Miracle-dz/UniFAR-FTUs huggingface dataset:
A Secure and Privacy-Aware Smart Health System with Secret Key Leakage Resilience

With the development of the smart health (s-health), data security and patient privacy are becoming more and more important. However, some traditional cryptographic schemes can not guarantee data security and patient privacy under various forms of leakage attacks. To prevent the adversary from capturing the part of private keys by leakage attacks, we propose a secure leakage-resilient s-health system which realizes privacy protection and the safe transmission of medical information in the case of leakage attacks. The key technique is a promising public key cryptographic primitive called leakage-resilient anonymous Hierarchical Identity-Based Encryption. Our construction is proved to be secure against chosen plaintext attacks in the standard model under the Diffie-Hellman exponent assumption and decisional linear assumption. We also blind the public parameters and ciphertexts by using double exponent technique to achieve the recipient anonymity. Finally, the performance analysis shows the practicability of our scheme, and the leakage rate of the private key approximates to 1/6.
Connecting Language and Vision to Actions

A long-term goal of AI research is to build intelligent agents that can see the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and act in a physical or embodied environment. To this end, recent advances at the intersection of language and vision have made incredible progress – from being able to generate natural language descriptions of images/videos, to answering questions about them, to even holding free-form conversations about visual content! However, while these agents can passively describe images or answer (a sequence of) questions about them, they cannot act in the world (what if I cannot answer a question from my current view, or I am asked to move or manipulate something? ). Thus, the challenge now is to extend this progress in language and vision to embodied agents that take actions and actively interact with their visual environments. To reduce the entry barrier for new researchers, this tutorial will provide an overview of the growing number of multimodal tasks and datasets that combine textual and visual understanding. We will comprehensively review existing state-of-the-art approaches to selected tasks such as image captioning, visual question answering (VQA) and visual dialog, presenting the key architectural building blocks (such as co-attention) and novel algorithms (such as cooperative/adversarial games) used to train models for these tasks. We will then discuss some of the current and upcoming challenges of combining language, vision and actions, and introduce some recently-released interactive 3D simulation environments designed for this purpose.
Binary Compressive Sensing via Smoothed $\ell_0$ Gradient Descent.

We present a Compressive Sensing algorithm for reconstructing binary signals from its linear measurements. The proposed algorithm minimizes a non-convex cost function expressed as a weighted sum of smoothed $\ell_0$ norms which takes into account the binariness of signals. We show that for binary signals the proposed algorithm outperforms other existing algorithms in recovery rate while requiring a short run time.
A Differential Privacy Mechanism with Network Effects for Crowdsourcing Systems

In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore the network effect and provide a suitable differential privacy guarantee, we design PINE (Privacy Incentivization with Network Effects). PINE is a mechanism that maximizes the initiator's payoff while providing participating users with privacy protections.
Uniqueness in the Inverse Boundary Value Problem for Piecewise Homogeneous Anisotropic Elasticity

Consider a three dimensional piecewise homogeneous anisotropic elastic medium $\Omega$ which is a bounded domain consisting of a finite number of bounded subdomains $D_\alpha$, with each $D_\alpha$ a homogeneous elastic medium. One typical example is a finite element model with elements with curvilinear interfaces for an anisotropic elastic medium. Assuming the $D_\alpha$ are known and Lipschitz, we are concerned with the uniqueness in the inverse boundary value problem of identifying the anisotropic elasticity tensor on $\Omega$ from a localized Dirichlet-to-Neumann map given on a part of the boundary $\partial D_{\alpha_0}\cap\partial\Omega$ of $\partial\Omega$ for a single $\alpha_0$, where $\partial D_{\alpha_0}$ denotes the boundary of $ D_{\alpha_0}$. If we can connect each $D_\alpha$ to $D_{\alpha_0}$ by a chain of $\{D_{\alpha_i}\}_{i=1}^n$ such that interfaces between adjacent regions contain a curved portion, we obtain global uniqueness for this inverse boundary value problem. If the $D_\alpha$ .
Robust Blending and Occlusion Compensation in Dynamic Multi-Projection Mapping

Using multi-projection systems allows us to immerse users in an altered reality without the need to wear additional head-gear. The immersion of such systems relies on the quality of the calibration which in general will degenerate over time when used outside of a lab environment. This work introduces a novel balance term that allows us to hide high frequency brightness seams caused by self-shadowing of the projected geometry and the borders of the projection frustum. We further use this more robust blending between projectors to compensate for occluding spectators, who enter the projection volume, by ﬁlling the resulting shadows with light from other projectors.
A measurement study of the subresource integrity mechanism on real-world applications

Today, billions of websites are available to users in just a click to give them required and appropriate service. Most of these websites provide rich functionalities by relying on third-party-hosted resources. Subresource integrity (SRI) is a mechanism that provides ways to examine the integrity of third-party-hosted resources. This paper provides detailed statistics on websites that are using SRI. This research also addresses different aspects of SRI implementation, such as inconsistency in the adoption of SRI and failover management, and it also estimates the amount of effort required to adopt SRI. This research first identifies different issues of SRI implementation and then provides a way to mitigate these issues and make adoption of SRI easier and error-free. We implemented a tool called UserSRI as a browser extension. UserSRI uses dynamic analysis to infer the SRI mechanism, facilitates testing, and gives savvy users the authority to enforce client-side policies on websites.
Two-Stage Adaptive Bloom Filters for Per-Flow Monitoring in Software Defined Networks

Per-flow monitoring attracts great attention for its importance in networks. Thus a two-stage Bloom Filter is proposed for it. However, without considering the scalability of the two-stage Bloom Filter to different probability distributions of target flows' monitoring action types with high probability deviations, it leads to some serious problems, such as high false positive rate or low resource utilization. Therefore we propose the two-stage adaptive Bloom Filters that are operated collaboratively in network-wide range in software defined networks. The Bloom Filter supports dynamic mappings from the pre-undetermined action types of its constituent Bloom Filters to specific ones and adjustable number of the constituent Bloom Filters. This proposal has two major advantages: 1) false positive rate can be kept under a threshold in the different probability distributions; 2) resource utilization and rejection probability can be significantly improved at low cost of increasing the threshold. We analyze and discuss the two-stage adaptive Bloom Filters from false positive rate, resource utilization and rejection probability. The results from our simulation based on real-life network topology agree with our analysis and discussion.
Green Fog Computing Resource Allocation Using Joint Benders Decomposition, Dinkelbach Algorithm, and Modified Distributed Inner Convex Approximation

Fog computing is a promising approach to alleviate the computation burden in traditional mobile networks to meet the increasing application demands. Such a complicated system is typically challenging and requires distributed solutions. In this paper, we investigate the resource allocation problem in fog computing to maximize the utility function from the energy efficiency perspective. The formulated problem is a mix integer nonlinear programming problem, which is NP-hard. We adopt a modified distributed inner convex approximation (NOVA) to approximate the problem first. Then, the Benders decomposition algorithm is applied to deal with integer variables. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent parametric subtractive form. Furthermore, the subproblem is decomposed distributedly, which enables users to update without information exchange. The simulation results indicate the effectiveness of the proposed algorithm.
Affordable Modular Mobile Manipulator for Domestic Applications

Although there are some commercially available mobile manipulators, they are often very expensive, and can be used only in specific applications. A key contribution of this work is the development of a modular, reconfigurable, robust and low-cost autonomous mobile manipulator that can be used for a wide range of applications, including domestic applications in unknown and cluttered environment. This mobile manipulator is developed by a synergetic integration of vision, manipulation and navigation subsystems that can be independently used for various applications. The applicability of the developed mobile manipulator for carrying out a number of domestic tasks in various environments has been demonstrated.
Altered connectivity between striatal and extrastriatal regions in patients with schizophrenia on maintenance antipsychotics: an [18F]fallypride PET and functional MRI study

In addition to probing regional differences in receptor availability, molecular positron emission tomography (PET) is proving useful for investigating perturbations in neurotransmitter networks using interregional correlation analyses. In a multi‐modal imaging study, we examined interregional correlations of dopamine D2/3 receptor availability between striatal and extrastriatal regions using [18F]fallypride high‐resolution PET in 11 patients with schizophrenia receiving low‐dose maintenance atypical antipsychotics and 14 healthy control subjects, and investigated resting‐state functional connectivity in the same subjects using seed‐based functional magnetic resonance imaging (fMRI) analysis. In the healthy control group, there were no significant correlations of [18F]fallypride binding potential (BPND) between striatal regions and any cortical areas, whereas the patient group showed significant and widespread inter‐correlations. Correlations between BPND in striatum, amygdala and insula with cortex were significantly higher in patients than in controls. In seed‐based resting‐state fMRI analysis, the healthy controls revealed positive and negative functional connectivity patterns, while patients exhibited a pattern of exclusively positive connectivity. Functional connectivity was significantly higher between striatal regions and extrastriatal areas including cortical regions in patients compared to controls. In this first such report, molecular and functional connectivity between striatal and extrastriatal regions was primarily characterized by increased interregional relationships in treated patients with schizophrenia. The results suggest that the spatial organization of D2/3 receptor availability and related functional connectivity are significantly perturbed in stable outpatients on maintenance antipsychotics. Future studies should include antipsychotic‐naïve patients to determine if these relationships are illness‐related characteristics, or arising due to chronic antipsychotic treatment.
International Conference on Identification , Information and Knowledge in the Internet of Things A Method for Extracting Section Information of Highway Based on Massive GPS Data

Information of highway is the basic element of intelligent transportation system. This study proposes a method for extracting section information of highway based on massive GPS data. The method has the advantages of low time complexity and high accuracy. The method is inspired by the algorithm of Density Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN can discover cluster of arbitrary shape. The method calculated neighborhood density of GPS points by algorithm of zone division; therefore this method reduces the computation scale and the time complexity greatly. Taking into account the uneven distribution of GPS data between sections, the method of hierarchical division is adopted in clustering, and iterative clustering is computed from dense to sparse. Finally we find that our method can find the road information efficiently on massive GPS data. Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2017 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI2017).
Energy-Efficient Virtual Network Function Placement in Telecom Networks

Network Function Virtualization (NFV) is an emerging network architecture, which decouples the software implementation of network functions from the underlying hardware. Telecom operators widely place diverse types of Virtual Network Functions (VNFs) on specified software middlebox. Traffic needs to go through a set of ordered VNFs which forms a Service Function Chain (SFC). However, how to efficiently place VNFs at various network locations while minimizing energy consumption is still an open problem. To this end, we study joint optimization of VNF placement and traffic routing for energy efficiency in telecom networks. We first present the energy model in NFV-enabled telecom networks, and then formulate the studied problem as an Integer Linear Programming (ILP) model. Since the problem is NP-hard, we design a polynomial algorithm using the Markov approximation technique to find the near-optimal result. Extensive simulation results show that our algorithm saves up to 14.84% energy consumption in telecom networks compared with previous VNF placement algorithms.
Parameterized Joint Reconstruction of the Initial Pressure and Sound Speed Distributions for Photoacoustic Computed Tomography

Accurate estimation of the initial pressure distribution in photoacoustic computed tomography (PACT) depends on knowledge of the sound speed distribution. However, the sound speed distribution is typically unknown. Further, the initial pressure and sound speed distributions cannot both, in general, be stably recovered from PACT measurements alone. In this work, a joint reconstruction (JR) method for the initial pressure distribution and a low-dimensional parameterized model of the sound speed distribution is proposed. By employing a priori information about the structure of the sound speed distribution, both the initial pressure and sound speed can be accurately recovered. The JR problem is solved by use of a proximal optimization method that allows constraints and non-smooth regularization functions for the initial pressure distribution. The gradients of the cost function with respect to the initial pressure and sound speed distributions are calculated by use of an adjoint state method that has the same per-iteration computational cost as calculating the gradient with respect to the initial pressure distribution alone. This approach is evaluated through 2D computer-simulation studies for a small animal imaging model and by application to experimental in vivo measurements of a mouse.
Who Spread to Whom? Inferring Online Social Networks with User Features

Network inference has been extensively studied to better understand the information diffusion in online social networks. In this field, state-of- art widely adopted a priori knowledge related to users' infection timestamps. Researchers also assume that the smaller the time difference between two nodes, the higher the likelihood of an edge between the pair of users. However, according to our technical analyses and empirical studies, existing methods have two critical problems 1) alternative spreading paths 2) users' delivery delay, which leads to the inaccuracy of previous methods. In this paper, we developed an innovative method to address the inference inaccuracy caused by the exposed two problems. This method determined the existence of an edge between a pair of users according to part of the users' features. The experiment results suggested that our method achieved around 70% accuracy in inferring network structures while existing methods failed in the same tasks.
A Self-Reconfiguring Cache Architecture to Improve Control Quality in Cyber-Physical Systems

Quality of control is a critical concern in Cyber-Physical Systems (CPS) which are comprised of multiple intercommunicating control applications. Due to complex timing behaviour of these systems, poor quality of control can lead to catastrophe. Recent studies showed that, conflict miss increment in the processor cache memory shared by concurrently running control applications can degrade control quality in CPS significantly. Increasing cache associativity can help to reduce conflict misses. However, the existing reconfigurable cache architectures that allow runtime modification of cache associativity are not capable to guaranty a newly chosen associativity's suitability for the forthcoming control quality requirement. Moreover, they have timing and energy related overheads. In this regard, this paper presents a novel, self-reconfiguring cache memory architecture "SeReMo". When conflict misses increase significantly, SeReMo reconfigures its associativity to better suit the current as well as future control quality demand. To trigger reconfiguration, a low overhead, non-strictly inclusive cache hierarchy-specific approach is used. Configurations with different associativity are generated using modules made of 4 cache lines and 7 special bits. Special replacement policy and indexing scheme are used to suit modular reconfiguration. SPEC CPU 2006 benchmark trace-driven simulation reveals that SeReMo reduces average number of conflict misses per line to 1/12951 of the state-of-the-art reconfigurable cache architecture at maximum (to 1/830 on average). As a result, execution time and energy consumption reduce by 48 hours at maximum (by 2/3 on average) and by 2907 Joules at maximum (86% on average) respectively.
High-correlated residuals improved estimation in the high-dimensional SUR model

ABSTRACT This article focuses on estimating regression coefficients in the high-dimensional seemingly unrelated regression model. When the number of equations exceeds that of the observations, both the maximum likelihood estimator and Zellner’s two-stage estimator do not exist. As an alternative, we propose a two-stage conditional expectation improved estimator. The new estimator is further improved by the high-correlated residuals, and the high correlation is determined by hypothesis testings. Simulations show that the new estimator outperforms the ordinary least-squares estimator in terms of mean square errors, especially when high-correlated residuals exist between the equations.
A home security system for seniors based on the beacon technology

Many elderly people have a hard time adapting to information technology (IT); for instance, many wearable devices have such rich functions that render them highly power‐consuming, creating operational difficulties for the elderly. The wearable devices must constantly be recharged, which is also an issue for disabled elderly people. Hence, this study utilizes transmitter and receiver of the beacon to learn about the habits of elderly people in their residence while monitoring their heart rate to track their physical condition; when and if they press the medical alarm, help can be sent on their way. This study uses the beacon device to position the location of elderly people in their residence. We have developed a wearable wristband that transmits data through the beacon. The only button on the wristband is a medical alarm; other functions include home positioning and heart rate monitoring, which operate automatically to relieve the elderly people from information technology adaptation issues. This paper will also develop an integrative monitoring platform that aims at detecting whether an elderly person has pressed the medical alarm as well as analyzing whether their heart rate is regular. Given that elderly people stay mostly within the range of their residence, the platform will conduct statistical analysis on schedule of the elderly person of being in and out of the house; if they have left their house for too long or have displayed irregular behavior in leaving or entering the house, then their family will be notified. The proposed method has added privacy and access authority control to prevent malicious parties from tracking the schedule or the residence of elderly people from being invaded. This study has also completed application development and conducted privacy efficacy analysis.
Improving Collaboration in Augmented Video Conference using Mutually Shared Gaze

To improve remote collaboration in video conferencing systems, researchers have been investigating augmenting visual cues onto a shared live video stream. In such systems, a person wearing a head-mounted display (HMD) and camera can share her view of the surrounding real-world with a remote collaborator to receive assistance on a real-world task. While this concept of augmented video conferencing (AVC) has been actively investigated, there has been little research on how sharing gaze cues might affect the collaboration in video conferencing. This paper investigates how sharing gaze in both directions between a local worker and remote helper in an AVC system affects the collaboration and communication. Using a prototype AVC system that shares the eye gaze of both users, we conducted a user study that compares four conditions with different combinations of eye gaze sharing between the two users. The results showed that sharing each other's gaze significantly improved collaboration and communication.