Reviewerly/exHarmony huggingface dataset:
Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles

This study is to analyze the influence of visibility in a foggy weather environment on the accuracy of machine vision obstacle detection in assisted driving. We present a foggy day imaging model and analyze the image characteristics, then we set up the faster region convolutional neural network (Faster R-CNN) as the basic network for target detection in the simulation experiment and use Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data for network detection and classification training. PreScan software is used to build weather and traffic scenes based on a foggy imaging model, and we study object detection of machine vision in four types of weather condition-clear (no fog), light fog, medium fog, and heavy fog-by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.54~64.79%, and 57.75% respectively in no fog, light fog, medium fog, and heavy fog environments. Then we used real scenes in medium fog and heavy fog environment to verify the simulation experiment. Through this study, we can determine the influence of bad weather on the detection results of machine vision, and hence we can improve the safety of assisted driving through further research.
An Evolutionary Neuro-Fuzzy C-means Clustering Technique

One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain number of clusters. Among several significant ways of performing clustering, Fuzzy C-means (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current state-of-the-art clustering methods to demonstrate the efficacy of the proposed algorithm.
Deep Manifold-to-Manifold Transforming Network for Skeleton-based Action Recognition

In this paper, we will investigate skeleton-based action recognition by employing high-order statistics feature and first-order statistics feature, where the high-order statistics feature is characterized by symmetric positive definite (SPD) matrices. Noting that SPD matrices are theoretically embedded on Riemannian manifolds, we propose an end-to-end deep manifold-to-manifold transforming network (DMT-Net), which can make SPD matrices flow from one Riemannian manifold to another one for facilitating the action recognition task. To learn discriminative SPD features from both spatial and temporal dependencies, we propose a neural network model with three novel layers on manifolds: i.e., (1) the local SPD convolutional layer, (2) the non-linear SPD activation layer, and (3) the Riemannian-preserved recursive layer. The SPD property is preserved through all layers without the singular value decomposition (SVD) operation, which has to be conducted in the existing methods with expensive computation cost. Furthermore, a diagonalizing SPD layer is designed to efficiently calculate the final metric for the classification task. Finally, DMT-Net is further fused with a first order layer to capture temporal evolution information. To evaluate our proposed method, we conduct extensive experiments on the task of action recognition, where the input signals are represented as SPD matrices. The experimental results demonstrate that the proposed method is competitive over state-of-the-art methods.
A Secure and Privacy-Preserving Technique Based on Contrast-Enhancement Reversible Data Hiding and Plaintext Encryption for Medical Images

Protection of medical data has become a prerequisite in medical imaging clouds due to the semi-trusted cloud. Aiming at preserving patients' privacy and increasing the security of medical images in the cloud, this letter proposes a secure and privacy-preserving technique which provides a new security mechanism for medical data. In this technique, a novel reversible data hiding (RDH) based on adaptive texture classification is proposed to embed privacy data into medical images for preserving patients' privacy and improving image quality, and plaintext encryption is proposed to encrypt the marked medical image into the similar image of target image for increasing image security. Extensive experiments have shown that the proposed RDH is better than other RDH methods. Plaintext encryption can reduce the attacker's attention and increase the security of medical images well.
Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks.This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance.A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal).After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process.Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy.
AGARNet: Adaptively Gated JPEG Compression Artifacts Removal Network for a Wide Range Quality Factor

Most of existing compression artifacts reduction methods focused on the application for low-quality images and usually assumed a known compression quality factor.However, images compressed with high quality should also be manipulated because even small artifacts become noticeable when we enhance the compressed image.Also, the use of quality factor from the decoder is not practical because there are too many recompressed or transcoded images whose quality factor are not reliable and spatially varying.To address these issues, we propose a quality-adaptive artifacts removal network based on the gating scheme, with a quality estimator that works for a wide range of quality factor.Specifically, the estimator gives a pixel-wise quality factor, and our gating scheme generates gate-weights from the quality factor.Then, the gate-weights control the magnitudes of feature maps in our artifacts removal network.Thus, our gating scheme guarantees the proposed network to perform adaptively without changing the parameters according to the change of quality factor.Moreover, we exploit the Discrete Cosine Transform (DCT) scheme with 3D convolution for capturing both spatial and frequency dependencies of images.Experiments show that the proposed network provides better performance than the state-of-the-art methods over a wide range of quality factor.Also, the proposed method provides robust results in real-world scenarios such as the manipulation of transcoded images and videos.
Rumor events detection enhanced by encoding sentimental information into time series division and word representations

Online Social Networks (OSNs) is an ideal place for spreading rumor events as it is convenient in information production and dissemination. Automatically debunking these rumor events is important to pursue and restore the truth. However, it is a challenging task to employ traditional classification approaches for rumor events detection since they rely on hand-crafted features that require daunting manual efforts. Besides, we observe that the various posts of each rumor event will debate its realness over time. Different individuals also have different emotional reactions to events, which will affect others’ identification. Thus, this paper firstly employs an automatic construction method to develop a Sentiment Dictionary (SD) to capture the fine-grained human emotional reactions to different events. Secondly, a Two-steps Dynamic Time Series (TsDTS) algorithm, involving the sentimental information in the division process, is elaborated to retain the time-span distribution information of microblog events in a natural manner. At last, a novel two-layer Cascaded Gated Recurrent Unit (CGRU) model based on the SD and the TsDTS algorithm is proposed for rumor events detection, named as SD-TsDTS-CGRU. Experimental results on real datasets from OSNs demonstrate that our proposed SD-TsDTS-CGRU model outperforms the latest rumor events detection algorithms.
Me vs. Super(wo)man: Effects of Customization and Identification in a VR Exergame

Customised avatars are a powerful tool to increase identification, engagement and intrinsic motivation in digital games. We investigated the effects of customisation in a self-competitive VR exergame by modelling players and their previous performance in the game with customised avatars. In a first study we found that, similar to non-exertion games, customisation significantly increased identification and intrinsic motivation, as well as physical performance in the exergame. In a second study we identified a more complex relationship with the customisation style: idealised avatars increased wishful identification but decreased exergame performance compared to realistic avatars. In a third study, we found that 'enhancing' realistic avatars with idealised characteristics increased wishful identification, but did not have any adverse effects. We discuss the findings based on feedforward and self-determination theory, proposing notions of intrinsic identification (fostering a sense of self) and extrinsic identification (drawing away from the self) to explain the results.
An Augmented Reality Interface for Human-Robot Interaction in Unconstrained Environments

As robots start to become ubiquitous in the personal workspace, it is necessary to have simple and intuitive interfaces to interact with them. In this paper, we propose an augmented reality (AR) interface for human-robot interaction (HRI) in a shared working environment. By fusing marker-based and markerless AR technologies, a mobile AR interface is created that enables a smartphone to detect planar surfaces and localize a manipulator robot in its working environment while obviating the need for a controlled or constrained environment. The AR interface and robot manipulator are integrated to render a system that enables users to perform pick-and-place task effortlessly. Specifically, a smartphone-based AR application is developed that allows a user to select any location within the robot's workspace by merely touching on the smartphone screen. Virtual objects, rendered at user-selected locations, are used to determine the pick and place locations of objects in the real world. The virtual object's start and end points, originally specified in the smartphone camera coordinate frame, are transformed into the robot coordinate frame for the robot manipulator to autonomously perform the assigned task. A user study is conducted with participants to evaluate the system performance and user experience. The results show that the proposed AR interface is user-friendly and intuitive to operate the robot, and it allows users to communicate their intentions through the virtual object easily.
A Blockchain-Based Traceable IP Copyright Protection Algorithm

Current Intellectual Property (IP) copyright protection technologies have low efficiency of authority management, traceability, and scalability. In this work, a blockchain-based IP copyright protection algorithm is proposed to address these issues by establishing a mathematical model of quadratic matrix transformation for IP circuit trading. This algorithm proposes the design of a distributed random embedding mechanism and position mapping function that, when IP trading occurs in blockchain, the traceable mapping function can trace the copyright information in IP trading with the mapping factor. Besides, this work analyzes the credibility, transparency, overhead, and complexity. Experimental results show that the proposed algorithm can resist replaying attacks, yet the copyright information can be rapidly retrieved after suffered from attacks. Still, the proposed algorithm has higher security, stability, and traceability.
Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification

Due to implicitly introduced periodic shifting of limited searching area, visual object tracking using correlation filters often has to confront undesired boundary effect. As boundary effect severely degrade the quality of object model, it has made it a challenging task for unmanned aerial vehicles (UAV) to perform robust and accurate object following. Traditional hand-crafted features are also not precise and robust enough to describe the object in the viewing point of UAV. In this work, a novel tracker with online enhanced background learning is specifically proposed to tackle boundary effects. Real background samples are densely extracted to learn as well as update correlation filters. Spatial penalization is introduced to offset the noise introduced by exceedingly more background information so that a more accurate appearance model can be established. Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object. In order to mitigate changes of objects' appearances, multi-frame technique is applied to learn an ideal response map and verify the generated one in each frame. Exhaustive experiments were conducted on 100 challenging UAV image sequences and the proposed tracker has achieved state-of-the-art performance.
Applying blockchain in the geoenergy domain: The road to interoperability and standards

Geoenergy sources will continue to be mainstays of the world’s energy mix for many years to come, but the technological and business realities behind these energy sources are changing in two fundamental ways. First, with much of the world’s “easy oil” already consumed, the companies behind geoenergy will have to use increasingly sophisticated technologies to find and deliver these energy sources to the market. Second, the expectations placed upon the geoenergy sector by many of its stakeholders have grown considerably with regards to environmental stewardship, safety, and human welfare. In the face of these kinds of challenges, the industry will require an increasing degree of technological and commercial sophistication to continue to be a part of the world’s sustainable energy mix. Blockchain has emerged as a promising innovation that could potentially play an important role in delivering the kinds of technological and commercial capabilities that the geoenergy sector will need to achieve these ends. In spite of the myriad ways that blockchain could potentially improve the efficiency and sustainability of the geoenergy industry, however, the technology is still evolving, and a few barriers stand in the way of its widespread deployment. This paper puts forward case study evidence from the Intel Corporation and the Energistics Consortium showing what the geoenergy sector can learn about blockchain from other industries, and highlights that the absence of data standards and interoperability has contributed to blockchain’s failure to deliver significant value in the geoenergy domain thus far.
Integrated model-driven development of self-adaptive user interfaces

Abstract Modern user interfaces (UIs) are increasingly expected to be plastic , in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context-of-use ( ContextML ) and UI adaptation rules ( AdaptML ). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.
Moodle-Based Learning: Motivating and Demotivating Factors

Over the past 10 years, a lot of universities worldwide have designed different online courses available to their students. The emergence of online courses makes Higher Education a more flexible and elastic concept. The present paper documents a qualitative study that examines motivating and demotivating factors in using Moodle for English language learning in higher schools. The survey encompasses data collected from 137 students and 20 teachers in a higher technical school (Russia) with a focus on students’ and teachers’ perception of Moodle-based learning. Analysis of the obtained data reveals a set of factors concerning pedagogical, technical, management approaches towards Moodle implementation. The results suggest further development of positive experiences and minimizing demotivating factors in online learning environment.
Incorporating fault-proneness estimations into coverage-based test case prioritization methods

Context: During the development process of a software program, regression testing is used to ensure that the correct behavior of the software is retained after updates to the source code. This regression testing becomes costly over time as the number of test cases increases and it makes sense to prioritize test cases in order to execute fault-detecting test cases as soon as possible. There are many coverage-based test case prioritization (TCP) methods that only use the code coverage data to prioritize test cases. By incorporating the fault-proneness estimations of code units into the coverage-based TCP methods, we can improve such techniques. Objective: In this paper, we aim to propose an approach which improves coverage-based TCP methods by considering the fault-proneness distribution over code units. Further, we present the results of an empirical study that shows using our proposed approach significantly improves the additional strategy, which is a widely used coverage-based TCP method. Method: The approach presented in this study uses the bug history of the software in order to introduce a defect prediction method to learn a neural network model. This model is then used to estimate fault-proneness of each area of the source code and then the estimations are incorporated into coverage-based TCP methods. Our proposed approach is a general idea that can be applied to many coverage-based methods, such as the additional and total TCP methods. Results: The proposed methods are evaluated on datasets collected from the development history of five real-world projects including 357 versions in total. The experiments show that using an appropriate bug history can improve coverage-based TCP methods.
Lens aberration compensation in interference microscopy

Emergence of products that feature functional surfaces with complex geometries, such as freeform optics in consumer electronics and augmented reality and virtual reality, requires high-accuracy non-contact surface measurement. However, large discrepancies are often observed between the measurement results of optical methods and contact stylus methods, especially for complex surfaces. For interference microscopy, such as coherence scanning interferometry, the three-dimensional surface transfer function provides information about the instrument spatial frequency passband and about lens aberrations that can result in measurement errors. Characterisation and phase inversion of the instrument's three-dimensional surface transfer function yields an inverse filter that can be applied directly to the three-dimensional fringe data. The inverse filtering is shown to reduce measurement errors without using any data processing or requiring any a priori knowledge of the surface. We present an experimental verification of the characterisation and correction process for measurements of several freeform surfaces and an additive manufactured surface. Corrected coherence scanning interferometry measurements agree with traceable contact stylus measurements to the order of 10 nm.
Real-Time Translation of Indian Sign Language using LSTM

Sign language is the only medium of communication for the speech-impaired community while the rest of the population communicate verbally. This project aims to bridge this communication gap by proposing a novel approach to interpret the static and dynamic signs in the Indian Sign Language and convert them to speech. A sensor glove, with flex sensors to detect the bending of each finger and an IMU to read the orientation of the hand, is used to collect data about the actions. This data is then wirelessly transmitted and classified into corresponding speech outputs. LSTM networks were studied and implemented for classification of gesture data because of their ability to learn long-term dependencies. The designed model could classify 26 gestures with an accuracy of 98%, showing the feasibility of using LSTM based neural networks for the purpose of sign language translation.