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Searching skills toolkit : finding the evidence
This is a user-friendly, hands-on guide to literature searching, which is an essential skill for all involved in health care research and development, researchers, and students from all disciplines.
Book
Applications of Wireless Sensor Networks: An Up-to-Date Survey
Wireless Sensor Networks are considered to be among the most rapidly evolving technological domains thanks to the numerous benefits that their usage provides. As a result, from their first appearance until the present day, Wireless Sensor Networks have had a continuously growing range of applications. The purpose of this article is to provide an up-to-date presentation of both traditional and most recent applications of Wireless Sensor Networks and hopefully not only enable the comprehension of this scientific area but also facilitate the perception of novel applications. In order to achieve this goal, the main categories of applications of Wireless Sensor Networks are identified, and characteristic examples of them are studied. Their particular characteristics are explained, while their pros and cons are denoted. Next, a discussion on certain considerations that are related with each one of these specific categories takes place. Finally, concluding remarks are drawn.
Journal Article
Post-quantum cryptography
Cryptography is essential for the security of online communication, cars and implanted medical devices. However, many commonly used cryptosystems will be completely broken once large quantum computers exist. Post-quantum cryptography is cryptography under the assumption that the attacker has a large quantum computer; post-quantum cryptosystems strive to remain secure even in this scenario. This relatively young research area has seen some successes in identifying mathematical operations for which quantum algorithms offer little advantage in speed, and then building cryptographic systems around those. The central challenge in post-quantum cryptography is to meet demands for cryptographic usability and flexibility without sacrificing confidence.
The era of fully fledged quantum computers threatens to destroy internet security as we know it; the ways in which modern cryptography is developing solutions are reviewed.
Journal Article
Quantum computational supremacy
The field of quantum algorithms aims to find ways to speed up the solution of computational problems by using a quantum computer. A key milestone in this field will be when a universal quantum computer performs a computational task that is beyond the capability of any classical computer, an event known as quantum supremacy. This would be easier to achieve experimentally than full-scale quantum computing, but involves new theoretical challenges. Here we present the leading proposals to achieve quantum supremacy, and discuss how we can reliably compare the power of a classical computer to the power of a quantum computer.
Proposals for demonstrating quantum supremacy, when a quantum computer supersedes any possible classical computer at a specific task, are reviewed.
Journal Article
Locally noisy autonomous agents improve global human coordination in network experiments
A networked colour coordination game, with humans interacting with autonomous software bots, shows that bots acting with small levels of random noise and being placed centrally in the network improves not only human–bot interactions but also human–human interactions at distant nodes.
Erratic AI helps humans to cooperate
Collective action towards a common goal, even if everyone's interests are aligned, faces a 'coordination' problem: an individual's attempts to reach a personal, locally optimized solution may not be optimal for the group as a whole. Now Nicholas Christakis and colleagues have introduced autonomous software ('bots') in small networks of humans engaged in solving a standard colour coordination game in which the collective goal is for every node to have a colour different from all of its neighbour nodes, so as to study the potential benefits of introducing noise in the decision making. They find that noisy bots work best when displaying moderate (10%) randomness and placed centrally in the network. Such bots not only improve human–bot but also human–human interactions at distant nodes, thus helping humans to help themselves.
Coordination in groups faces a sub-optimization problem
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and theory suggests that some randomness may help to achieve global optima
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. Here we performed experiments involving a networked colour coordination game
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in which groups of humans interacted with autonomous software agents (known as bots). Subjects (
n
= 4,000) were embedded in networks (
n
= 230) of 20 nodes, to which we sometimes added 3 bots. The bots were programmed with varying levels of behavioural randomness and different geodesic locations. We show that bots acting with small levels of random noise and placed in central locations meaningfully improve the collective performance of human groups, accelerating the median solution time by 55.6%. This is especially the case when the coordination problem is hard. Behavioural randomness worked not only by making the task of humans to whom the bots were connected easier, but also by affecting the gameplay of the humans among themselves and hence creating further cascades of benefit in global coordination in these heterogeneous systems.
Journal Article
Detecting, Preventing, and Responding to “Fraudsters” in Internet Research: Ethics and Tradeoffs
Research that recruits and surveys participants online is increasing, but is subject to fraud whereby study respondents — whether eligible or ineligible — participate multiple times. Online Internet research can provide investigators with large sample sizes and is cost efficient. Internet-based research also provides distance between the researchers and participants, allowing the participant to remain confidential and/or anonymous, and thus to respond to questions freely and honestly without worrying about the stigma associated with their answers. However, increasing and recurring instances of fraudulent activity among subjects raise challenges for researchers and Institutional Review Boards (IRBs). The distance from participants, and the potential anonymity and convenience of online research allow for individuals to participate easily more than once, skewing results and the overall quality of the data.
Journal Article
The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit
Use of the ResearchKit platform to track symptoms of a large cohort of asthma sufferers over time demonstrates the pros and cons of mobile health applications in large-scale observational studies.
The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.
Journal Article
Automatic image annotation method based on a convolutional neural network with threshold optimization
In this study, a convolutional neural network with threshold optimization (CNN-THOP) is proposed to solve the issue of overlabeling or downlabeling arising during the multilabel image annotation process in the use of a ranking function for label annotation along with prediction probability. This model fuses the threshold optimization algorithm to the CNN structure. First, an optimal model trained by the CNN is used to predict the test set images, and batch normalization (BN) is added to the CNN structure to effectively accelerate the convergence speed and obtain a group of prediction probabilities. Second, threshold optimization is performed on the obtained prediction probability to derive an optimal threshold for each class of labels to form a group of optimal thresholds. When the prediction probability for this class of labels is greater than or equal to the corresponding optimal threshold, this class of labels is used as the annotation result for the image. During the annotation process, the multilabel annotation for the image to be annotated is realized by loading the optimal model and the optimal threshold. Verification experiments are performed on the MIML, COREL5K, and MSRC datasets. Compared with the MBRM, the CNN-THOP increases the average precision on MIML, COREL5K, and MSRC by 27%, 28% and 33%, respectively. Compared with the E2E-DCNN, the CNN-THOP increases the average recall rate by 3% on both COREL5K and MSRC. The most precise annotation effect for CNN-THOP is observed on the MIML dataset, with a complete matching degree reaching 64.8%.
Journal Article
Reversible data hiding techniques with high message embedding capacity in images
Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighbouring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this paper we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images. Experimental results demonstrate that the proposed method not only increases the message embedding capacity of a given image by more than 50% but also the visual quality of the marked image containing the message is more than the visual quality obtained by existing state-of-the-art reversible data hiding technique. The proposed technique is also verified by Pixel Difference Histogram (PDH) Stegoanalysis and results demonstrate that marked images generated by proposed method is undetectable by PDH analysis.
Journal Article