Reviewerly/exHarmony huggingface dataset:
Visual Relational Reasoning for Image Caption

Recently, various attention-based networks have achieved state-of-art results on image captioning tasks. However, this simple mechanism is insufficient to modelling and reasoning the relationships between the visual regions required for scene understanding. In this research, we propose a visual relational reasoning module to implicit learning semantic and spatial relationships between pairs of relevant visual objects and infers the feature output that is most relevant to the currently generated word. Furthermore, a context gate is introduced to dynamically control the contribution of visual region attention modules and visual relational reasoning module which allows predicting different words according to different type of features (visual or visual relationship). We evaluate our model on the MSCOCO dataset and achieved state-of-the-art results. Qualitative analysis shows that our visual relational reasoning model can dynamically model and reason the most relevant features of different types of generated words and improve the quality of the caption.
Stability of user satisfaction prediction in software projects

The goal of this paper was to investigate the stability of predictions of user satisfaction using the extended version of the ISBSG dataset. The analysis involved building and training 40 models using 12 machine learning techniques. The results were analysed using a ‘win-tie-loss’ procedure based on a statistical significance. The overall best performing models were random forests. However, 19 models using ten techniques performed the best in at least one of 20 passes. High variability of data across passes caused the difficulty of predictions in some passes.
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations

The ability to capture relations within data can provide the much needed inductive bias for robust and interpretable Machine Learning algorithms. Variational Autoencoder (VAE) is a promising candidate for such purpose thanks to their power in data representation inference, but its vanilla form and common variations cannot process feature relations. In this paper, inspired by recent advances in relational learning with graph neural networks, we propose the Self-Attention Graph Variational AutoEncoder (SAG-VAE) model which can simultaneously learn feature relations and data representations in an end-to-end manner. The SAG-VAE is trained by jointly inferring the posterior distribution of two types of latent variables, which respectively represent the data and the feature relations. The feature relations are represented as a graph structure, and the presence of each edge is determined by a Gumbel-Softmax distribution. The generative model is accordingly parameterized by a graph neural network with a special attention mechanism we introduced in the paper. Therefore, the SAG-VAE model can generate via graph convolution and be trained via backpropagation. Experiments based on graphs show that SAG-VAE is capable of approximately retrieving edges and links between vertices based entirely on feature observations. Furthermore, experiments on image data illustrate that the learned feature relations can provide the SAG-VAE robustness against perturbations in image reconstruction and sampling. The learned feature relations as graph adjacency matrices are observed to be structured, which provides intuitive interpretability of the models.
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors

The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses on attacking image classifiers or anchor-based object detectors, but they generate globally perturbation on the whole image, which is unnecessary. In our work, we leverage higher-level semantic information to generate high aggressive local perturbations for anchor-free object detectors. As a result, it is less computationally intensive and achieves a higher black-box attack as well as transferring attack performance. The adversarial examples generated by our method are not only capable of attacking anchor-free object detectors, but also able to be transferred to attack anchor-based object detector.
Data Security Platform Model in Networked Medical IT Systems based on Statistical Classifiers and ANN

The paper presents the results of research on improving the security of medical information systems. It begins with a presentation of the specific features of such systems in terms of functionality and potential risks. It was assumed that the features distinguishing medical systems from other information systems are: patient orientation, confidentiality of the information processed, state interference in the functioning of medical entities and the need to ensure maximum availability of system resources. The last requirement results from treating medical IT systems as elements of critical infrastructure, for which business continuity is one of the key features. The work focused on limiting the systems’ sensitivity to cyber-attacks limiting their availability. A software and hardware platform dedicated for attack detection is presented. The system uses parallel processing and artificial intelligence and is distinguished by several alternative detection methods, the use of expert system to make decisions about protection measures and the ability to independently identify unknown attacks. Paper ends with an evaluation of prototype testing results and directing further work in this area. The work is address to people involved in the operation of medical information systems.
BrainSeg3D to Detect Multiple Sclerosis Lesions Using Magnetic Resonance Imaging

Today, many researches and many existing methods treat the multiple sclerosis (MS) which is a chronic inflammatory pathology of the central nervous system. To detect multiple sclerosis outliers, we should have a diagnosis by magnetic resonance imaging (MRI) with different sequences. For this purpose, some recent methods of segmentation and classification of MRI sequences have been proposed for the automatic detection of MS tumors. In this paper, we propose a new protocol based on the existing and novel semi-automated lesions segmentation methods of BrainSeg3D. This protocol is applied on a novel MR databases of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to evaluate our test with the obtained Consensus segmentation. The obtained results show an improvement comparant to those proposed in the literature.
Deep Regression Forest with Soft-Attention for Head Pose Estimation

The task of head pose estimation from a single depth image is challenging, due to the presence of large pose variations, occlusions and inhomegeneous facial feature space. To solve the problem, we propose Deep Regression Forest with Soft-Attention (SA-DRF) in a multi-task learning setup. It can be integrated with a general feature learning net and jointly learned in an end-to-end manner. The soft-attention module is facilitated to learn soft masks from the general features and feeds the forest with task-specific features to regress head poses. Experiments on the Biwi Head Pose and Pandora datasets demonstrate its superior performance compared to current state-of-the-arts.
A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy

In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.
A Question-Oriented Visualization Recommendation Approach for Data Exploration

The increasingly rapid growth of data production and the consequent need to explore data to obtain answers to the most varied questions have promoted the development of tools to facilitate the manipulation and construction of data visualizations. However, building useful data visualizations is not a trivial task: it may involve a large number of subtle decisions from experienced designers. In this paper, we present an approach that uses a set of heuristics to recommend data visualizations associated with questions, in order to facilitate the understanding of the recommendations and assisting the visual exploration process. Our approach was implemented and evaluated through the VisMaker tool. We carried out two studies comparing VisMaker with Voyager 2 and analyzed some aspects of the recommendation approaches through the participants' feedbacks. As a result, we found some advantages of our approach and gathered comments to help improve the development of visualization recommender tools.
Response time analysis of dynamic load balancing algorithms in Cloud Computing

Due to rapid and tremendous growth in cloud computing, there is a huge demand by the applications for faster services hosted in the cloud. Minimizing the latency in the cloud requires approaches that can handle the requests of clients. Load balancing is the mechanism to distribute tasks to computational entities in a cloud computing environment to manage current needs of the application. There is need for efficient load balancing mechanisms to improve the overall performance of cloud computing environment by reducing the time to respond to the request. In this paper three algorithms to balance the load for cloud computing are compared with respect to minimization of job response time. Equally spread current execution, throttled and particle swarm optimization, a Swarm Intelligence algorithm are the algorithms considered for load balancing. The algorithms are simulated using CloudSim simulator. Performance measurement of these algorithms show that the particle swarm optimization balances the workload with reduced job response time as compared with the other two.
Fast Tumor Detector in Whole-Slide Image With Dynamic Programing Based Monte Carlo Sampling

In the last decade, computational pathology has attracted notable attention in the deep learning domain. However, even on the state-of-the-art deep learning computing platforms, a high-resolution scanned whole slide image (WSI) still requires reducing into massive patches to be processed, which is very time consuming in real-time diagnosis. In this paper, we propose a high-throughput tumor location system with Monte Carlo adaptive sampling to accelerate WSI analysis. Additionally, we design a dynamic programming framework to incorporate spatial correlation, which can iteratively eliminate false positives or false negatives in the identification or tumor tissues. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) for performance evaluation. The designed computer-aided system can reduce more than 50% of the diagnostic time on average in the tumor location task, along with a slight increase in accuracy.
Analysis and Learning of Capsule Networks Robust for Small Image Deformation

The Capsule Network (CapsNet) is a deep learning model proposed for image classification that is robust to pose of change of objects in images. A capsule is a vector representing the position, size and presence of an object. However, with CapsNet, the number of capsules increases, depending on the number of classification classes, and learning is computationally expensive. Thus, we propose a method for reducing computational costs by enabling a single capsule to represent multiple object classes. To learn the distance between classes, we incorporate the ArcFace distance learning method in the error function. In a preliminary experiment, the distribution of capsules was visualised by principal component analysis to demonstrate the validity of the proposed method. Using the MNIST and CIFAR-10 datasets, as well as an the affine transformed dataset, we compare the accuracy and learning time of the original CapsNet and proposed method. The results demonstrate that accuracy is improved by 2.74% on the CIFAR-10 dataset, and the learning time is reduced by more than 19% in both datasets.
Aortic 3D Deformation Reconstruction using 2D X-ray Fluoroscopy and 3D Pre-operative Data for Endovascular Interventions

Current clinical endovascular interventions rely on 2D guidance for catheter manipulation. Although an aortic 3D surface is available from the pre-operative CT/MRI imaging, it cannot be used directly as a 3D intra-operative guidance since the vessel will deform during the procedure. This paper aims to reconstruct the live 3D aortic deformation by fusing the static 3D model from the pre-operative data and the 2D live imaging from fluoroscopy. In contrast to some existing deformation reconstruction frameworks which require 3D observations such as RGB-D or stereo images, fluoroscopy only presents 2D information. In the proposed framework, a 2D-3D registration is performed and the reconstruction process is formulated as a non-linear optimization problem based on the deformation graph approach. Detailed simulations and phantom experiments are conducted and the result demonstrates the reconstruction accuracy and robustness, as well as the potential clinical value of this framework.
Model-Based Reinforcement Learning For Robot Control

Model-free deep reinforcement learning (MFRL) algorithms have achieved many impressive results. But they are generally stricken with high sample complexity, which puts forward a critical challenge for their application to real-world robots. Dynamic models are essential for robot control laws, but it is often hard to obtain accurate analytical dynamic models. Therefore a data-driven approach to learning models becomes significant for reinforcement learning to increase data efficiency. Model-based algorithms are effective methods to reduce sample complexity by learning the system dynamic model. However, in certain environments, it has been proven that learning an accurate system dynamic model is a formidable problem, and their asymptotic performance cannot achieve to the same level as model-free algorithms. In our work, we use an ensemble of deep neural networks to learn system dynamics and incorporate model uncertainty. Then in order to merge the high asymptotic performance of the advanced model-free methods, the deep deterministic policy gradient (DDPG) algorithm is adopted to optimize robot control policy. Furthermore, it has been implemented within ROS for controlling a Baxter robot in the simulation environment.
Information Enhanced Graph Convolutional Networks for Skeleton-based Action Recognition

Skeleton-based action recognition has recently attracted much attention in computer vision. The latest methods are mostly based on graph convolutional networks (GCNs), which construct the human body as spatial-temporal Skeleton graphs, and has achieved excellent performance. However, previous studies only capture the local and rough information based on the physical dependencies among joints, which may miss implicit joint correlations. In this work, we propose a novel action recognition model, namely Information Enhanced Graph Convolutional Networks (IE-GCN). To improve the accuracy and robustness of recognition, this model capture higher-order dependency in the skeleton-based graph by expanding the joint neighbors, and combine second stage skeleton features (the lengths and directions of bones) to enhance the discriminative information simultaneously. In addition, an training strategy is designed to solve the framework. Extensive experiments on two large-scale public datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate the superior performance of the proposed algorithms over the state-of-the-art methods.
Measuring the Optimal Information Security Complexity for Blockchain Operations

This article proposes and applies a qualitative method for measuring the optimal complexity of information security controls for adequate implementation and operations of a public, private, or consortium blockchain within organisations. The method can also be adjusted for blockchain solutions, such as smart contracts, user interfaces, or other related software.
Impact of Artificial Lights in Cultivation &amp; Generate Electricity Besides Agriculture: Feasibility &amp; Constraints Analysis in Bangladesh

Among all the renewable energy sources (RES) solar energy is eco-friendly and available everywhere. The limitation of empty land has become difficult to implement a mega solar power plant for many countries. Such as Bangladesh, where maximum lands are used for agriculture purposes. In this paper, we have designed a project where LED light's are used in addition to sunlight. To generate electric power we have also designed a solar panel where we have integrated few mini solar modules on transparent glass plates to enable both agricultural and power generation. Using strings, we can install it on agricultural land. A battery bank is used to store electrical energy generated from solar panels. The fuzzy logic controller (FLC) is used to control the whole system.
Unsupervised Embeddings for Categorical Variables

Real-world data sets often contain both continuous and categorical variables yet most popular machine learning methods cannot by default handle both data types. This creates the need for researchers to transform their data into a continuous format. When no prior information is available, the most widely applied methods are simple ones such as one-hot encoding. However, they ignore many possible sources of information, in particular, categorical dependencies, which could enrich the vector representations. We investigate the effect of natural language processing techniques for learning continuous word-vector representations on categorical variables. We show empirically that the learned vector representations of the categorical variables capture information about the variables themselves and their dependencies with other variables similar to how word embeddings capture semantic and syntactic information. We also show that machine learning models using unsupervised categorical embeddings are competitive with supervised embeddings, and outperform them when fine-tuned, on various classification benchmark data sets.
Revisiting Role of Autoencoders in Adversarial Settings

To combat against adversarial attacks, autoencoder structure is widely used to perform denoising which is regarded as gradient masking. In this paper, we revisit the role of autoencoders in adversarial settings. Through the comprehensive experimental results and analysis, this paper presents the inherent property of adversarial robustness in the autoencoders. We also found that autoencoders may use robust features that cause inherent adversarial robustness. We believe that our discovery of the adversarial robustness of the autoencoders can provide clues to the future research and applications for adversarial defense.
FeatureNET

We present FeatureNET, an open-source Neural Architecture Search (NAS) tool1 that generates diverse sets of Deep Learning (DL) models. FeatureNET relies on a meta-model of deep neural networks, consisting of generic configurable entities. Then, it uses tools developed in the context of software product lines to generate diverse (maximize the differences between the generated) DL models. The models are translated to Keras and can be integrated into typical machine learning pipelines. FeatureNET allows researchers to generate seamlessly a large variety of models. Thereby, it helps choosing appropriate DL models and performing experiments with diverse models (mitigating potential threats to validity). As a NAS method, FeatureNET successfully generates models performing equally well with handcrafted models.
Refactoring operations Grounded in manual code changes

Refactoring tools automate tedious and error-prone source code changes. The prevalence and difficulty of refactorings in software development makes this a high-impact area for successful automation of manual operations. Automated refactorings tools can improve the speed and accuracy of software development and are easily accessible in many programming environments. Even so, developers frequently eschew automation in favor of manual refactoring and cite reasons like lack of support for real usage scenarios and unpredictable tools. In this paper, we propose to redesign refactoring operations into transformations that are useful and applicable in real software evolution scenarios with the help of repository mining and user studies.
Open-Set Metric Learning For Person Re-Identification In The Wild

Person re-identification in the wild needs to simultaneously (frame-wise) detect and re-identify persons and has wide utility in practical scenarios. However, such tasks come with an additional open-set re-ID challenge as all probe persons may not necessarily be present in the (frame-wise) dynamic gallery. Traditional or close-set re-ID systems are not equipped to handle such cases and raise several false alarms as a result. To cope with such challenges open-set metric learning (OSML), based on the concept of Large margin nearest neighbor (LMNN) approach, is proposed. We term our method Open-Set LMNN (OS-LMNN). The goal of separating impostor samples from the genuine samples is achieved through a joint optimization of the Weibull distribution and the Mahalanobis metric learned through this OS-LMNN approach. The rejection is performed based on low probability over distance of imposter pairs. Exhaustive experiments with other metric learning techniques over the publicly available PRW dataset clearly demonstrate the robustness of our approach.
Review on the Methodologies for Image Segmentation Based on CNN

In computer vision, image distribution is the process of dividing the digital image into several categories. The main reason for the division is that, the image should be divided into regions for better analysis. Today, the distribution method is used for applications such as image classification, facial recognition, object, and video analysis. Several computer vision functions require some knowledge base using which the visual classification is carried out in order to identify the contents. Recent surveys on image distribution techniques often focus on deep learning techniques, to accurately identify real-world objects within each image. Techniques for segmenting several types of objects using numerous algorithms have been presented in the literature, but most are based on a complex strategies and little work has been reported on systematic interpretation. To provide a comprehensive and lucid approach for segmentation of the objects, deep learning approaches are the most appropriate. These methodologies will purposefully study the characteristics of visual aids and help in better identification of the components within the objects more comprehensively. Image Processing can be done using Deep learning design in a Convolutional Neural Network (CNN). These models are distinctly trained and implemented in engrossed processing units like GPUs (Graphical Processing Units), so that the execution time can be better optimized. In the article, other variations of CNN will also be articulated for more elegant object detection.