Reviewerly/exHarmony huggingface dataset:
Spatiotemporal road scene reconstruction using superpixel-based Markov random field

Scene reconstruction based on image rendering is an indispensable but challenging task in computer vision and intelligent transportation systems. We propose a framework for reconstructing road scenes as 3D corridor models and consisting of two stages: road detection and scene reconstruction. Road detection is realized by the novel superpixel-based Markov random field. The data fidelity term in the energy function is jointly computed according to superpixel features of color, texture and location. The smoothness term is established from the interaction of spatiotemporally adjacent superpixels. In the subsequent scene reconstruction, the foreground and background regions are modeled independently. Experiments on road detection demonstrate that our proposal outperforms state-of-the-art methods in both accuracy and computation speed. Scene reconstruction experiments further confirm that the scene models retrieved by the proposed method have higher correctness ratio, and can support several applications.
RV-CNN: Flexible and Efficient Instruction Set for CNNs Based on RISC-V Processors

Convolutional Neural Network (CNN) has gained significant attention in the field of machine learning, particularly due to its high accuracy in character recognition and image classification. Nevertheless, due to the computation-intensive and memory-intensive character of CNN, general-purpose processors which usually need to support various workloads are not efficient for CNN implementation. Therefore, a great deal of emerging CNN-specific hardware accelerators is able to improve efficiency. Although existing accelerators are significantly efficient, they are often inflexible or require complex controllers to handle calculations and data transfer. In this paper, we analyze classical CNN applications and design a domain-specific instruction set of 9 matrix instructions, called RV-CNN, based on the promising RISC-V architecture. By abstracting CNN into instructions, our design possesses a higher code density and provides sufficient flexibility and efficiency for CNN than general-purpose ISAs. Specifically, the proposed instructions are extended to RISC-V ISA as custom instructions. Besides, we also introduce micro-architectural optimizations to increase computational density and reduce the required memory bandwidth. Finally, we implement the architecture with the extended ISA and evaluate it with LeNet-5 on the datasets (MNIST, Caltech101, and Cifar-10). Results show that compared with the Intel Core i7 processor and Tesla k40c GPU, our design has 36.09x and 11.42x energy efficiency ratio and 6.70x and 1.25x code density respectively.
Estimating Downlink Throughput from End-user Measurements in Mobile Broadband Networks

In recent years, Downlink (DL)throughput estimation in Mobile Broadband (MBB)networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G)systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS)to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged. In this paper, we propose a supervised Machine Learning (ML)solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over-the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled testing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experiment as a Service (EaaS)on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE)of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15 % can be achieved for a MdAPE of 20%.
Superhuman sports in mixed reality: the multi-player game League of Lasers

In recent years, many promising developments have taken place around augmented, virtual and mixed-reality technology.One could wonder whether these technologies can be combined to yield a mix of video games and sports, involving physical activities previously deemed impossible: creating a superhuman sport.This work investigates how mixed-reality can be used to create a fun, intuitive and engaging superhuman sport.To this end, the game League of Lasers was developed, in which two teams compete in a mix between football and Pong, using the physical movement of the players as main means to interact within mixed-reality.An evaluation of League of Lasers with a user study with 32 participants showed that League of Lasers is perceived as a fun and immersive skill-based game.
Simulating a Futuristic Fire Pump Panel in Virtual Reality

Based on input and feedback from firefighters, we built a futuristic pump panel simulator using consumer VR hardware, basic electronics components, and simple physical props. The simulator mixes the physical and the virtual together in order to demonstrate how passive haptics can be used to enhance the user experience. This prototype will be used to explore how VR might be used with future first-responder training.
Emotion Recognition in Gamers Wearing Head-mounted Display

Wearing head-mounted display (HMD) makes previous research regarding emotion recognition using machine vision ineffective since they utilized entire face images for training. In this paper, we trained the convolutional neural networks (CNNs) which are capable of estimating the emotions from the images of a face wearing a HMD by hiding eyes and eyebrows from existing face-emotion dataset. Our analysis based on the class activation maps show that it is capable of classifying emotions without the eyes and the eyebrows which ar to serve useful information in recognizing emotions. This implies the possibility of estimating the emotions from the images of humans wearing HMDs using machine vision.
A Hybrid RTK GNSS and SLAM Outdoor Augmented Reality System

In the real world, we are surrounded by potentially important data. For example, military personnel and first responders may need to understand the layout of an environment, including the locations of designated assets, specified in latitude and longitude. However, many augmented reality (AR) systems cannot associate absolute geographic coordinates with the coordinate system in which they track. We describe a simple approach for developing a wide-area outdoor wearable AR system that uses RTK GNSS position tracking to align together and georegister multiple smaller maps from an existing SLAM tracking system.
Design of a New Chaotic System Based on Van Der Pol Oscillator and Its Encryption Application

The Van der Pol oscillator is investigated by the parameter control method. This method only needs to control one parameter of the Van der Pol oscillator by a simple periodic function; then, the Van der Pol oscillator can behave chaotically from the stable limit cycle. Based on the new Van der Pol oscillator with variable parameter (VdPVP), some dynamical characteristics are discussed by numerical simulations, such as the Lyapunov exponents and bifurcation diagrams. The numerical results show that there exists a positive Lyapunov exponent in the VdPVP. Therefore, an encryption algorithm is designed by the pseudo-random sequences generated from the VdPVP. This simple algorithm consists of chaos scrambling and chaos XOR (exclusive-or) operation, and the statistical analyses show that it has good security and encryption effectiveness. Finally, the feasibility and validity are verified by simulation experiments of image encryption.
PwAdaBoost: Possible world based AdaBoost algorithm for classifying uncertain data

Possible world has become one of the most effective tools to deal with various types of data uncertainty in uncertain data management. However, few uncertain data classification algorithms are proposed based on possible world. Most existing uncertain data classification algorithms are simply extended from traditional classification algorithms for certain data. They deal with data uncertainty based on relatively ideal probability distribution and data type assumptions, thus are difficult to be applied for various application scenarios. In this paper, we propose a novel possible world based AdaBoost algorithm for classifying uncertain data, called PwAdaBoost. In the training procedure, PwAdaBoost uses the possible world set generated from the uncertain training set sampled in each iteration to train the sub-basic classifiers, and employs the possible world set generated from the whole uncertain training set to adjust the weights of the sub-basic classifiers and detect the quality of the basic classifiers. In the prediction procedure, PwAdaBoost utilizes the possible world set generated from the predicted object to get the results of the basic classifiers via majority voting and weighted voting. Furthermore, we analyze the stability and give the parallelization strategies for its training procedure and prediction procedure respectively. The proposed PwAdaBoost can deal with various types of data uncertainty, and use any existing classification algorithms for certain data to serve for uncertain data. As far as we know, it is the first ensemble classification algorithm for uncertain data. Extensive experiment results demonstrate the superiority of our proposed algorithm in terms of effectiveness and efficiency.
Learning from Difference

Substantial effort has been spent on extending specification notations and their associated reasoning techniques to software product lines (SPLs). Family-based analysis techniques operate on a single artifact, referred to as a family model, that is annotated with variability constraints. This modeling approach paves the way for efficient model-based testing and model checking for SPLs. Albeit reasonably efficient, the creation and maintenance of family models tend to be time consuming and error-prone, especially if there are crosscutting features. To tackle this issue, we introduce FFSMDiff, a fully automated technique to learn featured finite state machines (FFSM), a family-based formalism that unifies Mealy Machines from SPLs into a single representation. Our technique incorporates variability to compare and merge Mealy machines and annotate states and transitions with feature constraints. We evaluate our technique using 34 products derived from three different SPLs. Our results support the hypothesis that families of Mealy machines can be effectively merged into succinct FFSMs with fewer states, especially if there is high feature sharing among products. These indicate that FFSMDiff is an efficient family-based model learning technique.
Removal of noise in MRI images using a block difference‐based filtering approach

Abstract Magnetic resonance imaging (MRI) images are frequently sensitive to certain types of noises and artifacts. The denoising of MRI images is essential for improving visual quality and reliability of the quantitative analysis of diagnosis and treatment. In this article, a new block difference‐based filtering method is proposed to denoise the MRI images. First, the normal MRI image is degraded by a certain percentage of noise. The block difference between the intensity of the normal and noisy MRI is computed, and then it is compared with the intensity of the blocks of the normal MRI image. Based on the comparison, the pixel weights are updated to each block of the denoised MRI image. Observational results are brought out on the BrainWeb and BraTS datasets and evaluated by performance metrics such as peak signal‐to‐noise ratio, structural similarity index measures, universal quality index, and root mean square error. The proposed method outperforms the existing denoising filtering techniques.
Tunable photon statistics in parametrically amplified photonic molecules

The proposal for single-photon generation utilizing quantum interference of two coupled cavity modes with weak Kerr nonlinearity under resonant coherent driving has been successfully put forward in previous work [T. C. H. Liew and V. Savona, Phys. Rev. Lett. 104, 183601 (2010)]. Here we suggest an alternative scheme to explore the statistical properties of the photons and their control in a parametrically amplified photonic molecule that is composed of a pair of coupled cavities, both containing the degenerate optical parametric amplifier (DOPA), by means of extending the model of Liew and Savona. Due to the destructive quantum interference effect between different paths for two-photon excitation, the photons in both cavity modes can exhibit a strong antibunching effect when one of the two cavities is coherently driven by an external laser field. In order to intuitively understand the strong photon antibunching phenomenon occurring in our DOPA photonic molecule, the approximate analytical solutions of the system are provided via the Schr\"odinger equation and further are compared with the full numerical solutions via the master equation. The in-depth results reveal that the two solutions are in good agreement. For the two cavity modes, we achieve the optimal antibunching conditions in terms of the nonlinear gain and the strength ratio between the two pump laser fields based on analytical calculations, gaining deep insight into the rich physics of photon statistics. We also show that the statistical properties of the photons can be well controlled by regulating the relative phase between the two pump laser fields and the tunneling rate between the two cavity modes. Furthermore, we address the second-order cross-correlation function between the two cavity modes and find that a strong intermodal antibunching can be efficiently generated. Our approach via the DOPA photonic molecule architecture can be useful to generate tunable single-photon sources or controlled photonic quantum gates.
Hybrid weapon detection algorithm, using material test and fuzzy logic system

As crime rates continue to hike, global security system is called to question. The rise of criminality puts to test the existing apparatus in terms of decision accuracy. Local security has so much trouble monitoring criminality, even with the installation of state of the art Closed Circuit television system (CCTV). At CCTV-installed locations, one operator can be assigned to monitor as many as seven to ten footages. This increases high false alarm rates and low efficiency of security apparatus. In this paper, a hybrid automatic detection system is proposed, using a combination of metallic test and image test arms plus a decision making Fuzzy logic system. The proposed algorithm receives additional parameters from metallic test, which enhance accurate conclusions and reduce false alarm rate. In this study, a proactive system capable of detecting potentially harmful objects like guns and knives before they cause harm in controlled areas, is achieved.
Exclusive feature selection and multi-view learning for Alzheimer’s Disease

In Alzheimer's Disease (AD) studies, high dimension and small sample size have been always an issue and it is common to apply a dimension reduction method to predict the early diagnosis of AD. In this paper, we propose a multi-view feature selection algorithm embedded with exclusive lasso learning and sparse learning. It extracts the feature subsets that best represent the symptoms of patients through feature selection, so as to reduce the dimension and achieve a better diagnosis rate. Firstly, in order to overcome the limitation of non-overlapping, the features under different view are clustered by fuzzy C-means clustering. Then, the exclusive group lasso learning is performed according to the clustering results and each view is sparsely learned through the l2,1-norm, resulting in better removal of redundant features. Finally, the results of each view are combined to obtain the final features subsets. This exclusive lasso learning combined through multiple views is novel in clinical practice and can effectively target AD. At the same time, the experimental results show that our method could achieve better results compared to its competing methods.
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements

We present an approach to depth estimation that fuses information from a stereo pair with sparse range measurements derived from a LIDAR sensor or a range camera. The goal of this work is to exploit the complementary strengths of the two sensor modalities, the accurate but sparse range measurements and the ambiguous but dense stereo information. These two sources are effectively and efficiently fused by combining ideas from anisotropic diffusion and semi-global matching.We evaluate our approach on the KITTI 2015 and Middlebury 2014 datasets, using randomly sampled ground truth range measurements as our sparse depth input. We achieve significant performance improvements with a small fraction of range measurements on both datasets. We also provide qualitative results from our platform using the PMDTec Monstar sensor. Our entire pipeline runs on an NVIDIA TX-2 platform at 5Hz on 1280×1024 stereo images with 128 disparity levels.
Snatched secrets: Cybercrime and trade secrets modelling a firm's decision to report a theft of trade secrets

Cybercrime and economic espionage are increasing problems for firms. We build on US FBI policy to frame the interaction between a cybercrime victim firm and a government security agency. We bring together several strands in the literature to model the strategies of the firm, which has suffered a cyber breach and theft of trade secrets, and the government security agency, which must investigate and prosecute crimes. We investigate the interactions between these two players, in which the firm has private information about its cybersecurity investment. This investment level is unknown to the security agency, which must nonetheless decide how to prioritize reported crime. We model this asymmetric information problem within a game theoretic signaling framework derived from Becker's work in crime and punishment. We suggest that such a framework can inform policy to encourage security investments by firms and more efficient resource utilization by security agencies. We particularly focus on an illustrative stylized example to highlight how our modelling approach can be helpful. In this example we compare two worlds; one where all security breaches become public knowledge and another where only reported breaches become public knowledge. We then formulate two potentially testable Hypotheses and several implications of these Hypotheses. Case studies and a policy analysis further highlight how our framework plays out in reality.
A Seeding-based GA for Location-Aware Workflow Deployment in Multi-cloud Environment

To gain technical and economic benefits, various enterprises are increasingly moving their workloads to the cloud. Multi-cloud environment makes it possible to coordinate access and utilize multiple cloud resources. When business application developers host their business process in the cloud, they face the issue of choosing which cloud to deploy the instance-intensive business workflows. However, the existing studies rarely consider the optimization techniques for organizing cloud services with respect to various criteria, such as cost and performance. In this paper, we propose a seeding-based GA approach to address the multi-cloud workflow deployment problem, i.e. selecting and leasing virtual machines (VMs) to minimize deployment cost and response time. Experimental results show that the proposed GA approach with seeding strategy outperforms the existing approach proposed in the literature and standard GA algorithm.
Haptic Force Guided Sound Synthesis in Multisensory Virtual Reality (VR) Simulation for Rigid-Fluid Interaction

This paper tackles a challenging problem for interactive rigid-fluid interaction sound synthesis. One core issue of the rigid-fluid interaction in multisensory VR system is how to balance the algorithm efficiency, result authenticity and result synchronization. Since the sampling rate of audio is far greater than visual and haptic modalities, sound synthesis for a multisensory VR system is more difficult than visual simulation and haptic rendering, which still remains an open challenge until now. Therefore, this paper focuses on developing an efficient sound synthesis method tailored for a multisensory system. To improve the result authenticity while ensuring real time performance and result synchronization, we propose a novel haptic force guided granular sound synthesis method tailored for sounding in multisensory VR systems. To the best of our knowledge, this is the first step that exploits haptic force feedback from the tactile channel for guiding sound synthesis in a multisensory VR system. Specifically, we propose a modified spectral granular sound synthesis method, which can ensure real time simulation and improve the result authenticity as well. Then, to balance the algorithm efficiency and result synchronization, we design a multi-force (MF) granulation algorithm which avoids repeated analysis of fluid particle motion and thereby improves the synchronization performance. Various results show that the proposed sound synthesis method effectively overcomes the limitations of existing methods in terms of audio modality, which has great potential to provide powerful technological support for building a more immersive multisensory VR system.
Fireworks Algorithm for Multimodal Optimization Using a Distance-based Exclusive Strategy

We propose a distance-based exclusive strategy to extend fireworks algorithm as a niche method to find out multiple global/local optima. This strategy forms sub-groups consisting of a firework individual and its generated spark individuals, each sub-group is guaranteed not to search overlapped areas each other. Finally, firework individuals are expected to find different global/local optima. The proposed strategy checks the distances between a firework and other fireworks which fitness is better than that of the firework. If the distance between two firework individuals is shorter than the sum of their searching radius, i.e. amplitude of firework explosions, these two firework individuals are considered to search overlapped area. Thus, the poor firework is removed and replaced by its opposite point to track multiple optima. To evaluate the performance of our proposed strategy, enhanced fireworks algorithm (EFWA) is used as a baseline algorithm and combined with our proposal. We design a controlled experiment, and run EFWA and (EFWA + our proposal) on 8 benchmark functions from CEC 2015 test suite, that is dedicated to single objective multi-niche optimization. The experimental results confirmed that the proposed strategy can find multiple different optima in one trial run.
Classification of Regional Accent Using Speech Rhythm Metrics

In this paper, MSA speech rhythm metrics were used to classify two regional accent (northern vs. southern regions) using an MLP - neural network classifier. Seven rhythm metrics vectors were computed from a speech dataset taken from ALGerian Arabic Speech Database (ALGASD) using both Interval Measures (IM) and Control/Compensation Index (CCI) algorithms. The classifier was trained and tested using different input vectors of speech rhythm measurements. The best accuracy of the NN-classifier was achieved when a combination of all metrics was used (88.6%).