Reviewerly/exHarmony huggingface dataset:
PPMR: A Privacy-Preserving Online Medical Service Recommendation Scheme in eHealthcare System

With the continuous development of eHealthcare systems, medical service recommendation has received great attention. However, although it can recommend doctors to users, there are still challenges in ensuring the accuracy and privacy of recommendation. In this paper, to ensure the accuracy of the recommendation, we consider doctors' reputation scores and similarities between users' demands and doctors' information as the basis of the medical service recommendation. The doctors' reputation scores are measured by multiple feedbacks from users. We propose two concrete algorithms to compute the similarity and the reputation scores in a privacy-preserving way based on the modified Paillier cryptosystem, truth discovery technology, and the Dirichlet distribution. Detailed security analysis is given to show its security prosperities. In addition, extensive experiments demonstrate the efficiency in terms of computational time for truth discovery and recommendation process.
A multi-role based differential evolution

Differential evolution (DE) is an efficient and powerful stochastic optimization algorithm. Extensive studies in recent years have verified that different trial vector generation strategies and associated control parameters offer distinct characteristics on different problems. To take full advantages of them, different ensemble methods of trial vector generation strategies and control parameters based on various adaptive strategies have been proposed during the last decade. Aiming to organically integrate merits of some popular generation strategies and control parameters, and then utilize distinct advantages of them, a multi-role based DE (MRDE) is proposed in this paper. In MRDE, the entire population is divided into multiple small-sized groups, and individuals in each group are assigned with different roles in each generation according to their fitness. Based on the assigned role, an individual selects its own trial vector generation strategies and control parameters from a pool to breed offspring. Moreover, an adaptive strategy for population size is used to rationally distribute the computational resources, which is beneficial for speeding up the convergence. Furthermore, a regroup strategy enables individuals to play different roles in different generations, which is favorable for diversifying the search behaviors. The performance of MRDE is compared with that of ten state-of-the-art DE variants on CEC2017 test suite with three dimension cases, and the experimental results demonstrate the competitive and reliable performance of MRDE. In addition, the effectiveness of the newly proposed strategies is also verified through comparison experiments.
Empowering cultural heritage professionals with tools for authoring and deploying personalised visitor experiences

This paper presents an authoring environment, which supports cultural heritage professionals in the process of creating and deploying a wide range of different personalised interactive experiences that combine the physical (objects, collection and spaces) and the digital (multimedia content). It is based on a novel flexible formalism that represents the content and the context as independent from one another and allows recombining them in multiple ways thus generating many different interactions from the same elements. The authoring environment was developed in a co-design process with heritage stakeholders and addresses the composition of the content, the definition of the personalisation, and the deployment on a physical configuration of bespoke devices. To simplify the editing while maintaining a powerful representation, the complex creation process is deconstructed into a limited number of elements and phases, including aspects to control personalisation both in content and in interaction. The user interface also includes examples of installations for inspiration and as a means for learning what is possible and how to do it. Throughout the paper, installations in public exhibitions are used to illustrate our points and what our authoring environment can produce. The expressiveness of the formalism and the variety of interactive experiences that could be created was assessed via a range of laboratory tests, while a user-centred evaluation with over 40 cultural heritage professionals assessed whether they feel confident in directly controlling personalisation.
Social Network Identification Through Image Classification With CNN

Identification of the source social network based on the downloaded images is an important multimedia forensic task with significant cybersecurity implications in light of the sheer volume of images and videos shared across various social media platforms. Such a task has been proved possible by exploiting distinctive traces embedded in image content by social networks (SNs). To further advance the development of this area, we propose a novel framework, called FusionNET, that integrates two established convolutional neural networks (CNNs), with the former (named 1D-CNN) learning discriminative features from the histogram of discrete cosine transform coefficients and the latter (named 2D-CNN) inferring unique attributes from the sensor-related noise residual of the images in question. The separately learned features are then fused by the FusionNET to inform the ensuing source identification or source-oriented image classification component. A series of experiments were conducted on a number of image datasets across various SNs and instant messaging apps to validate the feasibility of the FusionNET also in comparison with the performance of the 1D-CNN and 2D-CNN. The encouraging results were observed.
Development of data-driven models for prediction of daily global horizontal irradiance in Northwest China

Global horizontal irradiance (Hg) data are the primary information for the design and evaluation of solar systems, which can generate clean thermal or electrical energy for agricultural and industrial production. However, Hg measurements are highly costly and time-consuming, which makes Hg data unavailable in most regions of the world. Therefore, numerous empirical and data-driven models have been established to predict Hg using other commonly available meteorological data. The present study proposed three data-driven models, i.e., extreme learning machine (ELM), random forests (RF) as well as the hybrid genetic algorithm and backpropagation neural networks (GANN), for accurate prediction of daily Hg in Northwest China. Daily minimum, maximum air temperature, sunshine duration and relative humidity were used to train and assess the models. The performance of the three data-driven models were also compared against two widely used empirical models (Angstrom–Prescott and Ogelman models). The results demonstrated that the estimated Hg had statistically significant correlations (P < 0.001) with the measured values. A sensitivity analysis indicated that sunshine duration was the most influential variable on Hg prediction in the three data-driven models. Generally, the data-driven models provided better estimates, compared with the two empirical models. The ELM model had the closest daily Hg estimates, followed by the GANN and RF models, with average global performance index (GPI) of 1.516, 1.261 and 0.000, respectively. Thus, the ELM model is highly recommended for daily Hg estimation in Northwest China.
Understanding the pedagogical potential of Interactive Spherical Video-based Virtual Reality from the teachers’ perspective through the ACE framework

This study explores the pedagogical potential of Interactive Spherical Video-Based Virtual Reality (ISVVR) in geography education from the teachers’ perspective. Based on a synthesized developmental model, namely ACE (Acceptance, Creation, and Expertise), we analyzed 187 sets of survey data collected in teacher professional training in which geography teachers were introduced to the production of ISVVR for learning and teaching purposes. The quantitative data analysis revealed that the participants were positive about the pedagogical use of ISVVR. Also, based on the analysis of the qualitative data gathered through the open-ended survey questions via a data-mining technique, we found that participants’ adoption concerns were already beyond the evaluative acceptance level, majorly revolving around issues on pedagogical technology integration. It is suggested that further efforts of teacher professional development are needed for designing and refining the pedagogical use of ISVVR in practice.
Fully complex conjugate gradient-based neural networks using Wirtinger calculus framework: Deterministic convergence and its application

Conjugate gradient method has been verified to be one effective strategy for training neural networks due to its low memory requirements and fast convergence. In this paper, we propose an efficient conjugate gradient method to train fully complex-valued network models in terms of Wirtinger differential operator. Two ways are adopted to enhance the training performance. One is to construct a sufficient descent direction during training by designing a fine tuning conjugate coefficient. Another technique is to pursue the optimal learning rate instead of a fixed constant in each iteration which is determined by employing a generalized Armijo search. In addition, we rigorously prove its weak and strong convergence results, i.e., the gradient norms of objective function with respect to weights approach zero along with the increasing iterations and the weight sequence tends to the optimal point. To verify the effectiveness and rationality of the proposed method, four illustrated simulations have been performed on both typical regression and classification problems.
Collision Avoidance for Underactuated Marine Vehicles Using the Constant Avoidance Angle Algorithm

Avoiding collisions is a crucial ability for unmanned vehicles. In this paper, we present the constant avoidance angle algorithm, a reactive method for collision avoidance. It can be used to avoid both static and moving obstacles by making the vehicle keep an avoidance angle between itself and the obstacle edge. Unlike many other algorithms, it requires neither knowledge of the complete obstacle shape nor that the vehicle follows a desired speed trajectory. Instead, safe vehicle headings are provided at the current vehicle speed. Thus, the speed can be used as an input to the algorithm, which provides flexibility and makes the approach suitable for a wide range of vehicles, including vehicles with a limited speed envelope or high acceleration cost. We demonstrate this by applying the algorithm to a marine vehicle described by a full kinematic and dynamic model in three degrees of freedom. We specifically consider vehicles with underactuated sway dynamics, where the vehicle velocity contains a component that cannot be directly controlled. Such dynamics can be highly detrimental to the performance of collision avoidance algorithms and need to be included in the design and analysis of control systems for such vehicles. In this paper, we compensate for the underactuation by including these dynamics in the underlying analysis and control design. We provide a mathematical analysis of sparse obstacle scenarios, where we derive conditions under which safe avoidance is guaranteed, even for underactuated vehicles. We furthermore show how the modular nature of the algorithm enables it to be combined both with a target reaching and a path following guidance law. Finally, we validate the results both through numerical simulations and through full-scale experiments aboard the R/V Gunnerus.
A novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of solutions in the search space

In this paper a novel statistical approach for comparing meta-heuristic stochastic optimization algorithms according to the distribution of the solutions in the search space is introduced, known as extended Deep Statistical Comparison. This approach is an extension of the recently proposed Deep Statistical Comparison approach used for comparing meta-heuristic stochastic optimization algorithms according to the solutions values. Its main contribution is that the algorithms are compared not only according to obtained solutions values, but also according to the distribution of the obtained solutions in the search space. The information it provides can additionally help to identify exploitation and exploration powers of the compared algorithms. This is important when dealing with a multimodal search space, where there are a lot of local optima with similar values. The benchmark results show that our proposed approach gives promising results and can be used for a statistical comparison of meta-heuristic stochastic optimization algorithms according to solutions values and their distribution in the search space.
Real-Time 3D Motion Tracking and Reconstruction System Using Camera and IMU Sensors

Jointly using the data of visual and inertial sensors to achieve higher accuracy and robustness is a problem of high computational complexity. In this work, this paper presents a 3D motion tracking and reconstruction system on a mobile device using camera and inertial measurement unit (IMU) sensors. The mobile device has very small active infrared projection depth sensors with high-performance IMU and wide field of view cameras. We utilize visual-inertial odometry (VIO) to integrate visual and IMU data. However, traditional VIO method is too complex to apply to a mobile device with limited computing resources in real time. We employ sliding window filter (SWF) for the proposed system to achieve accurate 3D motion tracking and high-quality reconstruction models in real time based on delayed state marginalization. Moreover, we also extensively evaluate in the dataset and real world. Many experiments show the accurate trajectories and high quality 3D reconstruction models by the proposed system. To the end, the qualitative and quantitative experimental results indicate the proposed system based on SWF has much better performance than existing methods in motion tracking and 3D reconstruction models.
UAV Detection System with Multiple Acoustic Nodes Using Machine Learning Models

Class 1 unmanned aerial vehicles (UAVs), known as drones, have become popular and accessible, which makes them tools for malicious purposes. As a result, there is an increasing demand for an effective defense system that can detect UAVs. In this paper, a UAV detection system with multiple acoustic nodes using machine learning models is proposed along with an empirically optimized configuration of the nodes for deployment. Features including Mel-frequency cepstral coefficients (MFCC) and short-time Fourier transform (STFT) were used for training. Support vector machines (SVM) and convolutional neural networks (CNN) were trained with the data collected in person. Experiments were done to evaluate models' ability to find the path of the UAV that was flying. Sensing nodes were placed in four different configurations and the best of test set was chosen which maximizes the detection range without blind spots. STFT-SVM model showed the best performance and a semi-circle formation with 75 meters distance between a node and the protected area is found to be the optimized configuration.
CNN and RNN mixed model for image classification

In this paper, we propose a CNN(Convolutional neural networks) and RNN(recurrent neural networks) mixed model for image classification, the proposed network, called CNN-RNN model. Image data can be viewed as two-dimensional wave data, and convolution calculation is a filtering process. It can filter non-critical band information in an image, leaving behind important features of image information. The CNN-RNN model can use the RNN to Calculate the Dependency and Continuity Features of the Intermediate Layer Output of the CNN Model, connect the characteristics of these middle tiers to the final full-connection network for classification prediction, which will result in better classification accuracy. At the same time, in order to satisfy the restriction of the length of the input sequence by the RNN model and prevent the gradient explosion or gradient disappearing in the network, this paper combines the wavelet transform (WT) method in the Fourier transform to filter the input data. We will test the proposed CNN-RNN model on a widely-used datasets CIFAR-10. The results prove the proposed method has a better classification effect than the original CNN network, and that further investigation is needed.
Shortcut Mining: Exploiting Cross-Layer Shortcut Reuse in DCNN Accelerators

Off-chip memory traffic has been a major performance bottleneck in deep learning accelerators. While reusing on-chip data is a promising way to reduce off-chip traffic, the opportunity on reusing shortcut connection data in deep networks (e.g., residual networks) have been largely neglected. Those shortcut data accounts for nearly 40% of the total feature map data. In this paper, we propose Shortcut Mining, a novel approach that “mines” the unexploited opportunity of on-chip data reusing. We introduce the abstraction of logical buffers to address the lack of flexibility in existing buffer architecture, and then propose a sequence of procedures which, collectively, can effectively reuse both shortcut and non-shortcut feature maps. The proposed procedures are also able to reuse shortcut data across any number of intermediate layers without using additional buffer resources. Experiment results from prototyping on FPGAs show that, the proposed Shortcut Mining achieves 53.3%, 58%, and 43% reduction in off-chip feature map traffic for SqueezeNet, ResNet-34, and ResNet152, respectively and a 1.93X increase in throughput compared with a state-of-the-art accelerator.
Mind the Tap

From voice commands and air taps to touch gestures on frames: Various techniques for interacting with head-mounted displays (HMDs) have been proposed. While these techniques have both benefits and drawbacks dependent on the current situation of the user, research on interacting with HMDs has not concluded yet. In this paper, we add to the body of research on interacting with HMDs by exploring foot-tapping as an input modality. Through two controlled experiments with a total of 36 participants, we first explore direct interaction with interfaces that are displayed on the floor and require the user to look down to interact. Secondly, we investigate indirect interaction with interfaces that, although operated by the user's feet, are always visible as they are floating in front of the user. Based on the results of the two experiments, we provide design recommendations for direct and indirect foot-based user interfaces.
Joint graph optimization and projection learning for dimensionality reduction

Nowadays, graph-based dimensionality reduction approaches have become more and more popular due to their successful utilization for classification and clustering tasks. In these approaches, how to establish an appropriate graph is critical. To address this issue, a novel graph-based dimensionality reduction framework termed joint graph optimization and projection learning (JGOPL) is proposed in this paper. Compared with existing dimensionality reduction approaches, there are three main advantages of JGOPL. First, through performing the graph optimization and low-dimensional feature learning simultaneously, our proposed approach can accomplish the tasks of graph construction and dimensionality reduction jointly. Second, the l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved. Third, in order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization. Extensive classification and clustering experiments are carried out on seven publicly available databases to demonstrate the effectiveness of our approach. At last, the locality constraint and graph optimization strategy proposed in this paper is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks (such as spectral clustering).
XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.
Towards the definitive evaluation framework for cross-platform app development approaches

Mobile app development is hindered by device fragmentation and vendor-specific modifications. Boundaries between devices blur with PC-tablet hybrids on the one side and wearables on the other. Future apps need to support a host of app-enabled devices with differing capabilities, along with their software ecosystems. Prior work on cross-platform app development concerned concepts and prototypes, and compared approaches that target smartphones. To aid choosing an appropriate framework and to support the scientific assessment of approaches, an up-to-date comparison framework is needed. Extending work on a holistic, weighted set of assessment criteria, we propose what could become the definitive framework for evaluating cross-platform approaches. We have based it on sound abstract concepts that allow extensions. The weighting capabilities offer customisation to avoid the proverbial comparison of apples and oranges lurking in the variety of available frameworks. Moreover, it advises on multiple development situations based on a single assessment. In this article, we motivate and describe our evaluation criteria. We then present a study that assesses several frameworks and compares them to Web Apps and native development. Our findings suggest that cross-platform development has seen much progress but the challenges are ever growing. Therefore, additional support for app developers is warranted.
Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, called RDF property filtering, filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.