Reviewerly/exHarmony huggingface dataset:
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations

Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community.Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction.We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time.To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction.This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling.We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.
Skeleton-Based Emotion Recognition Based on Two-Stream Self-Attention Enhanced Spatial-Temporal Graph Convolutional Network

Emotion recognition has drawn consistent attention from researchers recently. Although gesture modality plays an important role in expressing emotion, it is seldom considered in the field of emotion recognition. A key reason is the scarcity of labeled data containing 3D skeleton data. Some studies in action recognition have applied graph-based neural networks to explicitly model the spatial connection between joints. However, this method has not been considered in the field of gesture-based emotion recognition, so far. In this work, we applied a pose estimation based method to extract 3D skeleton coordinates for IEMOCAP database. We propose a self-attention enhanced spatial temporal graph convolutional network for skeleton-based emotion recognition, in which the spatial convolutional part models the skeletal structure of the body as a static graph, and the self-attention part dynamically constructs more connections between the joints and provides supplementary information. Our experiment demonstrates that the proposed model significantly outperforms other models and that the features of the extracted skeleton data improve the performance of multimodal emotion recognition.
Federated Learning Empowered End-Edge-Cloud Cooperation for 5G HetNet Security

The distributed and heterogeneous framework in the 5G heterogeneous network (Het-Net) makes it vulnerable to attacks of different kinds. Nodes for improving the network security are therefore important to eliminate such critical threats. Without cooperation or with limited cooperation, these nodes are substantially restricted in their protecting capacity due to specific characteristics such as heterogeneity, hierarchy, and wide range in the 5G HetNet. In this article, we propose a federated learning empowered end-edge-cloud cooperation-based framework for enhancing 5G HetNet security. In this framework, nodes equipped with attack detection mechanisms are distributed in the end, edge, and cloud of the 5G HetNet. We then design cooperative training schemes to realize the full potential of these nodes in detecting attacks. Illustrative results demonstrate the superior performance of our proposed scheme compared to three different benchmark schemes.
Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm

Traffic classification is widely used in various network functions such as software-defined networking and network intrusion detection systems. Many traffic classification methods have been proposed for classifying encrypted traffic by utilizing a deep learning model without inspecting the packet payload. However, they have an important challenge in that the mechanism of deep learning is inexplicable. A malfunction of the deep learning model may occur if the training dataset includes malicious or erroneous data. Explainable artificial intelligence (XAI) can give some insight for improving the deep learning model by explaining the cause of the malfunction. In this paper, we propose a method for explaining the working mechanism of deep-learning-based traffic classification as a method of XAI based on a genetic algorithm. We describe the mechanism of the deep-learning-based traffic classifier by quantifying the importance of each feature. In addition, we leverage the genetic algorithm to generate a feature selection mask that selects important features in the entire feature set. To demonstrate the proposed explanation method, we implemented a deep-learning-based traffic classifier with an accuracy of approximately 97.24%. In addition, we present the importance of each feature derived from the proposed explanation method by defining the dominance rate.
Deep active learning for classifying cancer pathology reports

Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model.We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes.Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.
Identification of OFDM-Based Radios Under Rayleigh Fading Using RF-DNA and Deep Learning

The Internet of Things (IoT) is here and has permeated every aspect of our lives. A disturbing fact is that the majority of all IoT devices employ weak or no encryption at all. This coupled with recent advances within the areas of computational power and deep learning has increased interest in Specific Emitter Identification (SEI) as an effective means of IoT security. Deep learning is capable of in-situ extraction of discriminating features, making it well suited to discrimination of wireless transmitters without the need for feature engineering. However, the accuracy of the deep learning model is adversely affected by time-varying channel conditions. The time-varying nature is attributed to the mobility of the transmitter, receiver, objects within the operations environment, or combinations thereof. This can result in the channel conditions changing faster than the deep learning algorithm is capable of handling. This paper assesses deep learning-based SEI using waveforms that undergo Rayleigh fading, as well as channel estimation and equalization, prior to being input into a deep learning algorithm.
Blockchain-Enabled Public Key Encryption with Multi-Keyword Search in Cloud Computing

The emergence of the cloud storage has brought great convenience to people’s life. Many individuals and enterprises have delivered a large amount of data to the third-party server for storage. Thus, the privacy protection of data retrieved by the user needs to be guaranteed. Searchable encryption technology for the cloud environment is adopted to ensure that the user information is secure with retrieving data. However, most schemes only support single-keyword search and do not support file updates, which limit the flexibility of the scheme. To eliminate these problems, we propose a blockchain-enabled public key encryption scheme with multi-keyword search (BPKEMS), and our scheme supports file updates. In addition, smart contract is used to ensure the fairness of transactions between data owner and user without introducing a third party. At the data storage stage, our scheme realizes the verifiability by numbering the files, which ensures that the ciphertext received by the user is complete. In terms of security and performance, our scheme is secure against inside keyword guessing attacks (KGAs) and has better computation overhead than other related schemes.
The influence of conversational agents’ role and behaviors on narrative experiences

The human brain, containing billions of nerve cells (neurons) and their connecting fibers (Axons and Dendrons), is among the least understood organs in our body. A bundle of axon makes up a nerve tract that transmits electrical signals to other neurons, muscles and glands throughout the body. Over the years, clinical visualization techniques of brain nerve tracts have been developed to enhance the performance of medical diagnostics and neurosurgery [1] [2]. One of them is 2D MRI brain scans (raster) while another is the use of 2.5D visualization software, such as ExploreDTI11https://www.exploredti.com/, to connect nerve tract pathways and visualize off-axis viewpoints of tract trajectories (vector). The main objective of this project is to apply Virtual (VR), Augmented (AR) or Mixed Reality (MR) technologies to this problem and progress current standalone 2.5D brain tract visualisation tools to collaborative 3DVR visualisation and analysis of brain structures.
IMI—Bigpicture: A Central Repository for Digital Pathology

To address the challenges posed by large-scale development, validation, and adoption of artificial intelligence (AI) in pathology, we have constituted a consortium of academics, small enterprises, and pharmaceutical companies and proposed the BIGPICTURE project to the Innovative Medicines Initiative. Our vision is to become the catalyst in the digital transformation of pathology by creating the first European, ethically compliant, and quality-controlled whole slide imaging platform, in which both large-scale data and AI algorithms will exist. Our mission is to develop this platform in a sustainable and inclusive way, by connecting the community of pathologists, researchers, AI developers, patients, and industry parties based on creating value and reciprocity in use based on a community model as the mechanism for ensuring sustainability of the platform.
Unsupervised Visual Representation Learning via Dual-Level Progressive Similar Instance Selection

The superiority of deeply learned representations relies on large-scale labeled datasets. However, annotating data are usually expensive or even infeasible in some scenarios. To address this problem, we propose an unsupervised method to leverage instance discrimination and similarity for deep visual representation learning. The method is based on an observation that convolutional neural networks (CNNs) can learn a meaningful visual representation with instancewise classification, in which each instance is treated as an individual class. By this instancewise discriminative learning, instances can reasonably distribute in the representation space, which reveals their similarities. In order to further improve visual representations, we propose a dual-level progressive similar instance selection (DPSIS) method to build a bridge from instance to class by selecting similar instances (neighbors) for each instance (anchor) and treating the anchor and its neighbors as the same class. To be specific, DPSIS adaptively selects two levels of neighbors, that is: 1) an “absolutely similar level” and 2) a “relatively similar level.” Instances in the absolutely similar level are used as hard labels, while instances in the relatively similar level are used as soft labels. Moreover, during training, DPSIS is able to progressively select more neighbors without human supervision. At the beginning of training, because CNNs are weak, most instances are distributed relatively randomly in the representation space and only a few easy-to-recognize instances are selected as neighbors. As CNN models become stronger, the semantic meaning of each instance grows clearer. Those instances originally distributed in a relatively random manner gradually move to meaningful positions. This consequently facilitates CNN training since the number of reliable samples increases. Experiments on seven benchmarks, including three small-scale and two large-scale coarse-grained image classification datasets, and two fine-grained categorization datasets, demonstrate the effectiveness of our DPSIS. Our codes have been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hehefan/DPSIS</uri> .
Task Duplication-Based Scheduling Algorithm for Budget-Constrained Workflows in Cloud Computing

Workflow scheduling is crucial to the efficient operation of cloud platforms, and has attracted a lot of attention.Up to now, many algorithms have been reported to schedule workflows with budget constraints, so as to optimize workflows' makespan on cloud resources.Nevertheless, the hourly-based billing model in cloud computing is an ongoing challenge for workflow scheduling that easily results in higher makespan or even infeasible solutions.Besides, due to data constraints among workflow tasks, there must be a lot of idle slots in cloud resources.Few works adequately exploit these idle slots to duplicate tasks' predecessors to shorten their completion time, thereby minimizing workflow's makespan while ensuring its budget constraint.Motivated by these, we propose a task duplication based scheduling algorithm, namely TDSA, to optimize makespan for budget-constrained workflows in cloud platforms.In TDSA, two novel mechanisms are devised: 1) a dynamic sub-budget allocation mechanism, it is responsible for recovering unused budget of scheduled workflow tasks and redistributing remaining budget, which is conducive to using more expensive/powerful cloud resources to accelerate completion time of unscheduled tasks; and 2) a duplication-based task scheduling mechanism, which strives to exploit idle slots on resources to selectively duplicate tasks' predecessors, such advancing these tasks' completion time while trying to ensuring their sub-budget constraints.At last, we carry out four groups of experiments, three groups on randomly generated workflows and another one on actual workflows, to compare the proposed TDSA with four baseline algorithms.Experimental results confirm that the TDSA has an overwhelming superiority in advancing the workflows' makespan (up to 17.4%) and improving the utilization of cloud computing resources (up to 31.6%).
Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment

In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation. The dataset consists of around 2000 high-resolution aerial images, with annotated ground-truth data for semantic segmentation. We discuss the challenges of the dataset and train the state-of-the-art methods on this dataset to evaluate how well these methods can recognize the disaster situations. Finally, we discuss challenges for future research.
Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to... | Find, read and cite all the research you need on Tech Science Press
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees

Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications.However, how to incorporate the syntax trees effectively and efficiently into pre-trained Transformers is still unsettled.In this paper, we address this problem by proposing a novel framework named Syntax-BERT.This framework works in a plug-and-play mode and is applicable to an arbitrary pre-trained checkpoint based on Transformer architecture.Experiments on various datasets of natural language understanding verify the effectiveness of syntax trees and achieve consistent improvement over multiple pre-trained models, including BERT, RoBERTa, and T5.
Adapting natural language processing for technical text

Abstract Despite recent dramatic successes, natural language processing (NLP) is not ready to address a variety of real‐world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and large computational resource requirements, makes domain‐specific application of even the most successful NLP techniques difficult. This paper proposes technical language processing (TLP) which brings engineering principles and practices to NLP specifically for the purpose of extracting actionable information from language generated by experts in their technical tasks, systems, and processes. TLP envisages NLP as a socio‐technical system rather than as an algorithmic pipeline. We describe how the TLP approach to meaning and generalization differs from that of NLP, how data quantity and quality can be addressed in engineering technical domains, and the potential risks of not adapting NLP for technical use cases. Engineering problems can benefit immensely from the inclusion of knowledge from unstructured data, currently unavailable due to issues with out of the box NLP packages. We illustrate the TLP approach by focusing on maintenance in industrial organizations as a case‐study.
Exploring Factors Influencing Mobile Learning in Higher Education – A Systematic Review

&lt;p&gt;In order to facilitate effective learning experiences for the students, mobile learning (m-learning) has evolved as a powerful component of education – learning and teaching. M-learning, as opposed to electronic learning (e-learning) which uses small and portable devices along with laptops and desktops, uses mostly small and portable technological tools. M-learning assists in imparting knowledge focusing on the need of the learner, accessibility, infrastructure, and interaction, irrespective of the place and time. With rapid advancements in Information and Communication Technologies (ICT) and mobile devices, myriad applications (apps) and innovative m-learning and e-learning services are being developed and launched at an unprecedented pace. For students representing different demographics (age, education level, class, socioeconomic status, location, etc.) and enrolled in higher education, m-learning has quickly become the modern style and preferred format of learning and accessing knowledge for its ease of integrating different modes of learning. Motivated by this novel m-learning learning movement, this study synthesizes existing research on m-learning technologies, technological platforms that cater to both synchronous and asynchronous learning/teaching modalities for students and teachers at institutions of higher education.&lt;/p&gt;
NTIRE 2021 Challenge for Defocus Deblurring Using Dual-pixel Images: Methods and Results

This paper provides a review of the NTIRE 2021 challenge targeting defocus deblurring using dual-pixel (DP) data. The goal of this single-track challenge was to reduce spatially varying defocus blur present in images captured with a shallow depth of field. The images used in this challenge were obtained using a DP sensor that provided a pair of DP views per captured image. Submitted solutions were evaluated using conventional signal processing metrics, namely peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Out of 185 registered participants, nine teams provided methods and competed in the final stage. The paper describes the methods proposed by the participating teams and their results. The winning teams represent the state-of-the-art in terms of defocus de-blurring using DP images.
Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce

The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Signiﬁcantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a speciﬁc item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user inﬂuence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we ﬁrst study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Speciﬁcally, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Ofﬂine experiments demonstrate the superiority of the proposed HGSRec with signiﬁcant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.
Knowledge Distillation for Fast and Accurate Monocular Depth Estimation on Mobile Devices

Fast and accurate monocular depth estimation on mobile devices is a challenging task as one should always trade off the accuracy against the inference time. Most monocular depth methods adopt models with large computation overhead, which are not applicable on mobile devices. However, directly training a light-weight neural network to estimate depth can yield poor performance. To remedy this, we utilize knowledge distillation, transferring the knowledge and representation ability of a stronger teacher network to a light-weight student network. Experiments on Mobile AI 2021 (MAI2021) dataset demonstrate that our solution helps increase the fidelity of the output depth map and maintain fast inference speed. Specifically, with 94.7% less parameters than teacher network, the si-RMSE of student network only decrease by 10%. Moreover, our method ranks second in the MAI2021 Monocular Depth Estimation Challenge, with a si-RMSE of 0.2602, a RMSE of 3.25, and the inference time is 1197 ms tested on the Raspberry Pi 4.