Reviewerly/exHarmony huggingface dataset:
Shrinking Circles: Adaptation to Increased Curvature Gain in Redirected Walking

Real walking is the most natural way to locomote in virtual reality (VR), but a confined physical walking space limits its applicability. Redirected walking (RDW) is a collection of techniques to solve this problem. One of these techniques aims to imperceptibly rotate the user's view of the virtual scene in order to steer her along a confined path whilst giving the impression of walking in a straight line in a large virtual space. Measurement of perceptual thresholds for the detection of such a modified curvature gain have indicated a radius that is still larger than most room sizes. Since the brain is an adaptive system and thresholds usually depend on previous stimulations, we tested if prolonged exposure to an immersive virtual environment (IVE) with increased curvature gain produces adaptation to that gain and modifies thresholds such that, over time, larger curvature gains can be applied for RDW. Therefore, participants first completed a measurement of their perceptual threshold for curvature gain. In a second session, the same participants were exposed to an IVE with a constant curvature gain in which they walked between two targets for about 20 minutes. Afterwards, their perceptual thresholds were measured again. The results show that the psychometric curves shifted after the exposure session and perceptual thresholds for increased curvature gain further increased. The increase of the detection threshold suggests that participants adapt to the manipulation and stronger curvature gains can be applied in RDW, and therefore improves its applicability in such situations.
A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.
Privacy Preserving Distributed Association Rule Mining Approach on Vertically Partitioned Healthcare Data

The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost.
Application of Neutrosophic Logic to Evaluate Correlation between Prostate Cancer Mortality and Dietary Fat Assumption

This paper presents an epidemiological study on the dietary fat that causes prostate cancer in an uncertainty environment. To study this relationship under the indeterminate environment, data from 30 countries are selected for the prostate cancer death rate and dietary fat level in the food. The neutrosophic correlation and regression line are fitted on the data. We note from the neutrosophic analysis that the prostate cancer death rate increases as the dietary fat level in the people increases. The neutrosophic regression coefficient also confirms this claim. From this study, we conclude that neutrosophic regression is a more effective model under uncertainty than the regression model under classical statistics. We also found a statistical correlation between dietary fat and prostate cancer risk.
Optimal Virtual Machine Placement Based on Grey Wolf Optimization

Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources.
A Distributed Algorithm for the Cooperative Prediction of Power Production in PV Plants

Forecasting the energy production of photovoltaic plants is today an essential tool for asset owners because it has direct economic implications on the net operating income of the plants whose generated energy is sold in competitive electricity markets. In this paper, we propose an innovative distributed decentralized prediction technique for the forecasting of power generated by several PV plants. The prediction technique is based on the Echo State Networks, which are recurrent neural network models very promising in terms of prediction performance and model accuracy. They will be used in conjunction with a distributed learning algorithm and a distributed consensus protocol that makes unnecessary any central coordinator. The technique has been properly conceived for asset owners that hold a wide portfolio of PV systems that are geographically spread out over large areas. The algorithm reveals very efficient, accurate, and scalable performance to any number of plants and it requires only a simple reliable communication channel. Through a real-world example we assess the applicability of such methodology and show its strength.
FuturePose - Mixed Reality Martial Arts Training Using Real-Time 3D Human Pose Forecasting With a RGB Camera

In this paper, we propose a novel mixed reality martial arts training system using deep learning based real-time human pose forecasting. Our training system is based on 3D pose estimation using a residual neural network with input from a RGB camera, which captures the motion of a trainer. The student wearing a head mounted display can see the virtual model of the trainer and his forecasted future pose. The pose forecasting is based on recurrent networks, to improve the learning quantity of the motion's temporal feature, we use a special lattice optical flow method for the joints movement estimation. We visualize the real-time human motion by a generated human model while the forecasted pose is shown by a red skeleton model. In our experiments, we evaluated the performance of our system when predicting 15 frames ahead in a 30-fps video (0.5s forecasting), the accuracies were acceptable since they are equal to or even outperforms some methods using depth IR cameras or fabric technologies, user studies showed that our system is helpful for beginners to understand martial arts and the usability is comfortable since the motions were captured by RGB camera.
Novel framework for image attribute annotation with gene selection XGBoost algorithm and relative attribute model

Material attribute recognition from the visual appearance of attributes is an important problem in computer vision field. However, few works model the hierarchical relationship between material attributes and their deep-level semantics that occurs in the same image. Meanwhile, single image feature is insufficient to achieve high-quality material attribute classification. In this paper, we present methods for generating a new hierarchical material attribute representation mechanism using a new-designed feature mid-fusion algorithm and the state-of-the-art relative attribute (RA) model. The novel feature mid-fusion algorithm can improve the performance of material attribute classification. The deep-level semantics of material attributes are mined by the state-of-the-art RA model. They provide considerable useful and detailed knowledge on material attributes. We call the novel feature mid-fusion algorithm gene selection eXtreme gradient boosting (GS-XGBoost). This algorithm considers the state-of-the-art boosting idea (eXtreme gradient boosting) and the popular multi-feature fusion idea (effective range-based gene selection). To comprehensively describe material attributes, we also measure the relative degree of their deep-level semantics. A new hierarchical material attribute representation mechanism is constructed on the basis of the correctly classified material attributes and their deep-level semantics. The mechanism has two forms. One is binary attribute representation mechanism, and the other is relative attribute representation mechanism. We demonstrate the effectiveness of the proposed GS-XGBoost algorithm on two different datasets. The proposed GS-XGBoost algorithm is not an end-to-end framework but is efficient and practical for fine- and coarse-grained material attribute classification problems that can be applied in different scenarios in large-scale product image retrieval, robotics, and industrial inspection. The novel hierarchical material attribute representation mechanism will help humans or robotics accurately recognize diverse materials and their deep-level semantics. Our research contributes to not only computer science but also material science and engineering.
Distant viewing: analyzing large visual corpora

Abstract In this article we establish a methodological and theoretical framework for the study of large collections of visual materials. Our framework, distant viewing, is distinguished from other approaches by making explicit the interpretive nature of extracting semantic metadata from images. In other words, one must ‘view’ visual materials before studying them. We illustrate the need for the interpretive process of viewing by simultaneously drawing on theories of visual semiotics, photography, and computer vision. Two illustrative applications of the distant viewing framework to our own research are draw upon to explicate the potential and breadth of the approach. A study of television series shows how facial detection is used to compare the role of actors within the narrative arcs across two competing series. An analysis of the Farm Security Administration–Office of War Information corpus of documentary photography is used to establish how photographic style compared and differed amongst those photographers involved with the collection. We then aim to show how our framework engages with current methodological and theoretical conversations occurring within the digital humanities.
Forward and Backward Private Searchable Encryption with SGX

Symmetric Searchable Encryption (SSE) schemes enable users to search over encrypted data hosted on an untrusted server. Recently, there has been a lot of interest in forward and backward private SSE. The notion of forward privacy guarantees that updates to the encrypted structure do not reveal their association to any query made in the past. Backward privacy, on the other hand, guarantees that queries do not reveal their association to deleted documents. But strong backward private schemes are known to be inefficient in terms of both communication and computation. One avenue for improvement is leveraging the power of trusted execution environments such as Intel SGX inside the untrusted server to improve some of these inefficiencies. In this work, we propose the first SGX-supported dynamic SSE constructions that are forward-private as well as backward-private. To the best of our knowledge, while there is some work on SGX-supported Oblivious RAM (ORAM) and static SSE, there is no work on SGX-supported dynamic SSE. We propose three constructions that cover all types of backward privacy in literature that are very efficient compared to the state of the art backward private schemes. Our communication complexity is always the number of current documents matching the query and we show that there is no need for 'total obliviousness' in constructions for the strongest notion of backward privacy.
An Adversarial Risk Analysis Framework for Cybersecurity

Abstract Risk analysis is an essential methodology for cybersecurity as it allows organizations to deal with cyber threats potentially affecting them, prioritize the defense of their assets, and decide what security controls should be implemented. Many risk analysis methods are present in cybersecurity models, compliance frameworks, and international standards. However, most of them employ risk matrices, which suffer shortcomings that may lead to suboptimal resource allocations. We propose a comprehensive framework for cybersecurity risk analysis, covering the presence of both intentional and nonintentional threats and the use of insurance as part of the security portfolio. A simplified case study illustrates the proposed framework, serving as template for more complex problems.
Bridging User Interest to Item Content for Recommender Systems: An Optimization Model

Recommender systems are currently utilized widely in e-commerce for product recommendations and within content delivery platforms. Previous studies usually use independent features to represent item content. As a result, the relationship hidden among the content features is overlooked. In fact, the reason that an item attracts a user may be attributed to only a few set of features. In addition, these features are often semantically coupled. In this paper, we present an optimization model for extracting the relationship hidden in content features by considering user preferences. The learned feature relationship matrix is then applied to address the cold-start recommendations and content-based recommendations. It could also easily be employed for the visualization of feature relation graphs. Our proposed method was examined on three public datasets: 1) hetrec-movielens-2k-v2; 2) book-crossing; and 3) Netflix. The experimental results demonstrated the effectiveness of our method in comparison to the state-of-the-art recommendation methods.
The Feasibility of Injecting Inaudible Voice Commands to Voice Assistants

Voice assistants (VAs) such as Siri and Google Now have become an increasingly popular human-machine interaction method and have made various systems voice controllable. Prior work on attacking voice assistants shows that the hidden voice commands that are incomprehensible to people can control the VAs. Hidden voice commands, though `hidden', are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the voice assistants. We validate DolphinAttack on popular voice assistants, including Siri, Google Now, S Voice, HiVoice, Cortana, Alexa, etc. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to turn on the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice assistants to be resilient to inaudible voice command attacks.
Towards Deep Learning Prospects: Insights for Social Media Analytics

Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. In order to the exponential development and widespread availability of digital social media (SM), analyzing these data using traditional tools and technologies is tough or even intractable. DL is found as an appropriate solution to this problem. In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented summary, following the major efforts made toward the SM analytics (SMA). Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. To this end, we also highlight the DL research challenges (such as scalability, heterogeneity, and multimodality) and future trends.
Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision

Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).
Unified superradiant phase transitions

We prove, by means of a unified treatment, that the superradiant phase transitions of Dicke and classical oscillator limits of simple light-matter models are indeed of the same type. We show that the mean-field approximation is exact in both cases, and compute the structure and location of the transitions in parameter space. We extend this study to a fuller range of models, paying special attention to symmetry considerations. We uncover general features of the phase structure in the space of parameters of these models.
Single Image Haze Removal via Region Detection Network

Haze removal typically works on a physical model to estimate how light is transmitted and lost due to absorption and scattering through the atmosphere. In this paper, a region detection network is proposed to learn the relationship between the hazy image and the medium transmission map in a patchwise manner; the transmission map is then used to remove haze via an atmospheric scattering model and enhance the detail of de-hazed images. To this end, we design a simple yet powerful deep convolutional neural network, which mainly consists of two types of network units and can be trained in an end-to-end manner. One network unit is a module with the residual structure that facilitates the learning process of the deep network. The other is a novel module with a cascaded cross channel pool, which fuses multi-level haze-relevant features and boosts the abstraction ability of the model on a nonlinear manifold. Moreover, an evolutionary-based enhancement method is developed to improve the level of detail of over-smoothed results. Several comparative experiments have been conducted on synthetic and real images, through which we conclude that the proposed method achieves state-of-the-art haze removal results, qualitatively and quantitatively. Supplementary experiments further indicate that our method works better against other adverse effects on vision quality (e.g., the mist formed by heavy rain and the veil met underwater). Moreover, we present a lightweight version of the proposed network, which achieves an impressive haze removal performance even on low-power devices.
Clinical Data in Context

Clinical data augmented with contextual data can help patients with chronic conditions make sense of their disease. However, existing tools do not support interpretation of multiple data streams. To better understand how individuals make sense of clinical and contextual data, we interviewed patients with Type 1 diabetes and their caregivers using context-enhanced visualizations of patients' data as probes to facilitate interpretation activities. We observed that our participants performed four analytical activities when interpreting their data -- finding context-based trends and explaining them, triangulating multiple factors, suggesting context-specific actions, and hypothesizing about alternate contextual factors affecting outcomes. We also observed two challenges encountered during analysis -- the inability to identify clear trends challenged action planning and counterintuitive insights compromised trust in data. Situating our findings within the existing sensemaking frameworks, we demonstrate that sensemaking can not only inform action but can guide the discovery of information needs for exploration. We further argue that sensemaking is a valuable approach for exploring contextual data. Informed by our findings and our reflection on existing sensemaking frameworks, we provide design guidelines for sensemaking tools to improve awareness of contextual factors affecting patients and to support patients' agency in making sense of health data.