Reviewerly/exHarmony huggingface dataset:
PinpointFly: An Egocentric Position-pointing Drone Interface using Mobile AR

We propose PinpointFly, an egocentric drone interface that allows users to arbitrarily position and rotate a flying drone using position control interactions on a see-through mobile AR where the position and direction of the drone are visually enhanced with a virtual cast shadow. Unlike traditional speed control methods, users hold a smartphone and precisely edit the drone’s motions and directions by dragging the cast shadow or a slider bar on the touchscreen.
An Improved Fuzzy Region Competition-Based Framework for the Multiphase Segmentation of SAR Images

The objective of this article is to investigate a multiphase segmentation framework for synthetic aperture radar (SAR) images, which is proposed based on the idea of the fuzzy region competition-based method. The fuzzy region competition-based framework is highly efficient and can attain good segmentation performances for conventional images. The framework is achieved based on its convexity, which not only ensures the existence of a globally optimized solution but also enables the convex optimization theory-based solving algorithms that are feasible. However, the constraint conditions of the framework that guarantee this convexity probably cannot be satisfied in the segmentation of images corrupted with strong noise. Therefore, applying this method to an SAR image probably produces an unsatisfactory segmentation result. To address this problem, we propose an improved fuzzy region competition-based framework in terms of the hierarchical strategy, such that the framework is always convex during the iterative calculation. The proposed framework inherits the advantages of the fuzzy region competition-based method, as well as that it is able to be applied to the segmentation of images with strong noise. Several experiments are then carried out to test and verify the performance and the robustness of the proposed framework. It demonstrates that the proposed segmentation framework can be applied to various types of SAR images and achieves satisfactory segmentation results.
Variational Bayesian GAN

Generative adversarial network (GAN) has been successfully developing as a generative model where the artificial data drawn from the generator are misrecognized as real samples by a discriminator. Although GAN achieves the desirable performance, the challenge is that the mode collapse easily happens in the joint optimization of generator and discriminator. This study copes with this challenge by improving the model regularization by means of representing the weight uncertainty in GAN. A new Bayesian GAN is formulated and implemented to learn a regularized model from diverse data where the strong modes are flattened via the marginalization and the issues of model collapse and gradient vanishing are alleviated. In particular, we present a variational GAN (VGAN) where the encoder, generator and discriminator are jointly estimated according to the variational Bayesian inference. The experiments on image generation over two tasks (MNIST and CeleA) demonstrate the superiority of the proposed VGAN to the variational autoencoder, the standard GAN and the Bayesian GAN based on the sampling method. The learning efficiency and generation performance are evaluated.
Understanding Users Information Needs and Collaborative Sensemaking of Microbiome Data

Recent years are seeing a sharp increase in the availability of personal omic (e.g. genomes, microbiomes) data to non-experts through direct-to-consumer testing kits. While the scientific understanding of human -omic information is evolving, the interpretation of the data may impact well-being of users and relevant others, and therefore poses challenges and opportunities for CSCW research. We identify the information, interaction, and sense-making needs of microbiomic data users, within the broader context of social omics - the sharing and collaborative engagement with data and interpretation. Analyzing users' discussions on Reddit's r/HumanMicrobiome, we identified seven user needs for microbiome data: reviewing an annotated report, comparing microbiome data, tracking changes, receiving personalized actionable information, curating and securing information, documenting and sharing self experiments, and enhancing the communication between patients and health-care providers. We highlight the ways in which users interact with each other to collaboratively make sense of the data. We conclude with design implications, including tools for better communication with care providers, and for symptom-centered sharing and discussion.
A Granular SVM-Based Method for Top-N Web Services Recommendation

With the wide application of service technologies in various fields, the number of services is increasing dramatically. So, a function is often provided by many services of different QoS (quality of service). Thus, due to the lack of professional knowledge, users often face great difficulties in finding the right services they really need. Hence, accurate and efficient service recommendation is not just an effective way of service advertising, but also an important means to promote user experience. Most of traditional service recommendation methods are based on predictions of QoS values. However, because of the dynamic nature of the Internet, it is hard to guarantee the predicted values are consistent with the actual values. This article proposes a granule distribution-aware SVM (support vector machine) model for service recommendation, namely GDSVM4SR. It takes advantages of granular computing to identify similar users, refine the training service set, and decrease the influence of unfaithful ratings. And then, GDSVM4SR trains an SVM separating hyperplane to sort unknown services and generate recommendations, thus it can avoid the prediction of QoS values. Experimental results show that the proposed GDSVM4SR outperforms several state-of-the-art methods in terms of the efficiency and the precision of recommendation.
Recognition of Plate Identification Numbers Using Convolution Neural Network and Character Distribution Rules

Recognition of plate identification numbers (PINs) is of much importance for the automation of the iron and steel production. The recognition of PINs in industrial site is a challenging problem due to complicated background, low quality of characters. Conventional image processing algorithms are employed to extract the numbers, but it is difficult for these methods to locate and recognize the numbers on the plates in complex industrial production by manually designed features. The end-to-end convolution neural network is employed to solve these problems by automatically extracted features. These features seldom combine the real production rules. A delicate recognition method of PINs using convolution neural network and characters distribution rules is proposed. The PINs are roughly recognized by convolution neural network with Non-Maximum Suppression (NMS), and the PINs are exactly processed using the character distribution rules. Experiment results demonstrate that the method proposed can arrive at a very high recognition rate 96.32% and improve the recognition rate by 54.07% compared with the end-to-end convolution neural network.
Privacy Protection for Recommendation System: A Survey

Abstract Recommendation system have become one of the most well-liked and accepted way to solve overload of information or merchandise. By collecting user’s personal data for processing, suitable lists of information or merchandise are provided to the potential consumers. For online business, recommendation systems have become an extremely effective revenue driver and developed rapidly. Although recommendation systems are great beneficial, directly exposing privacy data to the recommender may lead to leakage of privacy and cause risks. Therefore, quality of recommendation and privacy protection are both important metrics in recommendation. In this paper we present a review investigating development in recommendation systems with privacy protection, including the definition of privacy, classification of privacy leakage, taxonomy of privacy, measuring of privacy risk, policies for privacy protection, approaches of privacy protection and models of privacy protection. We also speculate on the future direction.
Attention Embedded Spatio-Temporal Network for Video Salient Object Detection

The main challenge in video salient object detection is how to model object motion and dramatic changes in appearance contrast.In this work, we propose an attention embedded spatio-temporal network (ASTN) to adaptively exploit diverse factors that influence dynamic saliency prediction within a unified framework.To compensate for object movement, we introduce a flow-guided spatial learning (FGSL) module to directly capture effective motion information in the form of attention based on optical flows.However, optical flow represents the motion information of all moving objects, including movements of non-salient objects caused by large camera motion and subtle changes in background.Therefore, using the flow-guided attention map alone causes the spatial saliency to be influenced by all moving objects rather than just the salient objects, resulting in unstable and temporally inconsistent saliency maps.To further enhance the temporal coherence, we develop an attentive bidirectional gated recurrent unit (AB-GRU) module to adaptively exploit sequential feature evolution.With this AB-GRU, we can further refine the spatiotemporal feature representation by incorporating an accommodative attention mechanism.Experimental results demonstrate that our model achieves superior empirical performance on video salient object detection.Moreover, an experiment on the extended application to unsupervised video object segmentation further demonstrates the generalization ability and stability of our proposed method.
Fast and Practical Snippet Generation for RDF Datasets

Triple-structured open data creates value in many ways. However, the reuse of datasets is still challenging. Users feel difficult to assess the usefulness of a large dataset containing thousands or millions of triples. To satisfy the needs, existing abstractive methods produce a concise high-level abstraction of data. Complementary to that, we adopt the extractive strategy and aim to select the optimum small subset of data from a dataset as a snippet to compactly illustrate the content of the dataset. This has been formulated as a combinatorial optimization problem in our previous work. In this article, we design a new algorithm for the problem, which is an order of magnitude faster than the previous one but has the same approximation ratio. We also develop an anytime algorithm that can generate empirically better solutions using additional time. To suit datasets that are partially accessible via online query services (e.g., SPARQL endpoints for RDF data), we adapt our algorithms to trade off quality of snippet for feasibility and efficiency in the Web environment. We carry out extensive experiments based on real RDF datasets and SPARQL endpoints for evaluating quality and running time. The results demonstrate the effectiveness and practicality of our proposed algorithms.
Scaling Address Parsing Sequence Models through Active Learning

Address parsing is a critical step for map search engines. This component annotates the terms of an address query, e.g. house numbers, road names, administrative units etc., so that the address search engine can resolve the expected result. Deep recurrent models achieve state of the art performance for address parsing; however, scaling such models is problematic. They require a significant amount of term-annotated data which is expensive to acquire. In this paper, active learning significantly reduces the amount of labeled data required to train accurate address parsing models. We demonstrate the efficiency of our approach when cold-starting with human-labeled as well as synthetically-generated data.
Grammar of VR Storytelling: Narrative Immersion and Experiential Fidelity in VR Cinema

As grammar of VR storytelling evolves, we must look beyond the technical capabilities of the medium and associated perceptual immersion, in order to better understand the effect of narrative on the users. This paper presents a qualitative analysis of the experience of a VR Cinema and the experiencer's connection to the narrative. The study attempts to illustrate the significance of narrative immersion with respect to the 360° medium of storytelling in VR. In addition how the various elements in such a narrative lead to experiential fidelity is examined. We believe that the insights gathered would help VR filmmakers in creating effective narrative experiences.
Minimax particle filtering for tracking a highly maneuvering target

Summary In this paper, we propose a new framework of particle filtering that adopts the minimax strategy. In the approach, we minimize a maximized risk, and the process of the risk maximization is reflected when computing the weights of particles. This scheme results in the significantly reduced variance of the weights of particles that enables the robustness against the degeneracy problem, and we can obtain improved quality of particles. The proposed approach is robust against environmentally adverse scenarios, particularly when the state of a target is highly maneuvering. Furthermore, we can reduce the computational complexity by avoiding the computation of a complex joint probability density function. We investigate the new method by comparing its performance to that of standard particle filtering and verify its effectiveness through experiments. The employed strategy can be adopted for any other variants of particle filtering to enhance tracking performance.
LightLock: User Identification System Using Light Intensity Readings on Smartphones

Sensor data on a user's mobile device can often be used to identify the user for improving the security of smartphones in indoor environments. In this paper, we present a novel continuous user identification system called LightLock that collects light sensor data from a user's smartphone and analyzes them to identify a specific user using a machine learning approach. We develop a multi-model system to extract four different feature vectors: (1) absolute time series (ATS); (2) auto-correlation function (ACF); (3) level crossing rate (LCR); and (4) peak readings detection (PRD). To show the feasibility of LightLock, we implemented an Android application and evaluated the performance of LightLock on the dataset collected during a period of 20 days. LightLock achieves over 98% accuracy in identifying a specific user. LightLock also provides an accurate and cost-less alternative solution to existing approaches that require explicit user-smartphone interaction or the high energy consumption of multiple sensors.
Different indicators for Markovian and non-Markovian dynamics

The Markovianity/non-Markovianity of two different systems are discussed by means of the quantum speed limit time and quantum Fisher information. The first system is described by a central mass particle interacts locally with its surrounding particles, while the second and third models consist of a single qubit interacts with a non-detuning Lorentzian cavity and with a thermal reservoir, respectively. For the first model, the large distance between the central particle and the surrounding particles is guaranty for a fixed quantum speed limit, while the driving time plays the central role on the fixed behavior of the quantum speed limit time. Due to the stable behavior of the quantum speed limit time and the quantum Fisher information, the exchange information between the systems and their surrounding is limited. The distance between the central mass particle and its surrounding particle plays the main role on predicating the Markovianity/non-Markovianity. For the second system the driving time is an important parameter that control on the Markovianity/non-Markovianity behavior. Finally the third model proves that non-Markovianity dynamic may increase the speed and the sensitivity of the open system.
Popularity-Based Detection of Malicious Content in Facebook Using Machine Learning Approach

In this world, people are encircled with various online social networks (OSNs) or media platform, various websites and applications. This brings media contents like texts, audio, and videos in daily basis. People share their current status and moments with their belongings to keep in touch by using these tools and software like Twitter, Facebook, and Instagram. The flow of information available in these social networks attract the cybercriminals who misuse this information to exploit vulnerabilities for their illicit benefits such as stealing personal information, advertising some product, attract victims, and infecting user personal system. In this paper, we proposed a popularity-based method which uses PSO-based feature selections and machine learning classifiers to analyze the characteristics of different features for spammer detection in Facebook. Our detection framework result shows higher rate of detection as compared to other techniques.
FairSDP: Fair and Secure Dynamic Cache Partitioning

Due to the widespread usage of cloud computing, and remote execution, where the resources of a computer are shared by multiple executions, hardware security gained more attention. It is possible to extract confidential information such as cryptographic keys through cache-based side-channel attacks as in Meltdown and Spectre attacks. Consequently, studies over secure cache architectures has gained a deeper focus. Secure cache architectures come with an inevitable performance penalty, since there is always an overhead for hiding information from the attackers. Subsequently, the performance degradation is traded off with the improvement in security. In this paper, we analyze the performance deterioration of the existing architectures and come up with a new solution which improves the fairness of the general framework. We propose a secure cache mechanism that respects fairness among the competing threads within a processor. We evaluate FairSDP architecture in 4-threaded and 8-threaded processors. As a result, we show that we can achieve up to 8.7% performance improvement over the baseline and 9.2% better performance compared to the static partitioning on the average, in an 8-threaded system. We also achieve almost identical results in terms of the fairness metric compared to a non-secure dynamic cache partitioning scheme.
Robust 3D Skeleton Tracking based on OpenPose and a Probabilistic Tracking Framework

For human skeleton tracking, conventional methods may directly rely on the Kinect built-in function to extract skeleton joints and realize the tracking function. However, the left-right confusion problem and the self-occlusion problem would make the Kinect joints unstable and incorrect. In this paper, we aim to solve these crucial issues and realize a reliable 3D skeleton tracking system via multiple Kinect cameras. First, the proposed method corrected the Kinect skeleton by referring to the OpenPose-extracted joints. Since OpenPose extracts the joints by analyzing image content, we can differentiate the front side and the back side and thus can correct the left-right confusion. Second, to alleviate the self-occlusion problem, we back-project the 2D OpenPose joints from multiple cameras and form an alternative 3D skeleton. The OpenPose reconstructed joints are treated as robust 3D anchors for multiple skeleton fusion. Last but not least, we introduce the inter-joint constraints into our skeleton tracking framework so that we can trace all joints simultaneously, make sure the skeleton movement consistent, and well maintain the length between neighboring joints. We evaluate our method with challenging actions. A practical system is also built for demonstration. The experimental results show the system can track skeleton stably without error propagation and vibration. The average localization error is also smaller than conventional methods.
On the Sample Complexity of Graphical Model Selection From Non-Stationary Samples

We study conditions that allow accurate graphical model selection from non-stationary data. The observed data is modelled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting of i.i.d. samples as well as the case of samples forming a stationary time series. More generally, our approach applies to any data for which efficient decorrelation transforms, such as the Fourier transform for stationary time series, are available. By analyzing a conceptually simple model selection method, we derive a sufficient condition on the required sample size for accurate graphical model selection based on non-stationary data.
FT-Aurora: A highly available IaaS cloud manager based on replication

In this work we describe FT-Aurora, a highly available IaaS (Infrastructure as a Service) cloud manager that allows cloud resources to be accessed even if the manager itself crashes. FT-Aurora provides flexible and efficient resource management by supporting network programmability. FT-Aurora is based on clusters of cloud managers running on multiple datacenters. After a manager crashes, the corresponding resources remain accessible from any other manager in the cluster. A cluster consists of a group of managers that use fine-grained multi-master replication to share information. Replicated data and resources include both management information stored at the Aurora database, and information used to keep virtual machine images and processes. Replication and monitoring the multiple Aurora instances are available as services that can be easily activated through a GUI button. Experimental results are presented for both the performance and robustness of FT-Aurora.
Proposing Recognition Algorithms For Hand Gestures Based On Machine Learning Model

The paper is being investigated, surveyed and conducted for the purpose of presenting an overview of human hand gesture recognition, including the general steps of hand gesture recognition, the common method and technique. Each of these steps, research directions and a summary of some successful hand gesture models have been announced. We also apply the knowledge learned in the school to analyze, build the program to recognize hand gestures. The program uses image processing techniques to help users interact with computers using common gestures. Users will interact through a camera connected to the computer by hand gestures. The program will analyze the gesture recorded and take action that the user wishes based on hand gestures. This application hopes to replace the mouse and keyboard when using the computer, giving users more flexibility and ease methods.