Reviewerly/exHarmony huggingface dataset:
Cost Minimization for Service Providers with Impatient Consumers in Cloud Computing

In this paper, we consider the cost minimization problem for scheduling stochastic service requests to heterogenous servers in cloud computing. Service requests are impatient with different maximizing waiting time. Using queuing theory, a queuing system model is constructed. An algorithm framework is proposed to minimize the cost. The actual expected waiting time of service requests is analyzed. The rejection probability of the system is obtained. Comparing the rejection probability of the system to a given system availability, suitable servers are selected to minimize the cost. Based on the proposed framework, algorithms with different components are compared. Experimental results show that the algorithm with the mixed server selection strategy outperforms the others on efficiency.
A Multi-layer Ontology for Data Processing Techniques

Currently, data processing technology is applied in various fields. But non-expert researchers are always confused about its diversity and complex processes. Especially due to the instability of real data, the preparation process for extracting information is lengthy. At the same time, different analysis algorithms are based on different mathematical models, so they are suitable for different situations. In the real data processing process, inappropriate data forms and algorithm selections always lead to unsatisfactory results. This paper proposes a multilayer description model of data processing algorithms and implements it based on ontology technology. The model provides a multi-layered structure including data pre-processing, data form conversion, and output model selection so that the user can obtain a complete data processing process from it. The extensibility and interpretability of ontology also provide a huge space for model improvement. The multilevel structure greatly reduces its complexity.
Platelets and Hematocrit in the Survival Model of Dengue Hemorrhagic Fever (DHF) Sufferers in Palopo

This study aims to apply cox regression analysis to predict the patient's survival to dengue disease occurring in Palopo. This study uses clinical data, namely the results of laboratory tests to determine the effect on the patient's healing period. Laboratory test results used are platelets and hematocrit. By using the MPLE method to obtain parameter estimation in the cox regression model, it is known that platelets have a stronger effect for patient resistance on DHF than hematocrit. This is based on the p-value obtained from the analysis less than alpha (0.05), which is equal to 0.0433. Patients who had an average platelet below normal when experiencing DHF are longer in their recovery period. In addition, patients with DHF ≤ 2 days, the probability to survive and recover was 90%.
Fractional‐integral‐operator‐based improved SVM for filtering salt‐and‐pepper noise

To protect edge and texture information, when removing salt-and-pepper (SP) noise in grayscale images, a support vector machine (SVM) denoising method is employed. First, a mapping relation between the neighborhood signal pixels and the central pixel is designed. The size of the neighborhood is a 5 × 5 region, with a signal pixel in the center. In this region, a 25-dimensional input sample is constructed using the correlation between the neighborhood pixels and the eight-direction fractional integral operators. The center signal pixel acts as the corresponding output sample to provide a training sample. Then, the SVM is trained with all training samples, and the SVM denoising model is obtained. Next, the center pixel value is estimated using the SVM denoising model in every 5 × 5 region with a noise pixel in the center. Finally, the noise pixel values are replaced with the estimated values of the SVM. The experiments demonstrate that the best denoising effect is obtained when the fractional integral order is in the range of 1.8 ± 0.1. The proposed method produces a visually pleasing denoised image and obtains superior image quality assessment indicators. Our method has significant advantages compared with state-of-the-art denoisers when a low level of noise is present.
ICME Grand Challenge on Short Video Understanding

In recent years, short video applications have entered our lives and lots of short videos are watched by users every day. ICME and Byte-Dance corporation sponsor the grand challenge for better understanding video content and recommending what users like. The challenge provides an incremental multi-modal video dataset including face features, title features, video content features, audio content features and users' behaviors features consisting of thousands of different users and millions of different videos. This paper reports the implementation details in the grand challenge. We have made different attempts and explorations in feature engineering and model structure, finally we got good results and achievements. The details of feature design, model experiments and ensemble learning are described. In this paper, we provides the implementation details of the whole challenge.
AI-GAN: Signal De-Interference via Asynchronous Interactive Generative Adversarial Network

Interfering signals, such as rain streak, haze, noise, etc, introduce various types of visibility degradation on original clean signals. Traditional algorithms tackle the signal de-interference problem by the way of signal removal, which usually causes over-smoothness and unexpected artifacts. Hereby, this paper attempts to solve this problem from a totally different perspective of signal decomposition, and introduces the interaction and constraints between the two decomposed signals during the restoration procedure. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN), which decomposes the degraded signal into original and interfering parts progressively through a double branch structure. Each branch employs an asynchronous synthesis strategy for the corresponding generator and interacts with each other by exchanging the feed-forward signal values and sharing the corresponding feedback gradients, achieving an effect of mutual adversarial optimization. The proposed AI-GAN shows significant qualitative and quantitative improvement on general signal de-interference tasks such as deraining, dehazing, and denoising.
Camera-Based Selection with Cardboard HMDs

We present a study of selection techniques for low-cost mobile VR devices, such as Google Cardboard, using the outward facing camera on modern smartphones. We compared three selection techniques, air touch, head ray, and finger ray. Initial evaluation indicates that hand-based selection (air touch) was the worst. A ray cast using the tracked finger position offered much higher selection performance. Our results suggest that camera-based mobile tracking is feasible with ray-based techniques.
An active region corrected method for weakly supervised aircraft detection in remote sensing images

Aircraft detection is a challenging task in remote sensing images which attract increasing attention in recent years. Existing methods based on fully-supervised convolutional neural networks (CNN) require expensive labeling information such as bounding box, which is time consuming and difficult to obtain. Recently, weakly supervised methods only using image-level labels has drawn increasing attention in natural imagery. An approach called class activation map (CAM) based on weakly supervised performs well in natural scene images for object detection, but there is a problem when using it in remote sensing images: inaccurate localization. In this paper, we propose a method called Active Region Corrected (ARC) to locate aircraft accurately. We find that generating the localization map in the classified network by extracting the feature before the last pooling layer contains more accurate position information but a lot of noise, and then we use the CAM to generate a localization map which contains rough location information of aircraft. Combining these two localization maps we can get the exact position of the aircraft. Experiments conducted on data set verify that our proposal obtains a superior performance on aircraft detect and localization in remote sensing images.
Prevalence of Visualization Techniques in Data Mining

Due to huge collections of data, exploration and analysis of vast data volumes has become very difficult. For dealing with the flood of information, integration of visualization with data mining can prove to be a great resource. With the development of a large number of information visualization techniques over the last decades, the exploration of large sets of data is well supported. In this chapter, we present a detailed explanation of data mining and visualization techniques. The chapter also discusses how visualization can be applied in real life applications where data needs to be mined as the most important and initial requirement. A detailed explanation of graphical tools and plotting various types of plots for sample datasets using R software is given.
Plaintext recovery attacks against linearly decryptable fully homomorphic encryption schemes

Homomorphic encryption primitives have the potential to be the main enabler of privacy preserving computation delegation to cloud environments. One of the strategies which has been explored to reduce their significant computational overhead with respect to cleartext computation is the one of the so-called noise-free homomorphic encryption schemes. In this work, we present an attack against fully homomorphic encryption primitives where a distinguisher for a single plaintext value exists. As our case studies, we employ two noise-free homomorphic encryption schemes where such a property holds, providing detailed attack procedure against them. We validate the effectiveness and performance of our attacks on prototype implementations of the said schemes, and suggest a countermeasure tailored to the schemes at hand.
Pavilion: Bridging Photo-Realism and Robotics

Simulation environments play a centric role in the research of sensor fusion and robot control. This paper presents Pavilion, a novel open-source simulation system, for robot perception and kinematic control based on the Unreal Engine and the Robot Operating System (ROS). The novelty of this work includes threefold: (1) developing a shader-based method to generate optical flow ground-truth data with the Unreal Engine, (2) developing a toolset to remove binary incompatibility between ROS and the Unreal Engine to enable real-time interaction, and (3) developing a method to directly import Simulation Description Format (SDF) robot models into the Unreal Engine at runtime. Finally, a Gazebo-compatible real-time simulation system is developed to enable training and evaluation of a large number of sensor fusion, planning, decision and control algorithms. The system can be implemented on both Linux and macOS, with the latest version of ROS. Various experiments have been performed to validate the superior performance of the proposed simulation environment over other state-of-the-art simulators in terms of number of modalities, simulation accuracy, latency and degree of integration difficulty.
Comparing the power of cops to zombies in pursuit-evasion games

We compare two kinds of pursuit-evasion games played on graphs. In Cops and Robbers, the cops can move strategically to adjacent vertices as they please, while in a new variant, called deterministic Zombies and Survivors, the zombies (the counterpart of the cops) are required to always move towards the survivor (the counterpart of the robber). The cop number of a graph is the minimum number of cops required to catch the robber on that graph; the zombie number of a graph is the minimum number of zombies required to catch the survivor on that graph. We answer two questions from the 2016 paper of Fitzpatrick, Howell, Messinger, and Pike. We show that for any integers m and k such that m≥k≥1, there is a graph with zombie number m and cop number k. We also show that the zombie number of the n-dimensional hypercube is ⌈2n∕3⌉.
Fuzzy Posture Matching for Pain Recovery Using Yoga

Owing to the present sedentary lifestyle, most of the office employees suffer from pain at different body parts and in turn they need to consult with doctors frequently. The doctors prescribe certain exercises (mostly in the form of yoga) to the patients to overcome the stiffness and pain. Yoga is an ancient practice which involves specific movements, and breathing exercises used to improve overall health. With the rapid advancement in computer vision and video processing technologies, automatic detection of yoga postures’ correctness will help to motivate the patients to do them accurately. Here lies the novelty of our proposed work of automatic detection of yoga postures for pain recovery using type-1 fuzzy set. As care must be taken that the exercises are done appropriately, so a doctor-consulted database of yoga postures has been taken into account. The postures are detected by Kinect sensor first; then the recognized skeletal forms of the postures are processed to obtain the feature space. While performing the yoga, the exercise pattern may vary for different subjects due to their body structure. So to deal with this uncertainty, fuzzy logic has been used here. The proposed system is an innovative work in this domain and outperforms all the existing ones in other posture recognition domains also.
An effective comparison protocol over encrypted data in cloud computing

Privacy-preserving is one of the most important concerns of cloud computing users. Encryption guarantees data security but makes data processing less convenient. Although many calculations in ciphertext domain are supported by some cryptosystems, the magnitude relation comparison over encrypted data is still difficult to be solved. In this paper, we proposed a comparison protocol for secure cloud computing using homomorphic encryption and incomplete re-encryption. Homomorphic encryption enables the process of data with some basic mathematical operations to be securely outsourced to cloud servers. Incomplete re-encryption is a novel method proposed by us, it can support ciphertexts to be decrypted to new plaintexts which maintains the magnitude relation but no original plaintexts leaked. Two cloud servers are involved in our protocol, calculator is responsible for the homomorphic operations on ciphertexts, and when the magnitude relation between the results needs to be known, calculator will ask comparator for assistance. The security and performance analysis demonstrate the security and effectiveness of the proposed protocol.
Phase analysis error reduction in the Fourier transform method using a virtual interferogram

We propose a method for reducing artifactual phase errors inherent to the Fourier transform method (FTM) for fringe analysis. The phase obtained by the FTM is subject to ripple errors at the boundary edges of the fringe pattern where fringes become discontinuous. We note that these artifactual phase errors are found to have certain systematic relations to the form of the phase, amplitude, and background intensity distributions, which can be modeled by low-order polynomials, such as Zernike polynomials, in many cases of practical interest. Based on this observation, we estimate the systematic ripple errors by analyzing a virtual interferogram that is numerically created for a fringe model with known phase, amplitude, and background intensity distributions. Starting from a rough initial guess, the virtual interferogram is sequentially improved by an iterative algorithm, and the estimated errors are finally subtracted from the experimental data. We present the results of simulations and experiments that demonstrate the validity of the proposed method.
Characterizing Conflicting User Values for Cyber Authentication Using a Virtual Public Values Forum

Authentication is a major component in protecting the security of online user services. An effective implementation of security policies requires compliance from users, who are one class of key stakeholders in the cybersecurity policy decision problem. We examine this multiple stakeholder decision problem by conducting a virtual public values forum, a policy decision structuring methodology to characterize stakeholder values by eliciting essential trade‐offs among conflicting objectives. We assess trade‐offs for a sample of users to explore heterogeneity in user values and the relationship of trade‐offs to both individual user characteristics and online context. We obtained responses from 265 online service users and elicited their trade‐offs among three conflicting objectives related to authentication security: (1) maximizing security, (2) maximizing convenience, and (3) minimizing cost. Using an additive multiattribute value model with four attributes, we obtained scaling coefficients that denote the relative valuation of each attribute to the decision maker and discovered that for the attribute ranges considered, security followed by cost receive the highest priority; however, there is a group of respondents who consider convenience to have higher valuation than either cost or security. We also explore the relationships between user characteristics (self-efficacy, response efficacy, response cost, and perceived severity) and the calculated scaling coefficients.
Evacuation route and evacuation shelter planning for tsunami hazard in Pangandaran District

The 2006 earthquake and tsunami which happened in Pangandaran caused the worst damage in the area. As we know, time and accessibility are very crucial in terms of evacuating people. There are several ways to reduce the impact of tsunami, such as evacuation direction map, evacuation route, evacuation shelter, and guide signs. The purpose of this research is to find out the potential location for the shelter evacuation and also the fastest evacuation route. Some variables used in this research are topography, accessibility, and travel time. This research also uses the GIS modeling using tools network analysis method in ArcGis to make the modeling for evacuation route and coverage area for evacuation shelter. Furthermore, this research also calculates capacity for evacuation shelter, the spatial data, the road condition, and the topography area. From the result, it can be concluded that the fastest evacuation route is slightly sloped, spacious enough, and approximately in a good condition. The location for tsunami evacuation shelter in Pangandaran is located in higher area and can be reached through the near settlement in the evacuation zone.
The Ontological Import of Adding Proper Classes

In this article, we analyse the ontological import of adding classes to set theories.We assume that this increment is well represented by going from ZF system to
Global Auto-Regressive Depth Recovery via Iterative Non-Local Filtering

Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy.The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data.In this paper, we present a novel framework for obtaining high-quality depth images and multi-view depth videos from simple acquisition systems.We first propose a single depth image recovery algorithm based on auto-regressive (AR) correlations.A fixedpoint iteration algorithm under the global AR modeling is derived to efficiently solve the large-scale quadratic programming.Each iteration is equivalent to a nonlocal filtering process with a residue feedback.Then, we extend our framework to an ARbased multi-view depth video recovery framework, where each depth map is recovered from low-quality measurements with the help of the corresponding color image, depth maps from neighboring views, and depth maps of temporally adjacent frames.AR coefficients on nonlocal spatiotemporal neighborhoods in the algorithm are designed to improve the recovery performance.We further discuss the connections between our model and other methods like graph-based tools, and demonstrate that our algorithms enjoy the advantages of both global and local methods.Experimental results on both the Middleburry datasets and other captured datasets finally show that our method is able to improve the performances of depth images and multi-view depth videos recovery compared with state-of-the-art approaches.
The impact of individual simulator experiences on usability and driving behavior in a moving base driving simulator

Individual reactions to driving simulators are subject to considerable interindividual differences. This includes the expression of motion sickness-like symptoms, simulator sickness (SS), and the subjective feeling of being in the virtual environment, spatial presence (SP). These differences can decrease the statistical power of simulator studies, i.e., lower the probability of finding actual existing effects. The study at hand explored the impact of SS and SP on usability and mean speed in driving simulations using different navigation systems. SS and SP were further measured using both post-exposure questionnaires and repeated online measurements. A mixed models analysis of data from 57 drivers could establish an effect of differing navigation systems on usability and mean speed. The further inclusion of SS and SP in the analysis of these effects was beneficial. SP also mediated an indirect effect of navigation systems on usability. Repeated online measurements did not offer an advantage compared to questionnaires for the prediction of usability. However, for mean speed, they improved predictions compared to questionnaires for both SP and SS. The study highlights the benefits of assessing individual reactions to simulators and illustrates that repeated online measures are more suitable than post-exposure questionnaires for the study of continuous responses.
Proactive UAVs for Cognitive Contextual Awareness

Unmanned vehicle systems are often teleoperated, semiautonomous, and strictly dependent on human operators. In complex and dynamic environments, unmanned vehicles should be autonomous in a stricter sense, which means they should exhibit a human-like behavior to be capable of accurately perceiving the environment; understanding the situation, locating and interacting with environmental elements; and reporting solutions to humans. In order to address these desiderata, a modeling of a proactive, context-aware unmanned system is presented. Precisely, the system framework is designed for an unmanned aerial vehicle (UAV) that flies over an area, and collects data in the form of video frames, sensor values, etc. It recognizes situations, senses scene object and environment data, acquires the awareness about the evolving scenes, and, finally, takes a decision based on the perception of the overall scenario. The system design is based on two primary building blocks: 1) the semantic web technologies that provide the high-level object description in the tracked scenario, and 2) the fuzzy cognitive map model that provides the cognitive accumulation of spatial knowledge in order to discern specific situations that need a decision. Although the paper presents a UAV-based surveillance system model, its applicability is shown based on a realistic case study (viz., broken car on the highway); moreover, several possible scenario configurations have been simulated to assess the criticality level perceived by the system (UAV) in a given situation and to validate the effective response/decision in the case of critical situations.