Reviewerly/exHarmony huggingface dataset:
Robust Mosaicing of Endomicroscopic Videos via Context-Weighted Correlation Ratio

Probe-based confocal laser endomicroscopy (pCLE) is a promising imaging tool that provides in situ and in vivo optical imaging to perform real-time pathological assessments. However, due to limited field of view, it is difficult for clinicians to get a full understanding of the scanned tissues. In this paper, we develop a novel mosaicing framework to assemble all frame sequences into a full view image. First, a hybrid rigid registration that combines feature matching and template matching is presented to achieve a global alignment of all frames. Then, the parametric free-form deformation (FFD) model with a multiresolution architecture is implemented to accommodate non-rigid tissue distortions. More importantly, we devise a robust similarity metric called context-weighted correlation ratio (CWCR) to promote registration accuracy, where spatial and geometric contexts are incorporated into the estimation of functional intensity dependence. Experiments on both robotic setup and manual manipulation have demonstrated that the proposed scheme significantly precedes some state-of-the-art mosaicing schemes in the presence of intensity fluctuations, insufficient overlap and tissue distortions. Moreover, the comparisons of the proposed CWCR metric and two other metrics have validated the effectiveness of the context-weighted strategy in quantifying the differences between two frames. Benefiting from more rational and delicate mosaics, the proposed scheme is more suitable to instruct diagnosis and treatment during optical biopsies.
Bridging Robotics Education between High School and University: RoboCup@Home Education

Comparing to the learning contents in high school level robotics education leads us to discover a gap of missing skill sets required in university level robotics development. Our objective in this work is to bridge this gap by outreaching our development in RoboCup@Home Education. We have conducted our studied in several RoboCup events to establish new local communities. We have run hands-on robot building workshop, and finally gauge the project outcome by organizing the Education Challenge.
IT Security Is From Mars, Software Security Is From Venus

The divide between IT security and software security can result in the neglect of proper software security. This divide can be bridged by establishing a formal security champion role in the development team and conducting collaborative risk-based security activities.
Research on a Hybrid Prediction Model for Purchase Behavior Based on Logistic Regression and Support Vector Machine

In recent years, online retail has maintained rapid growth, and websites are rich in user behavior data. The operation behaviors of users on the e-commerce platform can reflect user preferences. How to use user behaviors to mine user preferences has become the focus of academia and industry, and many research results have been achieved. In many cases, by fusion training two or more different algorithms, the generalization ability of the algorithm can be significantly improved to improve the prediction effect. This paper combines the fusion of logistic regression and support vector machine algorithms to construct a hybrid prediction model for user buying behavior, and conducts an empirical study on the effectiveness of the model. The empirical results show that the fusion model has better prediction effect than the single model.
A knowledge-based document assembly method to support semantic interoperability of enterprise information systems

This paper proposes a method for knowledge-based document assembly. A document assembly system is implemented as a proof of concept with service contracts as a sample document type. In comparison to related research, the method presented in this paper uses an explicit formulation of legal norms that prescribes the content and the form of service contracts to assist in the document assembly process. The assembly system also generates semantic documents, i.e., documents that preserve the meaning of the embedded data. As the primary contribution, this approach enables the automation of document processing operations and semantic interoperability of enterprise information systems.
Designing with AI for Digital Marketing

We present an interactive user interface that allows digital marketing professionals to have real time access to insights from a back-end AI that predicts potential click-through rates of composed content based on similar past campaigns. We wanted to investigate the extent to which digital marketing professionals would find our system usable and useful and whether or not the advice our system generated would create content that had higher click through rates than content developed without the system's advice. Our framework decomposes aspects of prior campaigns into features including image quality, memorability, and placement; and text readability, formality and sentiment. We show our algorithm has high predictive value on a historical test set (AUC .80); that digital marketing professionals give the system an overall high satisfaction rating and that, using the advice of the AI agent, we can generate content that creates up to 22% click-through rate lift on a 700 A/B preference tasks given to master workers on AMT.
An Obstacle Avoidance Algorithm for Manipulators Based on Six-Order Polynomial Trajectory Planning

Aiming at a series of requirements of obstacle avoidance trajectory planning of manipulators, a new algorithm based on six-order polynomial trajectory planning is proposed. Firstly, the six-order polynomial is used for the trajectory planning of the manipulator. Assuming that the coefficients of the sixth order term in the curve equation are undetermined parameters, by adjusting these parameters, the shape of the curve can be changed to make manipulators avoid the obstacle and to optimize performance indicators of the trajectory simultaneously. Thus, the obstacle avoidance trajectory planning of manipulators is transformed into a multi-objective optimization problem. Secondly, combining collision detection results and kinematics indexes, a fitness function is defined by the weighting coefficient method. At last, an ideal collision-free trajectory that is collaborative optimized in kinematics, trajectory length and rotation angle is planned in the joint space through genetic algorithm optimization. Additionally, the algorithm is validated by simulation experiments with MATLAB, the results show that the method of this study can effectively plan obstacle-free trajectories satisfying the performance requirements of the manipulator.
Key-Escrow Free Attribute-Based Multi-Keyword Search with Dynamic Policy Update in Cloud Computing

Attribute-based searchable encryption (ABSE) schemes provide searchability and fine-grained access control over encrypted data in the cloud. Although prior ABSE schemes are designed to provide data protection and retrieval efficiency, they are suffering from the following issues. 1) A key-escrow problem, which may lead to the misuse of the user's secret key. 2) A single keyword search, which may produce irrelevant search results and also vulnerable in real-world applications. 3) A static policy update mechanism, which incurs high computation and communication overheads. In this paper, we propose a novel ABSE scheme called a key-escrow free attribute-based multi-keyword search with dynamic policy updates in cloud computing (KAMS-PU) to addresses all the above-stated issues. The security analysis proves that KAMS-PU is secure against malicious authority attacks and chosen keyword attack (CKA) in the random oracle model. Furthermore, performance analysis proves that KAMS-PU is efficient and practical in real-world applications.
Floor Plan Recognition and Vectorization Using Combination UNet, Faster-RCNN, Statistical Component Analysis and Ramer-Douglas-Peucker

The floor plan recognition and vectorization problem from the image has a high market response due to the ability to be applied in such domains as design, automatic furniture fitting, property cost estimation, etc. Several approaches already exist on the market. Many of them are using just statistical or deep machine learning methods capable of recognizing a limited set of floor plan types or providing a semi-automatic tool for recognition. This paper introduces the approach based on the combination of statistical image processing methods in a row of machine learning techniques that allow training robust model for the different floor plan topologies. Faster R-CNN for the floor object detection with a mean average precision of 86% and UNet for the wall segmentation has shown the IoU metric results of about 99%. Both methods, combined with functional and component filtration, made it possible to implement the new approach for vectoring the floor plans.
Reducing catastrophic forgetting with learning on synthetic data

Catastrophic forgetting is a problem caused by neural networks' inability to learn data in sequence. After learning two tasks in sequence, performance on the first one drops significantly. This is a serious disadvantage that prevents many deep learning applications to real-life problems where not all object classes are known beforehand; or change in data requires adjustments to the model. To reduce this problem we investigate the use of synthetic data, namely we answer a question: Is it possible to generate such data synthetically which learned in sequence does not result in catastrophic forgetting? We propose a method to generate such data in two-step optimisation process via meta-gradients. Our experimental results on Split-MNIST dataset show that training a model on such synthetic data in sequence does not result in catastrophic forgetting. We also show that our method of generating data is robust to different learning scenarios.
Higher income, larger loan? monotonicity testing of machine learning models

Today, machine learning (ML) models are increasingly applied in decision making. This induces an urgent need for quality assurance of ML models with respect to (often domain-dependent) requirements. Monotonicity is one such requirement. It specifies a software as ''learned'' by an ML algorithm to give an increasing prediction with the increase of some attribute values. While there exist multiple ML algorithms for ensuring monotonicity of the generated model, approaches for checking monotonicity, in particular of black-box models are largely lacking.
A NSGA-II-based Approach for Multi-objective Micro-service Allocation in Container-based Clouds

Micro-services is a widely adopted architecture to develop large scale web applications. To provide a scalable and low-overhead resource service to micro-service applications, the new container-based clouds are proposed. The new clouds use both containers and VMs to manage resources to achieve a low-overhead, high-utilization data center. However, existing resource allocation approaches either do not consider the dependencies between containers or can only be applied in OS-level container clouds which allocate containers directly to physical machines. To address the multi-objective optimization problem, this work proposes a multi-objective NSGA-II to optimize the availability of applications and the energy consumption requirement of container-based clouds. Our goal is to provide solutions with different tradeoffs between two objectives for cloud providers to choose from. We evaluate the algorithm with a wide range of scenarios by simulation and compare with state-of-the-art algorithms. The results show that our approach significantly outperforms other approaches.
Transdisciplinary Strategy of Continuing Engineering Education

Acceleration of developments in science and technology requires a new approach to training and retraining of personnel working in real economy and first of its elite – engineering personnel. Globalization of world economy presents new claims to the quality of education of contemporary engineers. National engineers are facing a task of modernization of technology in industry, access to new markets with up-to-date knowledge-intensive products. In this connection, present-day engineering education requires comprehension of its cultural traditions, philosophical and historical framework. Starting from his college years integration of a present-day engineer into the current system of knowledge management, accumulation, storage and conveying new knowledge actualized as intellectual property becomes a key factor for the successful engineering training. This requires available modern laboratories, librarian search engines with segmentation function, system of protection and access to necessary information, interaction with the university industrial partner based on a twofold education. The prospect to partake in technology enterprise, in project work, in grant schemes, start-ups and venture funds is a popular area in engineering training. Thereupon the authors establish the development and realization of a new strategy of continuing engineering education on the grounds of transdisciplinarity and association of engineering, economic, linguistic and IT knowledge.
An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.
A New Approach to Optimum Steganographic Algorithm for Secure Image

In the current times, there is extensive use of the global network throughout communities as a consequence of which, users have become vulnerable to growing attacks of browsers. The existing world calls for ensuring self-security and protection of surroundings, chiefly when it comes to the online realm, inside which hiding data has become a rampant occurrence, starting a field of investigation that covers requests including copyright protection rules for digital media technology, watermarking, fingerprinting, iris and steganography. In its root, cryptography technique was first established as a system to secure secret e-mail, with several various approaches developed for the purpose of data encrypt and decrypt for secret emails. Steganography basically refers to an art and science of invisible communication. In this paper, the author proposed two new encryption types that contribute to technology security and accuracy. Thus, this paper proposed a new intended the methods to be user-friendly, easy to handle, while at the same time improve the security. Consequently, the study of this paper is aims to develop a new type of two encryption were used namely Advanced Encryption Standard (AES) and Least Significant Bit (LSB) and identifies the needs of optimum steganographic algorithm and represents the steganographic methods that are suitable for specific applications.
User Experience of Alexa when controlling music

We evaluate the user experience (UX) of Amazon's Alexa when users play and control music. For measuring UX we use established UX metrics (SASSI, SUISQ-R, SUS, AttrakDiff). We investigated face validity by asking users to rate how well they think a questionnaire measures what it is supposed to measure and we assessed construct validity by correlating UX scores of questionnaires with each other. We find a mismatch between face and construct validity of the evaluated questionnaires. Specifically, users feel that SASSI represents their experience better than other questionnaires, however this is not supported by correlations between questionnaires, which suggest that all investigated questionnaires measure UX to a similar extent. Importantly, the fact that face validity and construct validity diverge is not surprising as this has been observed before. Our work adds to existing literature by providing face and construct validity scores of UX questionnaires for interactions with the common speech assistant Alexa.
A Learning-Aided Generic Framework for Fault Detection and Recovery of Inertial Sensors in Automated Driving Systems

A generic framework is proposed for fault detection and recovery (FDR) in automated driving systems (ADSs) with inertial sensors, which is based on multitask one-dimensional convolutional neural networks (MONN). FDR plays an important role for safe and robust vehicle controls in driver-assistance and ADSs. Although model-based fault detection and recovery methods are well established in the literature, they need accurate model description and are often designed under ideal conditions. Data-driven fault diagnosis, however, might overlook apparent physical relations among sensors. In order to take advantage of both approaches, dynamic relations among measured physical quantities and temporal finite differences are introduced in a MONN as additional features. The proposed algorithm achieved more reliable performance and outperformed considered state-of-the-art methods under various testing conditions. Extensive experiments were conducted to empirically show the effectiveness of the proposed generic FDR framework.
Exploring frame-based gesture design for immersive VR shopping environments

In the design of gesture-based user interfaces, traditional gesture elicitation studies suffer from the legacy bias problem. In this paper, we conducted an exploratory study about the practical effects of frame-based design for gestural interaction with immersive VR shopping applications. In this study, we derived gestures via the traditional guessability and the framed guessability approaches. Experimental results indicated that priming participants with a frame, or a scenario, could significantly reduce the impact of legacy bias, and result in superior gesture vocabulary. However, no evidence was found that the priming technique would generate more gesture types, which may lead to lower agreement scores due to the reduction of legacy bias. Based on our findings, we propose some concrete design guidelines for gesture-based interaction. We highlight the implications of this work for the design of all gesture-based applications.
Training System for Hybrid Vehicles Through Augmented Reality

This article presents the development of a training system in augmented reality; oriented to the teaching and learning process in the area of Automotive Mechanics for hybrid vehicles. The application developed will allow the user to be more immersed in the operation of the hybrid vehicle, therefore, it will simulate the movements made by its transmission which involves the power dividing device (PSD), MG1, MG2 and internal combustion engine (IC), in addition, it performs the virtualization of the assembly and disassembly of the device in order to optimize time and economic resources, among other benefits of educational and technological impact. The system is developed in Unity 3D's graphic environment by means of large-scale 3D object recognition; by means of previously established patterns which, once the application has been developed, will allow the user to visualize the transmission together with its main components and will serve as an aid for the methodological process of teaching and learning in Automotive Mechanics.
A New Multi-Channel Deep Convolutional Neural Network for Semantic Segmentation of Remote Sensing Image

The semantic segmentation of remote sensing (RS) image is a hot research field. With the development of deep learning, the semantic segmentation based on a full convolution neural network greatly improves the segmentation accuracy. The amount of information on the RS image is very large, but the sample size is extremely uneven. Therefore, even the common network can segment RS images to a certain extent, but the segmentation accuracy can still be greatly improved. The common neural network deepens the network to improve the classification accuracy, but it has a lot of loss to the target spatial features and scale features, and the existing common feature fusion methods can only solve some problems. A segmentation network is built to solve the above problems very well. The network employs the InceptionV-4 network as the backbone and improves it. We modify the network structure and introduce the changed Atrous Spatial Pyramid Pooling module to extract the multi-scale features of the target from different training stages. Without losing the depth of the network, using Inception blocks to strengthen the width of the network can obtain more abstract features. At the same time, the backbone network is used for semantic fusion of the context, it can retain more spatial features, then an effective decoder network is designed. Finally, evaluate our model on the ISPRS 2D Semantic Labeling Contest Potsdam and Inria Aerial Image Labeling Dataset. The results show that the network has very superior performance, reaching 89.62% IOU score and 94.49% F1 score on the Potsdam dataset, and the IOU score on the Inria dataset has been greatly improved.
CNN Feature-Based Image Copy Detection with Contextual Hash Embedding

As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applications of computer vision. However, it is not feasible to directly apply the existing global CNN features for copy detection, since they are usually sensitive to partial content-discarded attacks, such as copping and occlusion. Thus, we propose a local CNN feature-based image copy detection method with contextual hash embedding. We first extract the local CNN features from images and then quantize them to visual words to construct an index file. Then, as the BOW quantization process decreases the discriminability of these features to some extent, a contextual hash sequence is captured from a relatively large region surrounding each CNN feature and then is embedded into the index file to improve the feature’s discriminability. Extensive experimental results demonstrate that the proposed method achieves a superior performance compared to the related works in the copy detection task.