Thursday, 24 September 2020

02:03 AM

Pocket Diagnosis: Secure Federated Learning against Poisoning Attack in the Cloud. (arXiv:2009.10918v1 [cs.CR]) [cs.CR updates on arXiv.org]

Federated learning has become prevalent in medical diagnosis due to its effectiveness in training a federated model among multiple health institutions (i.e. Data Islands (DIs)). However, increasingly massive DI-level poisoning attacks have shed light on a vulnerability in federated learning, which inject poisoned data into certain DIs to corrupt the availability of the federated model. Previous works on federated learning have been inadequate in ensuring the privacy of DIs and the availability of the final federated model. In this paper, we design a secure federated learning mechanism with multiple keys to prevent DI-level poisoning attacks for medical diagnosis, called SFPA. Concretely, SFPA provides privacy-preserving random forest-based federated learning by using the multi-key secure computation, which guarantees the confidentiality of DI-related information. Meanwhile, a secure defense strategy over encrypted locally-submitted models is proposed to defense DI-level poisoning attacks. Finally, our formal security analysis and empirical tests on a public cloud platform demonstrate the security and efficiency of SFPA as well as its capability of resisting DI-level poisoning attacks.

Fundamental Limits of Byzantine Agreement. (arXiv:2009.10965v1 [cs.IT]) [cs.CR updates on arXiv.org]

Byzantine agreement (BA) is a distributed consensus problem where $n$ processors want to reach agreement on an $\ell$-bit message or value, but up to $t$ of the processors are dishonest or faulty. The challenge of this BA problem lies in achieving agreement despite the presence of dishonest processors who may arbitrarily deviate from the designed protocol. The quality of a BA protocol is measured primarily by using the following three parameters: the number of processors $n$ as a function of $t$ allowed (resilience); the number of rounds (round complexity); and the total number of communication bits (communication complexity). For any error-free BA protocol, the known lower bounds on those three parameters are $3t + 1$, $t + 1$ and $\Omega(\max\{n\ell, nt\})$, respectively, where a protocol that is guaranteed to be correct in all executions is said to be error free.

In this work, by using coding theory we design a coded BA protocol (termed as COOL) that achieves consensus on an $\ell$-bit message with optimal resilience, asymptotically optimal round complexity, and asymptotically optimal communication complexity when $\ell \geq t\log n$, simultaneously. The proposed COOL is an error-free and deterministic BA protocol that does not rely on cryptographic technique such as signatures, hashing, authentication and secret sharing (signature free). It is secure against computationally unbounded adversary who takes full control over the dishonest processors (information-theoretic secure). We show that our results can also be extended to the setting of Byzantine broadcast, aka Byzantine generals problem, where the honest processors want to agree on the message sent by a leader who is potentially dishonest. This work reveals that coding is an effective approach for achieving the fundamental limits of Byzantine agreement and its variants.

Reliable, Fair and Decentralized Marketplace for Content Sharing Using Blockchain. (arXiv:2009.11033v1 [cs.CR]) [cs.CR updates on arXiv.org]

Content sharing platforms such as Youtube and Vimeo have promoted pay per view models for artists to monetize their content. Yet, artists remain at the mercy of centralized platforms that control content listing and advertisement, with little transparency and fairness in terms of number of views or revenue. On the other hand, consumers are distanced from the publishers and cannot authenticate originality of the content. In this paper, we develop a reliable and fair platform for content sharing without a central facilitator. The platform is built as a decentralized data storage layer to store and share content in a fault-tolerant manner, where the peers also participate in a blockchain network. The blockchain is used to manage content listings and as an auditable and fair marketplace transaction processor that automatically pays out the content creators and the storage facilitators using smart contracts. We demonstrate the system with the blockchain layer built on Hyperledger Fabric and the data layer built on Tahoe-LAFS,and show that our design is practical and scalable with low overheads.

PAC learning with stable and private predictions. (arXiv:1911.10541v2 [cs.LG] UPDATED) [cs.CR updates on arXiv.org]

We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of uniform stability (Bousquet and Elisseeff, 2001) and prediction privacy (Dwork and Feldman, 2018). Previous work on these notions shows how they can be achieved in the standard PAC model via simple aggregation of models trained on disjoint subsets of data. Unfortunately, this approach leads to a significant overhead in terms of sample complexity. Here we demonstrate several general approaches to stable and private prediction that either eliminate or significantly reduce the overhead. Specifically, we demonstrate that for any class $C$ of VC dimension $d$ there exists a $\gamma$-uniformly stable algorithm for learning $C$ with excess error $\alpha$ using $\tilde O(d/(\alpha\gamma) + d/\alpha^2)$ samples. We also show that this bound is nearly tight. For $\epsilon$-differentially private prediction we give two new algorithms: one using $\tilde O(d/(\alpha^2\epsilon))$ samples and another one using $\tilde O(d^2/(\alpha\epsilon) + d/\alpha^2)$ samples. The best previously known bounds for these problems are $O(d/(\alpha^2\gamma))$ and $O(d/(\alpha^3\epsilon))$, respectively.

The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation. (arXiv:2009.08000v2 [cs.DS] UPDATED) [cs.CR updates on arXiv.org]

There has been a recent wave of interest in intermediate trust models for differential privacy that eliminate the need for a fully trusted central data collector, but overcome the limitations of local differential privacy. This interest has led to the introduction of the shuffle model (Cheu et al., EUROCRYPT 2019; Erlingsson et al., SODA 2019) and revisiting the pan-private model (Dwork et al., ITCS 2010). The message of this line of work is that, for a variety of low-dimensional problems---such as counts, means, and histograms---these intermediate models offer nearly as much power as central differential privacy. However, there has been considerably less success using these models for high-dimensional learning and estimation problems.

In this work, we show that, for a variety of high-dimensional learning and estimation problems, both the shuffle model and the pan-private model inherently incur an exponential price in sample complexity relative to the central model. For example, we show that, private agnostic learning of parity functions over $d$ bits requires $\Omega(2^{d/2})$ samples in these models, and privately selecting the most common attribute from a set of $d$ choices requires $\Omega(d^{1/2})$ samples, both of which are exponential separations from the central model. Our work gives the first non-trivial lower bounds for these problems for both the pan-private model and the general multi-message shuffle model.

An Accidental Nutritionist [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 1-23, September 2020.

Nutritional Requirements for Sustaining Health and Performance During Exposure to Extreme Environments [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 221-245, September 2020.

Short Bowel Syndrome: A Paradigm for Intestinal Adaptation to Nutrition? [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 299-321, September 2020.

Drinking Water in the United States: Implications of Water Safety, Access, and Consumption [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 40, Issue 1, Page 345-373, September 2020.

Your neighborhood may raise your risk of chronic kidney disease [EurekAlert! - Breaking News]

A neighborhood's overall socioeconomic status, including income and education-level, may influence its residents' risk of chronic kidney disease, according to a study recently published in SSM Population Health by researchers from Drexel University's Dornsife School of Public Health.

Berry good news -- new compound from blueberries could treat inflammatory disorders [EurekAlert! - Breaking News]

Inflammatory bowel disease (IBD), caused by chronic inflammation in the digestive tract linings, can be debilitating and life threatening. Therapeutic options include suppression of immune response, but treatments leading to complete cure of IBD are still not available. Recently, a team of researchers of Tokyo University of Science has discovered a polyphenolic compound derived from blueberry that shows remarkable immunosuppressive effects and can be useful in treating IBD.

Wednesday, 23 September 2020

06:02 PM

Fructose made in the brain could be a mechanism driving Alzheimer's disease [EurekAlert! - Breaking News]

New research released from the University of Colorado Anschutz Medical Campus proposes that Alzheimer's disease may be driven by the overactivation of fructose made in the brain.The study outlined the hypothesis that Alzheimer's disease is driven largely by Western culture that has resulted in excessive fructose metabolism in the brain.

10:02 AM

Tuesday, 22 September 2020

06:02 PM

Daily briefing: Toxic bacteria caused the mysterious deaths of hundreds of African elephants [Nature - Issue - nature.com science feeds]

Nature, Published online: 21 September 2020; doi:10.1038/d41586-020-02703-9

Cyanobacteria-infected water killed at least 330 elephants in Botswana, and many questions remain. Plus: what the Israel–Arab peace accord means for scientific collaboration, and lessons from three centuries of vaccine opposition.

Pandemic’s Mental Health Toll Grows [JAMA Current Issue]

Considerable increases in mental health disorders since coronavirus disease 2019 (COVID-19) emerged demonstrate the need for specific screening tools to detect pandemic-related trauma and stress, according to a recent study.

Dermatology and COVID-19 [JAMA Current Issue]

In this Viewpoint, JAMA Dermatology editors review the skin findings seen in association with coronavirus disease 2019 (COVID-19), how best to respond to those manifestations, and ways the pandemic has affected the practice of dermatology, including reassignment of specialists to COVID-19 care and the transition to teledermatology.

Psychiatry and COVID-19 [JAMA Current Issue]

In this Viewpoint, JAMA Network’s psychiatry editors review how the coronavirus disease 2019 (COVID-19) pandemic has affected the practice of psychiatry in its first 6 months, for example, through disruptions of care provided in group settings, provision of telehealth, and widespread anxiety and health care worker burnout and depression.

Patient Information: High Blood Pressure [JAMA Current Issue]

This JAMA Patient Page explains what high blood pressure is, why it is important to control, the difference between the top (systolic) and bottom (diastolic) numbers, and how it is best treated and prevented.

Buprenorphine Implants, Injections Are Underused [JAMA Current Issue]

Injectable and implantable forms of buprenorphine to treat patients with opioid use disorder (OUD) are less likely to be diverted than oral formulations but they’re rarely prescribed, according to a recent US Government Accountability Office (GAO) report.

MTU engineers build three new open-source tools for COVID-19 [EurekAlert! - Breaking News]

A 3D printer that can take the heat, breathing tech to keep firefighters safe and a ventilator design printed for less than $170. Large groups of makers, engineers, and medical professionals collaborate to make open-source solutions that can be reproduced and assembled locally worldwide.

COVID-19 mortality rates higher among men than women [EurekAlert! - Breaking News]

A new review article from Beth Israel Deaconess Medical Center (BIDMC) shows people who are biologically male are dying from COVID-19 at a higher rate than people who are biologically female.

02:02 AM

Correction for Xiang et al., Using synthetic biology to overcome barriers to stable expression of nitrogenase in eukaryotic organelles [Corrections] [Early Edition]

MICROBIOLOGY Correction for “Using synthetic biology to overcome barriers to stable expression of nitrogenase in eukaryotic organelles,” by Nan Xiang, Chenyue Guo, Jiwei Liu, Hao Xu, Ray Dixon, Jianguo Yang, and Yi-Ping Wang, which was first published June 29, 2020; 10.1073/pnas.2002307117 (Proc. Natl. Acad. Sci. U.S.A. 117, 16537–16545). The authors...

Cooperative CC–CV Charging of Supercapacitors Using Multicharger Systems [IEEE Transactions on Industrial Electronics - new TOC]

The constant-current constant-voltage (CC-CV) charging method is a common approach to charge batteries with a single charger. Recently, with the emergence of high power-density batteries, e.g., supercapacitors, several chargers are typically connected in parallel to provide the required charging power in many applications. However, the classical decentralized control scheme for the multicharger system leads to the current imbalance, which deteriorates the reliability of the charging system. In this article, we extend the CC-CV charging protocol to multicharger systems using a cooperative control method, which can alleviate the current imbalance among chargers effectively. First, the motivation and charging system modeling of this work are provided. Then, a cooperative CC charging protocol and a cooperative CV charging protocol are presented, respectively, and a switching logic is designed to achieve the switching between them based on the supercapacitor voltage. The closed-loop model of the proposed charging system is developed using the block diagram. A laboratory testbed is built to verify the effectiveness of the proposed method. Experiment results show that the proposed method provides a better current balancing and voltage regulation performance when compared with existing decentralized control methods.

Active Suspension System Control With Decentralized Event-Triggered Scheme [IEEE Transactions on Industrial Electronics - new TOC]

This article concerns the problem of improving ride comfort of a full-vehicle active suspension system through an observer-based decentralized event-triggered H controller. The observer-based controller is adopted to cope with the situation that states information of vehicles are not available. To reduce the observer-based controller computing frequency, a decentralized event-triggered data transmission scheme is provided, and lower bounds on the minimum interevent time are guaranteed. Based on the proposed controller, the H performance of an active full-vehicle suspension system can be obtained. Finally, simulations in the bump and random road conditions are shown to demonstrate the effectiveness of the derived algorithm.

An Efficient Peer-to-Peer Energy-Sharing Framework for Numerous Community Prosumers [IEEE Transactions on Industrial Informatics - new TOC]

This article presents an efficient peer-to-peer energy-sharing framework for numerous community prosumers to reduce energy costs and to promote renewable energy utilization. Specifically, for day-ahead and real-time energy management of prosumers, an intercommunity energy-sharing strategy and an intracommunity energy-sharing strategy are proposed, respectively. In the former strategy, prosumers can share energy with any community peers, and community aggregators represent their own prosumers to coordinate energy sharing. A two-phase model is designed. In the first phase, the optimal energy-sharing profiles of prosumers are derived to minimize the global energy costs, and in the second phase, equilibrium-based energy-sharing prices are induced considering the individual interests of prosumers. In the latter strategy, prosumers share energy only with its community peers for time saving to handle real-time uncertainties collaboratively to reduce real-time costs. The framework efficiency is verified by the simulation cases on a typical distribution network.

Intermediate Observer-Based Robust Distributed Fault Estimation for Nonlinear Multiagent Systems With Directed Graphs [IEEE Transactions on Industrial Informatics - new TOC]

This article focuses on the problem of robust distributed fault estimation for nonlinear multiagent systems with actuator faults and sensor faults. The communication topology of the multiagent systems is assumed to be directed. A novel intermediate observer design method is proposed to estimate the system states, actuator faults, and sensor faults. For the observer constructed in one agent, the output estimation errors of itself and its neighbors are considered, simultaneously. The observer matching condition is not needed in the observer design process. Based on Schur decomposition, the observer parameter calculation method is presented in terms of solution to one linear matrix inequality, which is with the same order as it is for the single agent system. Thus, the calculated amount remains unchanged even when the number of agents increases, since the inequality dimension is independent of the agent number. At last, simulation results are provided to illustrate the effectiveness of the proposed technique.

Maritime Search and Rescue Based on Group Mobile Computing for Unmanned Aerial Vehicles and Unmanned Surface Vehicles [IEEE Transactions on Industrial Informatics - new TOC]

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.

Hierarchical Two-Stream Growing Self-Organizing Maps With Transience for Human Activity Recognition [IEEE Transactions on Industrial Informatics - new TOC]

The rapid growth in autonomous industrial environments has increased the need for intelligent video surveillance. As a predominant element of video surveillance, recognition of complex human movements is important in a wide range of surveillance applications. However, the current state-of-the-art video surveillance techniques use supervised deep learning pipelines for human activity recognition (HAR). A key shortcoming of such techniques is the inability to learn from unlabeled video streams. To operate effectively in natural environments, video surveillance techniques have to be able to handle huge volumes of unlabeled video data, monitor and generate alerts and insights derived from multiple characteristics such as spatial structure, motion flow, color distribution, etc. Furthermore, most conventional learning systems lack memory persistence capability which can reduce the influence of outdated information in memory-guided decision-making resulting in limiting plasticity and overfitting based on specific past events. In this article, we propose a new adaptation of the Growing Self-Organizing Map (GSOM) to address these shortcomings by 1) adopting two proven concepts of traditional deep learning, hierarchical, and multistream learning, applied into GSOM self-structuring architecture to accommodate learning from unlabeled video data and their diverse characteristics, 2) address overfitting and the influence of outdated information on neural architecture by implementing a transience property in the algorithm. We demonstrate the proposed model using three benchmark video datasets and the results confirm its validity and usability for HAR.

Friday, 18 September 2020

02:03 AM

Reply to Ng et al.: Not all trauma is the same, but lessons can be drawn from commonalities [Letters (Online Only)] [Early Edition]

In their Letter, Ng et al. (1) state that our (2) comparison of the coronavirus disease 2019 (COVID-19) to Hurricane Katrina is “slightly contrived.” We appreciate the opportunity for discussion. However, we maintain that there is much to be learned from prior disasters, including Hurricane Katrina, for anticipating and mitigating...

Editorial Expression of Concern: Living annulative π-extension polymerization for graphene nanoribbon synthesis [Nature - Issue - nature.com science feeds]

Nature, Published online: 18 September 2020; doi:10.1038/s41586-020-2756-0

Editorial Expression of Concern: Living annulative π-extension polymerization for graphene nanoribbon synthesis

[Comment] Access to health care and the 2020 US election [The Lancet]

Much is at stake for the health of the US population in the upcoming 2020 elections. Access to health care is an important driver of health and is—pending this election outcome—on the line. While the overall impacts of the Trump presidency on the health of Americans is likely to take years to be fully felt,1 the COVID-19 pandemic has surfaced many key health challenges that the USA faces. The USA is less healthy than its peer high-income nations, with lower life expectancy and higher morbidity and mortality rates from a broad range of diseases.

[Comment] Offline: Remembering the scientists [The Lancet]

One of the most frightening aspects of reading Daniel Defoe's A Journal of the Plague Year (1722), his imaginative account of the 1665 Great Plague, is his depiction of the total terror that enveloped London. No one, physicians included, had the slightest clue about the cause of “the Distemper”. The best that could be done was to paint a red cross on the doors of those afflicted, padlock them shut, and place watchmen outside to ensure nobody escaped. As bodies accumulated, giant pits were filled with infected corpses.

[Obituary] Peter Nicholas Kazembe [The Lancet]

Influential paediatrician in Malawi. He was born in Nkhotakota, Malawi, on Aug 28, 1954, and died of cholangiocarcinoma in Johannesburg, South Africa, on Aug 11, 2020, aged 65 years.

[Department of Error] Department of Error [The Lancet]

The Lancet. COVID-19: a new lens for non-communicable diseases. Lancet 2020; 396: 649—In the first paragraph of this Editorial, the incorrect year was given for the UN General Assembly meeting on HIV/AIDS; it should have been 2001. This correction has been made to the online version as of Sept 8, 2020.

[Correspondence] Brazil's COVID-19 response [The Lancet]

We read with interest the Editorial1 about Brazil's response to COVID-19. As Brazilian scientists, we would like to express major concerns about the multiple crises that our country is facing.

Wednesday, 16 September 2020

06:03 PM

Metabolic trait diversity shapes marine biogeography [Nature - Issue - nature.com science feeds]

Nature, Published online: 16 September 2020; doi:10.1038/s41586-020-2721-y

A tight coupling between metabolic rate, efficacy of oxygen supply and the temperature sensitivities of marine animals predicts a variety of geographical niches that better aligns with the distributions of species than models of either temperature or oxygen alone.

Daily briefing: How clinical trials can bounce back from COVID-19 disruption [Nature - Issue - nature.com science feeds]

Nature, Published online: 15 September 2020; doi:10.1038/d41586-020-02654-1

Coronavirus-vaccine efforts have shown us how clinical trials can be bolder. Plus: the mathematics of impossibility and red flags in Russian vaccine-trial results.

Tuesday, 15 September 2020

02:02 AM

Late lactation in small mammals is a critically sensitive window of vulnerability to elevated ambient temperature [Environmental Sciences] [Early Edition]

Predicted increases in global average temperature are physiologically trivial for most endotherms. However, heat waves will also increase in both frequency and severity, and these will be physiologically more important. Lactating small mammals are hypothesized to be limited by heat dissipation capacity, suggesting high temperatures may adversely impact lactation performance....

CANTO - Covert AutheNtication With Timing Channels Over Optimized Traffic Flows for CAN [IEEE Transactions on Information Forensics and Security - new TOC]

Previous research works have endorsed the use of delays and clock skews for detecting intrusions or fingerprinting controllers that communicate on the CAN bus. Recently, timing characteristics of CAN frames have been also used for establishing a covert channel for cryptographic authentication, in this way cleverly removing the need for cryptographic material inside the short payload of data frames. However, the main drawback of this approach is the limited security level that can be achieved over existing CAN bus traffic. In this work we significantly improve on this by relying on optimization algorithms for scheduling CAN frames and deploy the covert channel on optimized CAN traffic. Under practical bus allocations, we are able to extract 3–5 bits of authentication data from each frame which leads to an efficient intrusion detection and authentication mechanism. By accumulating covert channel data over several consecutive frames, we can achieve higher security levels that are in line with current real-world demands. To prove the correctness of our approach, we present experiments on automotive-grade controllers, i.e., Infineon Aurix, and bus measurements with the use of industry standard tools, i.e., CANoe.

Friday, 11 September 2020

02:02 AM

Multiheme hydroxylamine oxidoreductases produce NO during ammonia oxidation in methanotrophs [Microbiology] [Early Edition]

Aerobic and nitrite-dependent methanotrophs make a living from oxidizing methane via methanol to carbon dioxide. In addition, these microorganisms cometabolize ammonia due to its structural similarities to methane. The first step in both of these processes is catalyzed by methane monooxygenase, which converts methane or ammonia into methanol or hydroxylamine,...

Thursday, 10 September 2020

Wednesday, 09 September 2020

Tuesday, 08 September 2020

02:03 AM

PolyShard: Coded Sharding Achieves Linearly Scaling Efficiency and Security Simultaneously [IEEE Transactions on Information Forensics and Security - new TOC]

Today's blockchain designs suffer from a trilemma claiming that no blockchain system can simultaneously achieve decentralization, security, and performance scalability. For current blockchain systems, as more nodes join the network, the efficiency of the system (computation, communication, and storage) stays constant at best. A leading idea for enabling blockchains to scale efficiency is the notion of sharding: different subsets of nodes handle different portions of the blockchain, thereby reducing the load for each individual node. However, existing sharding proposals achieve efficiency scaling by compromising on trust - corrupting the nodes in a given shard will lead to the permanent loss of the corresponding portion of data. In this paper, we settle the trilemma by demonstrating a new protocol for coded storage and computation in blockchains. In particular, we propose PolyShard: “polynomially coded sharding” scheme that achieves information-theoretic upper bounds on the efficiency of the storage, system throughput, as well as on trust, thus enabling a truly scalable system. We provide simulation results that numerically demonstrate the performance improvement over state of the arts, and the scalability of the PolyShard system. Finally, we discuss potential enhancements, and highlight practical considerations in building such a system.

Friday, 04 September 2020

02:03 AM

<italic>TransPCFG</italic>: Transferring the Grammars From Short Passwords to Guess Long Passwords Effectively [IEEE Transactions on Information Forensics and Security - new TOC]

Long passwords are gaining popularity in password policy recommendations; however, data-driven guessing studies are woefully inadequate in adapting to long passwords, lacking in both guessing efficiency and their composition guidelines. For state-of-the-art data-driven password guessing methods such as PCFGs (Probabilistic Context-free Grammars), their guessing efficiency is limited by the presence of a large scale training data, or the lack thereof. Given that long passwords leaked in the real world are typically scarce, coupled with the fact that the data-driven methods’ performance depends on training data, obtaining good performance on long passwords has become a key challenge. To overcome the dataset limitation, we propose a framework TransPCFG, that transfers the knowledge, (i.e., grammars in PCFGs), from short passwords to facilitate long password guessing. We further perform an empirical evaluation based on three real-world datasets and the results demonstrate superior performance over the state-of-the-art data-driven guessing methods under ${10}^{14}$ offline guesses. For passwords with 16 characters, TransPCFG can compromise an average of 23.30% of the passwords, outperforming PCFG_v4.1 by 56.10%. Additionally,for better password-composition guidelines, we find that long password-composition policies requiring more segments are more resistant to guessing attacks. For the segment, the password 12zxcvbnword1997 has four segments since it follows the template ${Digit}_{2}{Keyboard}_{6}{Letter}_{4}{Year}_{4}$ . We thus recommend users to create long passwords with four or more segments instead of the widely recommended more character classes for security.

Wednesday, 02 September 2020

Thursday, 27 August 2020

02:02 AM

Convergent Validity, Reliability, and Sensitivity of a Running Test to Monitor Neuromuscular Fatigue [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 8
Pages: 1067-1073

Relationship Between Tethered Swimming in a Flume and Swimming Performance [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 8
Pages: 1087-1094

Transferable Benefits of Cycle Hypoventilation Training for Run-Based Performance in Team-Sport Athletes [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 8
Pages: 1103-1108

Core Temperature and Sweating in Men and Women During a 15-km Race in Cool Conditions [International Journal of Sports Physiology and Performance]

Journal Name: International Journal of Sports Physiology and Performance
Volume: 15
Issue: 8
Pages: 1132-1137

Tuesday, 25 August 2020

02:03 AM

A Novel High-Voltage DC Transformer Based on Diode-Clamped Modular Multilevel Converters With Voltage Self-Balancing Capability [IEEE Transactions on Industrial Electronics - new TOC]

DC transformers are essential for dc grids to interconnect different dc networks. This article proposed a high-voltage dc transformer, which consists of two diode-clamped modular multilevel converters (DCM2Cs) front-to-front connected with a medium-frequency transformer. The DCM2C is prominent for its simplified voltage balancing control using clamping circuits. However, a voltage controller and corresponding measuring circuits are still needed for each converter arm. With the existing quasi two-level modulation technology, this article proposed a voltage self-balancing method of the DCM2C when applied to the dc transformer. This approach is beneficial for reducing the overall cost of the system with no voltage sensor requirements. Meanwhile, the power loss of the clamping circuit is kept low with the clamping inductor. Both mathematical analysis and experimental results are conducted to verify the effectiveness of the proposed converter.

Adaptive Visual Servoing for an Underwater Soft Robot Considering Refraction Effects [IEEE Transactions on Industrial Electronics - new TOC]

Robots inspired from marine organisms are tremendously developed for applications of underwater exploration, rescuing, navigation, etc. In this article, we implement an uncalibrated visual servoing scheme to achieve accurate positioning performance of an octopus-tentacle-like soft robot arm. The image-based adaptive visual servoing controller is designed based on the underwater dynamic model of the robot system. An adaptive mechanism to solve the tedious camera calibration problem is also extremely complicated in underwater environment due to the refraction effect resulting in changes of optical condition. In this article, this effect is analogous to the radial distortion. The presented algorithm can linearize the distortion model, and then online iteratively estimate the unknown image mapping model based on the classical Slotine-Li adaptive algorithm. The intrinsic and extrinsic parameters can also be estimated in real time. The presented adaptive controller is verified both theoretically using the Lyapunov stability analysis to prove the stability of the dynamical system, and experimentally to prove the accuracy and rapid convergence to the target image position.

Performance Model for Advanced Neighbor Discovery Process in Bluetooth Low Energy 5.0-Enabled Internet of Things Networks [IEEE Transactions on Industrial Electronics - new TOC]

The neighbor discovery process (NDP) plays a key role in Bluetooth low energy (BLE)-enabled applications. However, when a massive number of BLE devices are involved in the NDP, signal collisions become severer, which degrade the NDP performance seriously. To solve the problem, BLE 5.0 specifies some extended features such as advanced NDP (A-NDP). The previous works on the performance models for the NDP have focused on the basic NDP (B-NDP). Though some works considered the performance evaluations on A-NDP, they have done on simulations or testbed-based experiments. Likewise, so far, the performance analysis for A-NDP has been insufficient. In this article, we propose an analytical model to evaluate A-NDP performances, such as the signal collision probability, the discovery delay, the energy consumption, and so on. With the proposed analysis model, the performances of A-NDP are analyzed from the viewpoint of various operational environments for BLE-enabled Internet-of-Things services, and compared to those of B-NDP with extended features of Bluetooth 5.0. Besides, it showed that A-NDP is not always better than B-NDP, and we discuss the operational conditions where A-NDP or B-NDP operate effectively, respectively.

Thursday, 20 August 2020

Friday, 14 August 2020

02:02 AM

Relational Deep Feature Learning for Heterogeneous Face Recognition [IEEE Transactions on Information Forensics and Security - new TOC]

Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information. However, these pre-trained features cause performance degradation due to the texture discrepancy with the visual domain. With this motivation, we propose a graph-structured module called Relational Graph Module (RGM) that extracts global relational information in addition to general facial features. Because each identity’s relational information between intra-facial parts is similar in any modality, the modeling relationship between features can help cross-domain matching. Through the RGM, relation propagation diminishes texture dependency without losing its advantages from the pre-trained features. Furthermore, the RGM captures global facial geometrics from locally correlated convolutional features to identify long-range relationships. In addition, we propose a Node Attention Unit (NAU) that performs node-wise recalibration to concentrate on the more informative nodes arising from relation-based propagation. Furthermore, we suggest a novel conditional-margin loss function ( $C$ -softmax) for the efficient projection learning of the embedding vector in HFR. The proposed method outperforms other state-of-the-art methods on five HFR databases. Furthermore, we demonstrate performance improvement on three backbones because our module can be plugged into any pre-trained face recognition backbone to overcome the limitations of a small HFR database.

Tuesday, 11 August 2020

02:02 AM

End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network [IEEE Transactions on Image Processing - new TOC]

The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly use recognition-oriented CNN for quality prediction. However, the properties of IQA task is different from image recognition task. Image recognition should be sensitive to visual content and robust to distortion, while IQA should be sensitive to both distortion and visual content. In this paper, an IQA-oriented CNN method is developed for blind IQA (BIQA), which can efficiently represent the quality degradation. CNN is large-data driven, while the sizes of existing IQA databases are too small for CNN optimization. Thus, a large IQA dataset is firstly established, which includes more than one million distorted images (each image is assigned with a quality score as its substitute of Mean Opinion Score (MOS), abbreviated as pseudo-MOS). Next, inspired by the hierarchical perception mechanism (from local structure to global semantics) in human visual system, a novel IQA-orientated CNN method is designed, in which the hierarchical degradation is considered. Finally, by jointly optimizing the multilevel feature extraction, hierarchical degradation concatenation (HDC) and quality prediction in an end-to-end framework, the Cascaded CNN with HDC (named as CaHDC) is introduced. Experiments on the benchmark IQA databases demonstrate the superiority of CaHDC compared with existing BIQA methods. Meanwhile, the CaHDC (with about 0.73M parameters) is lightweight comparing to other CNN-based BIQA models, which can be easily realized in the microprocessing system. The dataset and source code of the proposed method are available at https://web.xidian.edu.cn/wjj/paper.html.

Tuesday, 04 August 2020

02:02 AM

Poverty and the Labor Market: Today and Yesterday [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 107-134, August 2020.

How Distortions Alter the Impacts of International Trade in Developing Countries [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 213-238, August 2020.

Cities in the Developing World [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 273-297, August 2020.

Social Identity and Economic Policy [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 355-389, August 2020.

Peer Effects in Networks: A Survey [Annual Reviews: Annual Review of Economics: Table of Contents]

Annual Review of Economics, Volume 12, Issue 1, Page 603-629, August 2020.

Sparse Domain Gaussianization for Multi-Variate Statistical Modeling of Retinal OCT Images [IEEE Transactions on Image Processing - new TOC]

In this paper, a multivariate statistical model that is suitable for describing Optical Coherence Tomography (OCT) images is introduced. The proposed model is comprised of a multivariate Gaussianization function in sparse domain. Such an approach has two advantages, i.e. 1) finding a function that can effectively transform the input - which is often not Gaussian - into normally distributed samples enables the reliable application of methods that assume Gaussianity, 2) although multivariate Gaussianization in spatial domain is a complicated task and rarely results in closed-form analytical model, by transferring data to sparse domain, our approach facilitates multivariate statistical modeling of OCT images. To this end, a proper multivariate probability density function (pdf) which considers all three properties of OCT images in sparse domains (i.e. compression, clustering, and persistence properties) is designed and the proposed sparse domain Gaussianization framework is established. Using this multivariate model, we show that the OCT images often follow a 2-component multivariate Laplace mixture model in the sparse domain. To evaluate the performance of the proposed model, it is employed for OCT image denoising in a Bayesian framework. Visual and numerical comparison with previous prominent methods reveals that our method improves the overall contrast of the image, preserves edges, suppresses background noise to a desirable amount, but is less capable of maintaining tissue texture. As a result, this method is suitable for applications where edge preservation is crucial, and a clean noiseless image is desired.

Friday, 31 July 2020

02:02 AM

The Comparative Politics of Collective Memory [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 175-194, July 2020.

What Do Platforms Do? Understanding the Gig Economy [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 273-294, July 2020.

Multiracial Categorization, Identity, and Policy in (Mixed) Racial Formations [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 335-353, July 2020.

Transnational Professionals [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 399-417, July 2020.

A Verifiable Semantic Searching Scheme by Optimal Matching Over Encrypted Data in Public Cloud [IEEE Transactions on Information Forensics and Security - new TOC]

Semantic searching over encrypted data is a crucial task for secure information retrieval in public cloud. It aims to provide retrieval service to arbitrary words so that queries and search results are flexible. In existing semantic searching schemes, the verifiable searching does not be supported since it is dependent on the forecasted results from predefined keywords to verify the search results from cloud, and the queries are expanded on plaintext and the exact matching is performed by the extended semantically words with predefined keywords, which limits their accuracy. In this paper, we propose a secure verifiable semantic searching scheme. For semantic optimal matching on ciphertext, we formulate word transportation (WT) problem to calculate the minimum word transportation cost (MWTC) as the similarity between queries and documents, and propose a secure transformation to transform WT problems into random linear programming (LP) problems to obtain the encrypted MWTC. For verifiability, we explore the duality theorem of LP to design a verification mechanism using the intermediate data produced in matching process to verify the correctness of search results. Security analysis demonstrates that our scheme can guarantee verifiability and confidentiality. Experimental results on two datasets show our scheme has higher accuracy than other schemes.

Thursday, 30 July 2020

06:02 PM

Violence in Latin America: An Overview of Research and Issues [Annual Reviews: Annual Review of Sociology: Table of Contents]

Annual Review of Sociology, Volume 46, Issue 1, Page 693-706, July 2020.

Tuesday, 28 July 2020

06:02 PM

Algorithms as discrimination detectors [Computer Sciences] [Early Edition]

Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity...

Tuesday, 09 June 2020

02:01 AM

Deblurring Face Images Using Uncertainty Guided Multi-Stream Semantic Networks [IEEE Transactions on Image Processing - new TOC]

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: https://github.com/rajeevyasarla/UMSN-Face-Deblurring.

Tuesday, 12 May 2020

02:02 AM

Partisan Gerrymandering and Political Science [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 171-185, May 2020.

The Fluidity of Racial Classifications [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 221-240, May 2020.

Clientelism's Red Herrings: Dead Ends and New Directions in the Study of Nonprogrammatic Politics [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 277-294, May 2020.

The Changing Cleavage Politics of Western Europe [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 295-314, May 2020.

Survey Experiments in International Political Economy: What We (Don't) Know About the Backlash Against Globalization [Annual Reviews: Annual Review of Political Science: Table of Contents]

Annual Review of Political Science, Volume 23, Issue 1, Page 333-356, May 2020.

Monday, 13 April 2020

02:00 PM

Time-Restricted Eating to Prevent and Manage Chronic Metabolic Diseases [Annual Reviews: Annual Review of Nutrition: Table of Contents]

Annual Review of Nutrition, Volume 39, Issue 1, Page 291-315, August 2019.

Thursday, 12 March 2020

03:00 PM

Improving the Harmony of the Composite Image by Spatial-Separated Attention Module [IEEE Transactions on Image Processing - new TOC]

Image composition is one of the most important applications in image processing. However, the inharmonious appearance between the spliced region and background degrade the quality of the image. Thus, we address the problem of Image Harmonization: Given a spliced image and the mask of the spliced region, we try to harmonize the “style” of the pasted region with the background (non-spliced region). Previous approaches have been focusing on learning directly by the neural network. In this work, we start from an empirical observation: the differences can only be found in the spliced region between the spliced image and the harmonized result while they share the same semantic information and the appearance in the non-spliced region. Thus, in order to learn the feature map in the masked region and the others individually, we propose a novel attention module named Spatial-Separated Attention Module (S2AM). Furthermore, we design a novel image harmonization framework by inserting the S2AM in the coarser low-level features of the Unet structure by two different ways. Besides image harmonization, we make a big step for harmonizing the composite image without the specific mask under previous observation. The experiments show that the proposed S2AM performs better than other state-of-the-art attention modules in our task. Moreover, we demonstrate the advantages of our model against other state-of-the-art image harmonization methods via criteria from multiple points of view.

Tuesday, 11 February 2020

03:00 PM

New Approaches to Target Inflammation in Heart Failure: Harnessing Insights from Studies of Immune Cell Diversity [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 1-20, February 2020.

Neuronal Mechanisms that Drive Organismal Aging Through the Lens of Perception [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 227-249, February 2020.

Diurnal Regulation of Renal Electrolyte Excretion: The Role of Paracrine Factors [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 343-363, February 2020.

Marrow Adipocytes: Origin, Structure, and Function [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 461-484, February 2020.

The Osteocyte: New Insights [Annual Reviews: Annual Review of Physiology: Table of Contents]

Annual Review of Physiology, Volume 82, Issue 1, Page 485-506, February 2020.

Monday, 03 February 2020

03:00 PM

Reverse Attention-Based Residual Network for Salient Object Detection [IEEE Transactions on Image Processing - new TOC]

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel top-down reverse attention block to guide the above side-output residual learning. Specifically, the current predicted salient regions are used to erase its side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in more complete detection and high accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art approaches, and shows advantages in simplicity, compactness and efficiency.

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