Reviewerly/exHarmony huggingface dataset:
Classification of Mammography based on Semi-supervised Learning

In recent years, deep learning, especially the convo-lutional neural network (CNN), has been widely used in the field of molybdenum target image classification. Generally speaking, the CNN based methods train the network with a huge number of samples in a supervised way. However, it is too expensive to collect such a large-scale labeled mammogram dataset for training. In this paper, we propose a semi-supervised mammography classification approach to address this issue. Compared with the existing method, our method can simultaneously use the labeled and unlabeled samples to train the mammogram classification network. Specifically, for the breast mass images represented by Mediolateral Oblique (MLO) and Craniocaudal (CC) views, a new classification network is designed to extract features from both views simultaneously. The compact bilinear pooling (CBP) is introduced to extract the fine-grained features of breast mass images. Moreover, considering that the samples near the decision boundary are easy to be misclassified and those misclassified samples are harmful to the model training, we develop a K-Means based strategy, which first clusters the unlabeled samples and then removes those samples far away from the clustering center during model training. The method proposed in this paper is compared with the mainstream deep learning classification methods and semi-supervised methods on the Digital Database for Screening Mammography (DDSM) public mammogram database. The experimental results show that the method proposed is superior to most of the compared methods in terms of processing time and classification performance.
Geospatial Object Detection with Single Shot Anchor-Free Network

Geospatial object detection has made considerable progress with the use of anchor-based object detectors. In such a situation, the detection performance relies heavily on the parameter settings of anchor boxes. We present a Single Shot Anchor-Free Network (SSAFNet) to tackle with this problem. By eliminating the anchor boxes, the SSAFNet completely avoids the carefully predefined anchor boxes parameters and the computation for adapting the huge scale variation of geospatial objects. A compositional attention network is further introduced to enhance the saliency of foreground objects. We evaluate the SSAFNet on the representative NWPU VHR-10 and RSOD datasets, achieving competitive performance with state-of-the-art anchor-based detection methods.
The balance property in neural network modelling

In estimation and prediction theory, considerable attention is paid to the question of having unbiased estimators on a global population level. Recent developments in neural network modelling have mainly focused on accuracy on a granular sample level, and the question of unbiasedness on the population level has almost completely been neglected by that community. We discuss this question within neural network regression models, and we provide methods of receiving unbiased estimators for these models on the global population level.
Clustering Mixed-Type Data: A Benchmark Study on KAMILA and K-Prototypes

Benchmarking in cluster analysis is the process of analyzing which clustering techniques give the best result for different types of data structures as well as setting a standard for evaluation of newer clustering methods. There are many instances of benchmarking in cluster analysis for continuous data, but only a few for mixed-type data, i.e. data sets with nominal and continuous variables. Therefore, we explore the process for benchmarking various clustering methods on simulated mixed-type data sets with varying proportions of continuous and nominal variables. For this purpose, we test a newer clustering algorithm, KAMILA, against K-prototypes and tandem analysis where data are preprocessed using multiple correspondence analysis and then clustered using K-means, fuzzy K-means, probabilistic distance clustering (PD), and Student-t mixture models.
Kernel local outlier factor‐based fuzzy support vector machine for imbalanced classification

Abstract The problem of imbalanced data classification has become a research hotspot in the field of machine learning. Fuzzy support vector machine (FSVM) is an imbalanced classification processing method based on cost‐sensitive theory. The existing methods have cost‐sensitive, causing the prior distribution estimation of data inaccurate. This article proposes a novel FSVM algorithm based on the kernel local outlier factor (KLOF‐FSVM) for this problem. KLOF calculates the local outlier factor of the sample in the kernel space and assigns an appropriate membership value to the sample. This process enables the algorithm to obtain the distribution information of the data better. Compared with the algorithm based on distance only, KLOF has better robustness. It can expand the value range of majority class samples' membership degree to better balance the important relation between the minority class and the majority class. We selected some datasets in the Keel data repository and used cross‐validation to obtain the algorithm's effect under different evaluation indexes such as G ‐Mean, F 1 measure, and area under curve. By comparing with other algorithms, preliminary results show that this method has better classification quality.
Measuring Roaming in Europe: Infrastructure and Implications on Users’ QoE

“Roam like Home” is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming (HR). This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20 percent. We further analyze in isolation the impact of latency on QoE metrics and find that the penalty imposed by HR leads to a degradation on QoE metrics up to 150 percent in case of intercontinental roaming.
How Much Your Cloud Management Platform Is Secure? OpenStack Use Case

Cloud computing still one of the most hyped IT innovations. It envisages a world where components can be rapidly released, implemented, and scaled up and down providing an on-demand utility-like model of allocation and consumption. Moreover, Cloud Management Platform CMP is considered one of its typical components. It is a software product that deploys and manages a Cloud infrastructure. OpenStack, as the most widely adopted platform, has got more and more attention. It aims to be competitive compared to other platforms, like Amazon Web Services (AWS). Furthermore, the adoption of cloud solutions introduces security and privacy concerns. OpenStack is no exception, and security concerns are present in its lifecycle which makes its security analysis a crucial mission. Therefore, this paper firstly presents a state of the art of OpenStack components, sub-components, and their interaction. Then, it focuses principally on an analysis of the most common vulnerabilities affected by OpenStack. The analysis is based on ten years of security reports. Our work leads to have a good comprehension of the OpenStack project, identify its vulnerability trends, and characterize comprehensively its security issues.
Data-Agnostic Local Neighborhood Generation

Synthetic data generation has been widely adopted in software testing, data privacy, imbalanced learning, machine learning explanation, etc. In such contexts, it is important to generate data samples located within “local” areas surrounding specific instances. Local synthetic data can help the learning phase of predictive models, and it is fundamental for methods explaining the local behavior of obscure classifiers. The contribution of this paper is twofold. First, we introduce a method based on generative operators allowing the synthetic neighborhood generation by applying specific perturbations on a given input instance. The key factor consists in performing a data transformation that makes applicable to any type of data, i.e., data-agnostic. Second, we design a framework for evaluating the goodness of local synthetic neighborhoods exploiting both supervised and unsupervised methodologies. A deep experimentation shows the effectiveness of the proposed method.
Deep NLP-based co-evolvement for synthesizing code analysis from natural language

This paper presents Deepsy, a Natural Language-based synthesizer to assist source code analysis. It takes English descriptions of to-be-found code patterns as its inputs, and automatically produces ASTMatcher expressions that are directly usable by LLVM/Clang to materialize intended code analysis. The code analysis domain features profuse complexities in data types and operations, which make it elusive for prior rule-based synthesizers to tackle. On the other hand, machine learning-based solutions are neither applicable due to the scarcity of well labeled examples. This paper presents how Deepsy addresses the challenges by leveraging deep Natural Language Processing (NLP) and creating a new technique named dependency tree-based co-evolvement.Deepsy features an effective design that seamlessly integrates Natural Language dependency analysis into code analysis and meanwhile synergizes it with type-based narrowing and domain-specific guidance. Deepsy achieves over 70.0% expression-level accuracy and 85.1% individual API-level accuracy, significantly outperforming previous solutions.
Inbetweening auto-animation via Fokker-Planck dynamics and thresholding

We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the inbetweening transformations starting from an initial image to an equilibrium image, which covers images varying from a greyscale type to a colorful type on plane or manifold. The algorithm is based on Fokker-Planck dynamics on manifold, which automatically cooperates positivity, unconditional stability, mass conservation law, exponentially convergence and also the manifold structure suggested by dataset. The thresholding scheme is adapted for the sharp interface dynamics and is used to achieve the finite time convergence. Using EDDA, three challenging examples, (I) facial aging process, (II) coronavirus disease 2019 (COVID-19) invading/treatment process, and (III) continental evolution process are conducted efficiently.
Drift Detection Analytics for IoT Sensors

The Industrial Internet of Things (IoT) has an unique opportunity to have a greater impact on the manufacturing sector. Monitoring the health of the expensive equipments in the factory is critical for the business and the opportunities where IoT can be truly transformational. Most often the industries operates on a primitive way of monitoring these equipments on a Statistical Process Control (SPC) Limits. The major flaw in this monitoring system is unable to detect drifts within the static limit and upon triggering of the limits, it is usually too late for the team in the manufacturing floor to take preventive actions before the system goes down. In this paper, we developed a generic model for detecting drifts and identifying potential outliers. The model uses a double linear regression method to identify both aggressive and progressive drift, as well as adjusted boxplot method to detect outliers in both symmetric and skewed distributions. Unlike conventional drift detection approaches, this model has low computational complexity and can be applied to both batch and stream data. This paper will also introduce the infrastructure and architecture on enabling near real-time analytics using the IoT platform and streaming cluster, which reduces the data latency available for analysis to 10 minutes. Enabling real-time monitoring allows the end users to react to the alarms in a timely manner. This system has proven that it is able to provide early detection before the impact was observed as compared to the existing system. Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.
A Hybrid Data Access Control Using AES and RSA for Ensuring Privacy in Electronic Healthcare Records

In the current scenario, the data owners would like to access data from anywhere and anytime. Hence, they will store their data in public or private cloud along with encryption and particular set of attributes to access control on the cloud data. While uploading the data into public or private cloud they will assign some attribute set to their data. If any authorized cloud user wants to download their data they should enter that particular attribute set to perform further actions on the data owner's data. A cloud user wants to register their details under cloud organization to access the data owner's data. Users wants to submit their details as attributes along with their designation. Based on the Users details Semi-Trusted Authority generates decryption keys to get control on owner's data. A user can perform a lot of operation over the cloud data. If the user wants to read the cloud data he needs to be entering some read related, and if he wants to write the data he needs to be entering write related attribute. For each and every action user in an organization would be verified with their unique attribute set. These attributes will be stored by the admins to the authorized users in cloud organization. These attributes will be stored in the policy files in a cloud. Along with this attribute,a rule based engine is used, to provide the access control to user. If any user leaks their decryption key to the any malicious user data owners wants to trace by sending audit request to auditor and auditor will process the data owners request and concludes that who is the convict.
GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization

Bundle recommendation – recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting challenges – (1) how to personalize and recommend existing bundles to users, and (2) how to generate personalized novel bundles targeting specific users. Recently, few models have been proposed for modeling the bundle recommendation problem. However, they have the following shortcomings. First, they do not consider the higher-order relationships amongst the entities (users, items and bundles). Second, they do not model the relative influence of items present in the bundles, which is crucial in defining such bundles. In this work, we propose GRAM-SMOT – a graph attention-based framework to address the above challenges. Further, we define a loss function based on the metric-learning approach to learn the embeddings of entities efficiently. To generate novel bundles, we propose a strategy that leverages submodular function maximization. To analyze the performance of the proposed model, we conduct comprehensive experiments on two real-world datasets. The experimental results demonstrate the superior performance of the proposed model over the existing state-of-the-art models.
Demonstration of Classification Task Using Optical Neural Network Based on Si Microring Resonator Crossbar Array

We demonstrate Si microring resonator crossbar array as a programmable nanophotonic processor. By using multi-wavelength optical signal and monolithically integrated Ge photodetectors, we performed multiply-accumulate operation for optical neural network. We demonstrated classification task for Iris dataset, resulting in prediction accuracy of 91%.
Application of Artificial Intelligence Technology in Virtual Reality Animation Aided Production

Abstract The three-dimensional animation developed by virtual reality technology has been widely concerned. Although the virtual reality technology has changed the traditional animation production mode to a great extent, the existing virtual reality technology is still not perfect, which needs to be further compensated by artificial intelligence technology, and the domestic research on this aspect is less. Therefore, this paper establishes the application of artificial intelligence technology in virtual reality aided animation production. This paper investigates the popularity of artificial intelligence technology course in animation major of colleges and universities, and thinks that the interdisciplinary education needs to be strengthened. In this paper, the application of artificial intelligence technology in virtual reality aided animation production is analyzed in detail. Firstly, it is considered that artificial intelligence makes animation tools intelligent and further changes the traditional animation production mode. Secondly, the importance of artificial intelligence technology in 3D animation modelling due to the current that virtual reality technology is not perfect. It needs to be improved through artificial intelligence technology. Finally, this paper analyzes the development trend of the integration of artificial intelligence and virtual reality. This paper believes that the intelligent artistic creation is the future development trend, and the efficiency and effect of animation production can be effectively improved through artificial intelligence technology.
Fusion layer attention for image-text matching

Image-text matching aims to find the relationship between image and text data and to establish a connection between them. The main challenge of image-text matching is the fact that images and texts have different data distributions and feature representations. Current methods for image-text matching fall into two basic types: methods that map image and text data into a common space and then use distance measurements and methods that treat image-text matching as a classification problem. In both cases, the two data modes used are image and text data. In our method, we create a fusion layer to extract intermediate modes, thus improving the image-text processing results. We also propose a concise way to update the loss function that makes it easier for neural networks to handle difficult problems. The proposed method was verified on the Flickr30K and MS-COCO datasets and achieved superior matching results compared to existing methods.
Weak Adaptation Learning: Addressing Cross-domain Data Insufficiency with Weak Annotator

Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly hinder the learning process. While data from similar domains may be leveraged to help through domain adaptation, obtaining high-quality labeled data for those source domains themselves could be difficult or costly. To address such challenges on data insufficiency for classification problem in a target domain, we propose a weak adaptation learning (WAL) approach that leverages unlabeled data from a similar source domain, a low-cost weak annotator that produces labels based on task-specific heuristics, labeling rules, or other methods (albeit with inaccuracy), and a small amount of labeled data in the target domain. Our approach first conducts a theoretical analysis on the error bound of the trained classifier with respect to the data quantity and the performance of the weak annotator, and then introduces a multi-stage weak adaptation learning method to learn an accurate classifier by lowering the error bound. Our experiments demonstrate the effectiveness of our approach in learning an accurate classifier with limited labeled data in the target domain and unlabeled data in the source domain.
Optimized YOLOv2 based vehicle classification and tracking for intelligent transportation system

The prevailing real-time system used for vehicle detection and classification using deep learning techniques accuracy diminishes due to its background, illumination variation, occlusion, and variation of vehicle sizes in a scene. Hence the real-time system is proposed to enhance the accuracy level on detection and classification of vehicles for a multi-view surveillance video using an optimized YOLOv2 deep learning algorithm. The improvisation in optimized YOLOv2 has two major stages, firstly to eliminate the influence of background and illumination variation on Focal Loss is improved. Secondly, Rk-means++ clustering discovers the best prior, and normalization to bounding boxes loss to find length and width. Additionally, the network is made more accurate in detecting and classifying vehicles. The experimental results are tested on the COCO-2017 dataset and show that the mean Average Precision (mAP) is 97.85% underneath the premise of substantiating real-time performance. Since there exists a pre-trained model and GPU increases the speed processing to 40 frames per second.
Image Quality Improvements Based on Motion-Based Deblurring for Single-Photon Imaging

Photon counting imaging can be used to capture clearly photon-limited scenes. In photon counting imaging, information on incident photons is obtained as binary frames (bit-plane frames), which are transformed into a multi-bit image in the reconstruction process. In this process, it is necessary to apply a deblurring method to enable the capture of dynamic scenes without motion blur. In this article, a deblurring method for the high-quality bit-plane frame reconstruction of dynamic scenes is proposed. The proposed method involves the deblurring of units of object motion within a scene through the application of motion compensation to pixels sharing the same motions. This method achieves more efficient motion blur suppression than the application of simple deblurring to pixel block or spatial region units. It also applies a novel technique for accurate motion estimation from the bit-plane frame even in photon-limited situations through the statistical evaluation of the temporal variation of photon incidence. In addition to deblurring, our experimental results also revealed that the proposed method can be applied for denoising, which improves the peak signal-to-noise ratio by 1.2 dB. In summary, the proposed method for bit-plane reconstruction achieves high quality imaging even in photon-limited dynamic scenes.
GPU Accelerated Convex Approximations for Fast Multi-Agent Trajectory Optimization

In this letter, we present a computationally efficient trajectory optimizer that can exploit GPUs to jointly compute trajectories of tens of agents in under a second. At the heart of our optimizer is a novel reformulation of the non-convex collision avoidance constraints that reduces the core computation in each iteration to a large scale, convex, unconstrained Quadratic Program (QP). Importantly, our QP structure requires us to compute the associated matrix factorization/inverse only once for a fixed number of agents. Moreover, we can do it offline and then use the same for different problem instances. This further simplifies the solution process, effectively reducing it to a few matrix-vector products. For a large number of agents, this computation can be trivially accelerated on GPUs using existing off-the-shelf libraries. We validate our optimizer's performance on challenging benchmarks and show substantial improvement over state of the art in computation time and trajectory quality.