Reviewerly/exHarmony huggingface dataset:
A novel dual-network architecture for mixed-supervised medical image segmentation

In medical image segmentation tasks, deep learning-based models usually require densely and precisely annotated datasets to train, which are time-consuming and expensive to prepare. One possible solution is to train with the mixed-supervised dataset, where only a part of data is densely annotated with segmentation map and the rest is annotated with some weak form, such as bounding box. In this paper, we propose a novel network architecture called Mixed-Supervised Dual-Network (MSDN), which consists of two separate networks for the segmentation and detection tasks respectively, and a series of connection modules between the layers of the two networks. These connection modules are used to extract and transfer useful information from the detection task to help the segmentation task. We exploit a variant of a recently designed technique called ‘Squeeze and Excitation’ in the connection module to boost the information transfer between the two tasks. Compared with existing model with shared backbone and multiple branches, our model has flexible and trainable feature sharing fashion and thus is more effective and stable. We conduct experiments on 4 medical image segmentation datasets, and experiment results show that the proposed MSDN model outperforms multiple baselines.
Time to Leak: Cross-Device Timing Attack On Edge Deep Learning Accelerator

Edge deep learning accelerators are optimised hardware to enable efficient inference on the edge. The models deployed on these accelerators are often proprietary and thus sensitive for commercial and privacy reasons. In this paper, we demonstrate practical vulnerability of deployed deep learning models to timing side-channel attacks. By measuring the execution time of the inference, the adversary can determine and reconstruct the model from a known family of well known deep learning model and then use available techniques to recover remaining hyperparameters. The vulnerability is validated on Intel Compute Stick 2 for VGG and ResNet family of models. Moreover, the presented attack is quite devastating as it can be performed in a cross-device setting, where adversary profiles constructed on a legally own device can be used to exploit the victim device with a single query and still can achieve near perfect success rate.
Posture Estimation and Optimization in Ergonomically Intelligent Teleoperation Systems

Ergonomics and human comfort are essential concerns in physical human-robot interaction~(p-HRI) applications such as teleoperation. We introduce a novel framework for posture estimation and optimization for ergonomically intelligent teleoperation systems that estimates the human operator's posture solely from the leader robot's trajectory and provides online postural correction and offline initial posture correction according to the type of teleoperation task. Although our framework is in teleoperation, it can be extended to other p-HRI applications with minimal modifications.
Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.
Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis

The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health.This swift spread has caused the public to panic and look to scientists for answers.Fortunately, these scientists already have a wealth of data-the Covid-19 reports that each country releases, reports with valuable spatialtemporal properties.These data point toward some key actions that humans can take in their fight against Covid-19.Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure.Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data.Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities.Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data.Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States.Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE.We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series.Moreover, the prediction can assist in improving containment policies.
MSDU-Net: A Multi-Scale Dilated U-Net for Blur Detection

A blur detection problem which aims to separate the blurred and clear regions of an image is widely used in many important computer vision tasks such object detection, semantic segmentation, and face recognition, attracting increasing attention from researchers and industry in recent years. To improve the quality of the image separation, many researchers have spent enormous efforts on extracting features from various scales of images. However, the matter of how to extract blur features and fuse these features synchronously is still a big challenge. In this paper, we regard blur detection as an image segmentation problem. Inspired by the success of the U-net architecture for image segmentation, we propose a multi-scale dilated convolutional neural network called MSDU-net. In this model, we design a group of multi-scale feature extractors with dilated convolutions to extract textual information at different scales at the same time. The U-shape architecture of the MSDU-net can fuse the different-scale texture features and generated semantic features to support the image segmentation task. We conduct extensive experiments on two classic public benchmark datasets and show that the MSDU-net outperforms other state-of-the-art blur detection approaches.
Overview of the Dialogue Breakdown Detection Challenge 4

To promote the research and development of dialogue breakdown detection for dialogue systems, we have been organizing a series of dialogue breakdown detection challenges to detect a system's inappropriate utterances that lead to dialogue breakdowns in chat-oriented dialogue. In this paper, we overview Dialogue Breakdown Detection Challenge 4 (DBDC4). As in the previous challenges, we used datasets in English and Japanese. Four teams participated in the challenge, in which all four teams worked on English, and two of the four teams worked on Japanese as well. This paper describes the task setting, evaluation metrics, and datasets for the challenge and the results of the submitted runs of the participants.
CBCT‐based navigation system for open liver surgery: Accurate guidance toward mobile and deformable targets with a semi‐rigid organ approximation and electromagnetic tracking of the liver

Purpose The surgical navigation system that provides guidance throughout the surgery can facilitate safer and more radical liver resections, but such a system should also be able to handle organ motion. This work investigates the accuracy of intraoperative surgical guidance during open liver resection, with a semi‐rigid organ approximation and electromagnetic tracking of the target area. Methods The suggested navigation technique incorporates a preoperative 3D liver model based on diagnostic 4D MRI scan, intraoperative contrast‐enhanced CBCT imaging and electromagnetic (EM) tracking of the liver surface, as well as surgical instruments, by means of six degrees‐of‐freedom micro‐EM sensors. Results The system was evaluated during surgeries with 35 patients and resulted in an accurate and intuitive real‐time visualization of liver anatomy and tumor's location, confirmed by intraoperative checks on visible anatomical landmarks. Based on accuracy measurements verified by intraoperative CBCT, the system’s average accuracy was 4.0 ± 3.0 mm, while the total surgical delay due to navigation stayed below 20 min. Conclusions The electromagnetic navigation system for open liver surgery developed in this work allows for accurate localization of liver lesions and critical anatomical structures surrounding the resection area, even when the liver was manipulated. However, further clinically integrating the method requires shortening the guidance‐related surgical delay, which can be achieved by shifting to faster intraoperative imaging like ultrasound. Our approach is adaptable to navigation on other mobile and deformable organs, and therefore may benefit various clinical applications.
Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.
Improving Machine Learning-Based Code Smell Detection via Hyper-Parameter Optimization

Unlike code errors, the presence of code smell often does not affect the behavior of the software system, but it will cause quality problems in terms of readability, understandability, and efficiency. To improve the software quality and reduce the maintenance costs, the developers need to detect code smells rapidly and make corresponding code refactoring. In code smell detection, recently, machine learning-based methods become more prevalent and can overcome the shortcomings of the heuristic-based methods, which mainly rely on manually designed rules. However, to our best knowledge, there is little research to analyze whether using hyper-parameter optimization can improve the performance of machine learning-based methods. In this study, we mainly focus on two classical code smells (i.e., Data Class and Feature Envy). First, we consider four optimizers for hyper-parameter optimization, and six commonly used classifiers for machine-learning-based methods. Second, we use AUC as the performance measure to evaluate the performance of constructed models. Based on final empirical results, we find that (1) Using hyper-parameter optimization can significantly improve the performance of code smell detection. (2) Differential evolution (DE) optimizer can achieve better performance than the other three optimizers when using the random forest classifier. (3) We can further improve the performance of code smell detection when performing parameter optimization on the DE optimizer.
Design of computer network security defense system based on artificial intelligence and neural network

In recent years, the application of network technology has become popular, and the application of the Internet has occupied a large proportion in people’s daily life. The issue of network security has also begun to be paid attention to. In recent years, due to the rapid expansion of network applications, malicious events such as network threats have occurred from time to time. Many computers do not have a good protection structure so that they are often vulnerable to attacks during network interconnection. Traditional computer protection measures are border-based protection, but with the development of network technology, this protection system can no longer guarantee computer security. Therefore, in order to keep computers safe in the current network environment, the role of artificial intelligence in the computer field should be given full play. The artificial intelligence analysis system can analyze and predict the situation of computer network security based on the situation of network security. This paper integrates convolutional neural network algorithms on the basis of traditional machine learning to establish a new network intrusion model. This paper verifies the feasibility of the model through experiments, and the experimental results show that the accuracy of the new model proposed in this paper can reach more than 90% for KDDCUP99 data detection. In addition, traditional computer protection systems have many errors when dealing with DNN attack detection. In order to reduce the occurrence of this situation, this paper proposes a standardized attack detection model based on deep nerves. The detection precision of this model is higher and the results obtained are more accurate. In addition, this new model can also synthesize the impact of different network attacks on the security situation, and construct attack situation predictions for computer systems.
Virtual channel of multidimensional reconciliation in a continuous-variable quantum key distribution

The well-known multidimensional reconciliation is a significant stage of a continuous-variable quantum key distribution (CVQKD) system, which uses $d$-dimensional rotations to build a virtual channel between authenticated parties, Alice and Bob. Generally, a block low-density parity-check code with a belief-propagation (BP) iterative decoding algorithm, which is optimized for an additive white Gaussian noise channel, is always used in multidimensional reconciliation for the CVQKD system. In this paper, we study the signal-to-noise ratio (SNR) of the virtual channel of multidimensional reconciliation in CVQKD and prove that the noise of the virtual channel follows the Student's $t$-distribution. Therefore, we propose a $t$-BP decoding algorithm, which can be better applied to multidimensional reconciliation. Simulation results show that the frame error rate (FER) with the proposed $t$-BP decoding algorithm is superior to that with the conventional BP decoding algorithm applied to multidimensional reconciliation. Subsequently, the FER improvement results in significant influence on reconciliation efficiency and secret key rate of the CVQKD system.
Graph neural network: Current state of Art, challenges and applications

Several areas in science and engineering have the relationships between their underlying data which can be represented as graphs, for example, molecular chemistry, node prediction, link prediction, computer vision, pattern recognition, social networking and more. In this article, an approach to a model which can handle such type of data is elaborated, which is Graph Neural Networks (GNN). GNN encompasses the neural network technique to process the data which is represented as graphs. Due to its massive success, GNN has made its way into many applications and is a popular architecture to work upon. This paper explains the graph neural networks, its area of applications and its day-to-day use in our daily lives. Some of the very common application is a social networking site which is on our hands regularly, and another could be the recommendation system which recommends us friends, or the products of our interest based on our pat choices and preferences. This paper also demonstrates the basic challenges encountered while implementing GNN. This paper will be a great help to those researchers who are keen to work in the domain of GNN.
Revealing Development Trends in Blockchain-Based 5G Network Technologies through Patent Analysis

The fifth-generation (5G) network has special communication and security requirements including high reliability, low latency, precise automatic control, secure covert transmission, and evidence traceability. The 5G network combined with blockchain technology just meets this demand, so it is driving a rapidly growing volume of patent applications. This study proposes application scenarios, architecture diagrams, and patent analysis methods for blockchain-based 5G network technologies, beginning with a network architecture using mobile edge computing (MEC) and blockchain as independent platform components to solve MEC load pressure. In the patent analysis, a patent cluster map of blockchain-based 5G networks is proposed to analyze the intersection of technical application fields. The bottleneck period of technological development is presented for leading countries and enterprises in the technological development of blockchain-based 5G network, highlighting relative advantages and disadvantages. Specifically, to extract the core international patent classification (IPC) key technologies and their mutual interrelatedness, we use network topology analysis to establish an IPC network topology diagram through node global and local topology characteristics, thus revealing hotspots of IPC technology research and the characteristics of the technology relationship system. The findings provide a very useful reference for the formulation of government strategy to assist in the implementation and development of blockchain-based 5G network technologies for future smart cities.
Blockchain, Cryptocurrency, and Artificial Intelligence in Finance

This chapter describes the principles of blockchain, cryptocurrency, and artificial intelligence (AI) and their applications to the financial sector. We first discuss blockchain, and discuss cryptocurrency, the best-known application of blockchain. We present the question of whether a cryptocurrency is a currency or an asset and whether it can be a new safe haven asset. We summarize the controversy regarding the issuance of a central bank digital currency (CBDC). We argue that digital currencies only show the potential to inject liquidity into an economy during market stress. Additionally, most of the recognized advantages of blockchain applications relate to two concepts: decentralization and consensus. Blockchain's decentralization can be used to democratize banking services, corporate governance, and the real estate industry. Finally, we present the strengths of and concerns in using AI technologies in banking, lending platforms, and asset management, bearing in mind the most recently developed applications in these areas. This chapter provides a contribution to the literature that incorporates both theory and practice in blockchain, presenting a detailed review of performances and limitations of AI techniques in finance, including recent publications relating to the COVID-19 pandemic, CBDC, and alternative data.
Enabling Generic Verifiable Aggregate Query on Blockchain Systems

Currently, users in a blockchain system must maintain all the data on the blockchain and query the data locally to ensure the integrity of the query results. However, since data is updated in an append-only way, resulting in a huge amount of data, it will take considerable maintenance costs to users. In this paper, we present an approach to support verifiable aggregate queries on blockchain systems that alleviates both storage and computing costs for users, while ensuring the integrity of the query results. We design an accumulator-based authenticated data structure (ADS) that supports verifiable multidimensional aggregate queries (i.e., aggregate queries with multiple selection predicates). The structure is built for each block, based on which verifiable multidimensional aggregate queries within a single block or involving multiple blocks are supported. We further optimize the performance by merging ADSs on different blocks to reduce the verification time at the client side and reduce the verification object (VO) size. Extensive experiments demonstrate the effectiveness and efficiency of our proposed approach.
Automatic Title Generation for Text with Pre-trained Transformer Language Model

In this paper, we propose a novel approach to Automatic Title Generation for a given text using a pre-trained Transformer Language Model GPT-2. The model proposes an unique approach of generating a pool of candidate titles and selecting an appropriate title among them which is then refined or de-noised to get the final title. The approach consists of a pipeline of three modules namely Generation, Selection and Refinement followed by a Scoring function. The Generation and Refinement modules are based on GPT-2, while the Selection module has a heuristic based approach. The model is able to generate accurate titles in spite of having a smaller corpus of relevant training data due to the fact that the natural language generation capabilities come from the pre-training while the model has to primarily learn task and corpus specific nuances. Additionally, Selection and Refinement modules ensure that the titles are representative of the given text and are semantically and syntactically accurate. We train our model for research paper abstracts from arXiv and evaluate it on three different test sets. Our pipeline shows promising results when evaluated on ROUGE and BLEU metrics against the test sets. In addition, we also perform human evaluation for validating the results generated by our proposed approach.