuracy rates of 99.98% and 99.11% on the CIA and MORPH data sets are achieved, respectively, which is superior to the traditional feature fusion method.
Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer

Knowledge graph completion (KGC) aims to predict missing information in a knowledge graph. Many existing embedding-based KGC models solve the Out-of-knowledge-graph (OOKG) entity problem (also known as zero-shot entity problem) by utilizing textual information resources such as descriptions and types. However, few works utilize the extra structural information to generate embeddings. In this paper, we propose a new zero-shot scenario: how to acquire the embedding vector of a relation that is not observed at training time. Our work uses a convolutional transition and attention-based aggregation graph neural network to solve both the OOKG entity problem and the new OOKG relation problem without retraining, regarding the structural neighbors as the auxiliary information. The experimental results show the effectiveness of our proposed models in solving the OOKG relation problem. For the OOKG entity problem, our model performs better than the previous GNN-based model by 23.9% in NELL-995-Tail dataset.
A Novel Path Planning Algorithm for Warehouse Robots Based on a Two-Dimensional Grid Model

In this paper, the path planning problem of goods transportation is formulated as a traveling salesman problem (TSP).A novel path planning algorithm for warehouse robots based on a two-dimensional (2D) grid model is proposed to solve this type of TSP.Firstly, we simplified the traditional pile type warehouse as a node-based 2D grid model.Then, a new concept called the largest convex polygon (LCP) is introduced to illustrate the shortest path to traverse all goods locations in an ideal condition.Next, the remaining locations are classified by their relationship with LCP and designed path planning rules separately.Finally, we merged the paths of different types of cargo locations to get the final path.The experimental results show that, compared with ant colony optimization (ACO) and genetic algorithm (GA), our proposed algorithm could effectively reduce the computation time and total path length.
Medical Dialogue Summarization for Automated Reporting in Healthcare

Healthcare providers generally spend excessive time on administrative tasks at the expense of direct patient care. The emergence of new artificial intelligence and natural language processing technologies gives rise to innovations that could relieve them of this burden. In this paper, we present a pipeline structure for building dialogue summarization systems. Our pipeline summarizes a consultation of a patient with a care provider and automatically generates a report compliant with medical formats. Four pipeline components are used to generate a report based on audio input. The outputs of each component are analyzed to determine the most important challenges and issues. The current proof-of-concept, which was applied to eight doctor-to-patient sessions concerning ear infection, shows that automatic dialogue summarization and reporting is achievable, but requires improvements to increase completeness.