Reviewerly/exHarmony huggingface dataset:
Breast Cancer Histopathological Image Classification Based on Deep Second-order Pooling Network

With the breakthrough performance in a variety of computer vision and medical image analysis problems, convolutional neural networks (CNNs) have been successfully introduced for the classification task of breast cancer histopathological images in recent years. Nevertheless, existing breast cancer histopathological image classification networks mainly utilize the first-order statistic information of deep features to represent histopathological images, failing to characterize the complex global feature distribution of breast cancer histopathological images. To address the problem, this work makes a first attempt to explore global second-order statistics of deep features for the above task. More specifically, we propose a novel deep second-order pooling network (DSoPN) for breast cancer histopatho-logical image classification, in which a robust global covariance pooling module based on matrix power normalization (MPN) is embedded into a simple yet effective CNN architecture. The given DSoPN model can capture richer second-order statistical information of deep convolutional features and produce more informative global representations for breast cancer histopatho-logical images. Experimental results on the public BreakHis dataset illuminate the promising performance of the second-order pooling for breast cancer histopathological image classification. Besides, our DSoPN achieves very competitive performance compared to the state-of-the-art methods.
Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline

Predicting the progression of preclinical Alzheimer’s disease (AD) such as subjective cognitive decline (SCD) is fundamental for the effective intervention of pathological cognitive decline. Even though multimodal neuroimaging has been widely used in automated AD diagnosis, there are few studies dedicated to SCD progression prediction, due to challenges of incomplete and limited data. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework with transfer learning for SCD conversion prediction using incomplete multimodal neuroimaging data. Specifically, JSRL consists of two major components: 1) a generative adversarial network for synthesizing missing neuroimaging data, and 2) a classification network for learning neuroimage representations and predicting the progression of SCD. These two subnetworks share the same feature encoding module, encouraging that the to-be-generated representations are prediction-oriented and also the underlying association among multimodal images can be effectively modeled for accurate prediction. To handle the limited data problem, we further leverage both image synthesis and prediction models learned from a large-scale ADNI database (with MRI and PET acquired from 863 subjects) to a small-scale SCD database (with only MRI acquired from 113 subjects) in a transfer learning manner. Experimental results show that the proposed JSRL can synthesize reasonable PET scans and is superior to several state-of-the-art methods in SCD conversion prediction.
An artificial intelligence agent technology based web distance education system

Based on the analysis of the characteristics of artificial intelligence and agent, this paper discusses the feasibility of introducing Web services and intelligent agent technology into online teaching and learning and proposes a modern distance education system model based on artificial intelligence agent technology web. The architecture integrates the advantages of Agent technology and Web services. Starting from improving the shortcomings of the traditional Web-based distance teaching system, it strives to increase learners’ self-directed learning interest, monitor students’ emotions, and exchange knowledge between teaching agents. To realize students’ on-demand learning according to their aptitude, teachers’ teaching ultimately improve the system’s flexibility, personalization, and artificial intelligence. Under the guidance of learning communities and other theories, construct learner model ontology inference rules. Based on the learner’s relationship characteristics, the knowledge domain that the learner is interested in is inferred, thus constructing an intelligent information retrieval system. Knowledge retrieval is realized quickly and accurately, thereby verifying the application of learner relationship characteristics in digital learning.
A Task Scheduling Approach for Cloud Resource Management

As an emerging subfield of computer science, cloud computing is increasingly relevant to both academia and numerous industries as a solution to high barriers of entry in configuring and maintaining computing hardware and inflexible platform constraints. One vital aspect of cloud computing is task scheduling, which deals with the strategies in assigning tasks to computing resources. There are numerous commonly used task scheduling algorithms defined by their performance trade-off among various factors such as cutting down the completion time and increasing throughput. In this paper, an algorithm called BMin is designed to augment the performance for algorithm Min-min. Using the CloudSim package as a framework for simulating cloud processes, we conduct our experiments, yielding experimental results that demonstrate decreased completion time, increased throughput, and improved load balancing of resources—outperforming the classical algorithm.
Unsupervised Learning for Parametric Optimization

This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers of parameters. Such unsupervised training, which consists in repeatedly sampling parameter values and performing stochastic gradient descent, foregoes the taxing precomputation of labeled training data that supervised learning necessitates. As an example of application, we put this technique to use on a rather general constrained quadratic program. Follow-up letters subsequently apply it to more specialized wireless communication problems, some of them nonconvex in nature. In all cases, the performance of the proposed procedure is very satisfactory and, in terms of computational cost, its scalability with the problem dimensionality is superior to that of convex solvers.
SQVDT: A scalable quantitative vulnerability detection technique for source code security assessment

Summary Vulnerability detection and exploit is becoming a very important part of security, especially in malware code delivery, hacking a system, efforts to create patches, improving the source code, or updating a software. Vulnerabilities in applications, including browsers, media players, online services, document readers, and so forth. are often exploited and cause a serious damage. In this article, we propose a vulnerability detection technique to detect vulnerabilities in software, as well as shared libraries at source code level. We crawl the vulnerable source code by tracing and locating the patch files from different web sources according to their CVE‐numbers and built a fingerprint index of 2931 vulnerable files. Then we developed a vulnerability detection approach based on code clone detection technique and detect hundreds of vulnerabilities in thousands of GitHub open source projects, which are not noticed before as vulnerable. We detected vulnerabilities in some very famous recently available software, including latest version of Linux, HTC‐kernel, FindX‐8.1‐kernel, and in 7‐TB of C/C++ source code (152,823 open source projects). In this study, we discuss some of the very high severity level (CVSS) vulnerabilities that are detected by our approach. Furthermore, we performed an empirical evaluation and verification on these vulnerabilities, including intraproject clone vulnerabilities, copied‐kernel clone vulnerabilities, and library‐used clone vulnerabilities. Our technique is very fast, efficient, reliable, practical, scalable, and can be implemented at industrial level. The comparison with the state‐of‐the‐art tools shows the effectiveness of our approach.
AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

Sarcasm, though difficult to define but plays a crucial role in one’s life. Sarcasm as a jest is a matter of fun but when taken seriously can cause unwelcoming results. Sometimes, sarcasm is defined as “a sharp, bitter, or cutting expression or remark; a bitter jibe or taunt”. These days’ researchers are working towards the detection of sarcasm for the purpose of sentiment analysis. Emotion and sentiment-bearing information are carried by subjective sarcastic sentences. The objective of the paper is to highlight the different types of sarcastic tweets and their usage in sentiment analysis. The authors mainly emphasize several approaches which include sentiment analysis, machine and deep learning classifications. The paper focuses on the use of machine learning and deep learning for identifying sarcastic tweets. Numerous feature extraction techniques have been studied and machine and deep learning classifications have been taken into account. The comparative table shows the results obtained using the various evaluation metrics such as accuracy, precision, recall, and f-score.
Cross-modal Feature Alignment based Hybrid Attentional Generative Adversarial Networks for text-to-image synthesis

With the development of the generative model, image synthesis has become a research hotspot. This paper presents a novel Cross-modal Feature Alignment based Hybrid Attentional Generative Adversarial Networks (CFA-HAGAN) for text-to-image synthesis. It mainly consists of two steps, text-image encoding and text-to-image synthesis. Text-image encoding learns a Cross-modal Feature Alignment Model (CFAM), which adopts a fine-grained attentional network to learn the original multi-modalities' aligned features. The feature alignment space is viewed as the transitional space in the whole process. Then, the Hybrid Attentional Generative Adversarial Networks (HAGAN) learns the inverse mapping from the encoded text feature to the original image. Specifically, the hybrid attention block consists of text-image cross-modal attention mechanism and self-attention mechanism of an image. Cross-modal attention makes the synthesized image fine-grained by adding word-level information as additional supervision. Self-attention can solve the long-distance reliance problem of image sub-region features when synthesizes images from the hidden feature. Although excellent performance in an ocean of tasks, GANs are well-known for the difficulty of training. Adopting spectral normalization, the discriminators are satisfied with 1-Lipschitz constraint, which makes their training process more stable than original GANs. During quantitative and non-quantitative comparison with many state-of-the-art methods, the experimental results show that the proposed method achieves better performance on evaluation metric and visual effect. Besides, the experimental section presents attention visualization, ablation study, and generalization ability analysis to show the effectiveness of the proposed method.
Enabling Efficient, Secure and Privacy-Preserving Mobile Cloud Storage

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this article, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) protocol as the underlying primitive of the proposed mobile cloud storage scheme. OSU is based on onion additively homomorphic encryption with constant encryption layers and enables the client to obliviously retrieve an encrypted data item from the cloud and update it with a fresh value by generating a small encrypted vector, which significantly reduces the client&#x2019;s computation as well as the communication overheads. Compared with previous works, our presented work has valuable properties, such as fine-grained data structure (small item size), lightweight client-side computation (a few of additively homomorphic operations) and constant communication overhead, which make it more suitable for MCS scenario. Moreover, by employing the &#x201C;verification chunks&#x201D; method, our scheme can be verifiable to resist malicious cloud. The comparison and evaluation indicate that our scheme is more efficient than existing oblivious storage solutions with the aspects of client and cloud workloads, respectively.
Human Motion Capture Based on Kinect and IMUs and Its Application to Human-Robot Collaboration

In this paper, a nonlinear optimization solver is proposed to establish the human upper body motion capture system, including the orientation of each bone joint and the global position of the hip bone. 5 IMUs and one Kinect are used, the rotation data and acceleration data of IMUs, Kinect skeleton position data and human pose prior are fused in a non-linear optimization way. The method proposed in this paper combines the characteristics of Kinect data with high accuracy and IMU data with stability, which can make up for the accuracy of human pose capture in dynamic, magnetically disturbed and occlusion conditions. In order to verify the practicability of the human motion capture system proposed in this paper, the system is firstly applied to real-time human motion capture, then a simple human-robot collaboration (HRC) case is implemented, which is very common in modern collaborative medical field. The experimental results show that the proposed human motion capture system can achieve stable human motion measurement, which can meet the needs of HRC scenarios.
Active 3D Modeling via Online Multi-View Stereo

Multi-view stereo (MVS) algorithms have been commonly used to model large-scale structures. When processing MVS, image acquisition is an important issue because its reconstruction quality depends heavily on the acquired images. Recently, an explore-then-exploit strategy has been used to acquire images for MVS. This method first constructs a coarse model by exploring an entire scene using a pre-allocated camera trajectory. Then, it rescans the unreconstructed regions from the coarse model. However, this strategy is inefficient because of the frequent overlap of the initial and rescanning trajectories. Furthermore, given the complete coverage of images, MVS algorithms do not guarantee an accurate reconstruction result.In this study, we propose a novel view path-planning method based on an online MVS system. This method aims to incrementally construct the target three-dimensional (3D) model in real time. View paths are continually planned based on online feedbacks from the partially constructed model. The obtained paths fully cover low-quality surfaces while maximizing the reconstruction performance of MVS. Experimental results demonstrate that the proposed method can construct high quality 3D models with one exploration trial, without any rescanning trial as in the explore-then-exploit method.
Online Learning of Object Representations by Appearance Space Feature Alignment

We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful for robotics. The main contributions of this paper are: 1) a self-supervised model called Object-Contrastive Network (OCN) that can discover and disentangle object attributes from video without using any labels; 2) we leverage self-supervision for online adaptation: the longer our online model looks at objects in a video, the lower the object identification error, while the offline baseline remains with a large fixed error; 3) we show the usefulness of our approach for a robotic pointing task; a robot can point to objects similar to the one presented in front of it. Videos illustrating online object adaptation and robotic pointing are provided as supplementary material.
Deep Active Learning for Breast Cancer Segmentation on Immunohistochemistry Images

Immunohistochemistry (IHC) plays an essential role in breast cancer diagnosis and treatment. Reliable and automatic segmentation of breast cancer regions on IHC images would be of considerable value for further analysis. However, the prevalent fully convolutional networks (FCNs) suffer from difficulties in obtaining sufficient annotated training data. Active learning, on the other hand, aims to reduce the cost of annotation by selecting an informative and effective subset for labeling. In this paper, we present a novel deep active learning framework for breast cancer segmentation on IHC images. Three criteria are explicitly designed to select training samples: dissatisfaction, representativeness and diverseness. Dissatisfaction, consisting of both pixel-level and image-level dissatisfaction, focuses on selecting samples that the network does not segment well. Representativeness chooses samples that can mostly represent all the other unlabeled samples and diverseness further makes the chosen samples different from those already in the training set. We evaluate the proposed method on a large-scale in-house breast cancer IHC dataset and demonstrate that our method outperforms the state-of-the-art suggestive annotation (SA) [] and representative annotation (RA) [] on two test sets and achieves competitive or even superior performance using 40% of training data to using the full set of training data.
A Blockchain-Based OCF Firmware Update for IoT Devices

As the usage growth rate of Internet of Things (IoT) devices is increasing, various issues related to these devices need attention. One of them is the distribution of the IoT firmware update. The IoT devices’ software development does not end when the manufacturer sells the devices to the market. It still needs to be kept updated to prevent cyber-attacks. The commonly used firmware update process, over-the-air (OTA), mostly happens in a centralized way, in which the IoT devices directly download the firmware update from the manufacturer’s server. This central architecture makes the manufacturer’s server vulnerable to single-point-of-failure and latency issues that can delay critical patches from being applied to vulnerable devices. The Open Connectivity Foundation (OCF) is one organization contributing to providing interoperability services for IoT devices. In one of their subject areas, they provide a firmware update protocol for IoT devices. However, their firmware update process does not ensure the integrity and security of the patches. In this paper, we propose a blockchain-based OCF firmware update for IoT devices. Specifically, we introduce two types of firmware update protocol, direct and peer-to-peer updates, integrated into OCF firmware update specifications. In the direct scenario, the device, through the IoT gateway, can download the new firmware update from the manufacturer’s server. Meanwhile, in the peer-to-peer scheme, the device can query the update from the nearby gateways. We implemented our protocol using Raspberry Pi hardware and Ethereum-based blockchain with the smart contracts to record metadata of the manufacturer’s firmware updates. We evaluated the proposed system’s performance by measuring the average throughput, the latency, and the firmware update distribution’s duration. The analysis results indicate that our proposal can deliver firmware updates in a reasonable duration, with the peer-to-peer version having a faster completion time than the direct one.
Classifying Degraded Images Over Various Levels Of Degradation

Classification for degraded images having various levels of degradation is very important in practical applications. This paper proposes a convolutional neural network to classify degraded images by using a restoration network and an ensemble learning. The results demonstrate that the proposed network can classify degraded images over various levels of degradation well. This paper also reveals how the image-quality of training data for a classification network affects the classification performance of degraded images.
Learned Sampling Distributions for Efficient Planning in Hybrid Geometric and Object-Level Representations

We would like to enable a robotic agent to quickly and intelligently find promising trajectories through structured, unknown environments. Many approaches to navigation in unknown environments are limited to considering geometric information only, which leads to myopic behavior. In this work, we show that learning a sampling distribution that incorporates both geometric information and explicit, object-level semantics for sampling-based planners enables efficient planning at longer horizons in partially-known environments. We demonstrate that our learned planner is up to 2.7 times more likely to find a plan than the baseline, and can result in up to a 16% reduction in traversal costs as calculated by linear regression. We also show promising qualitative results on real-world data.
High-Order Attention Networks for Medical Image Segmentation

Segmentation is a fundamental task in medical image analysis. Current state-of-the-art Convolutional Neural Networks on medical image segmentation capture local context information using fixed-shape receptive fields and feature detectors with position-invariant weights, which limits the robustness to the variance of input, such as medical objects of variant sizes, shapes, and domains. In order to capture global context information, we propose High-order Attention (HA), a novel attention module with adaptive receptive fields and dynamic weights. HA allows each pixel to has its own global attention map that models its relationship to all other pixels. In particular, HA constructs the attention map through graph transduction and thus captures high relevant context information at high-order. Consequently, feature maps at each position are selectively aggregated as a weighted sum of feature maps at all positions. We further embed the proposed HA module into an efficient encoder-decoder structure for medical image segmentation, namely High-order Attention Network (HANet). Extensive experiments are conducted on four benchmark sets for three tasks, i.e., REFUGE and Drishti-GS1 for optic disc/cup segmentation, DRIVE for blood vessel segmentation, and LUNA for lung segmentation. The results justify the effectiveness of the new attention module for medical image segmentation.