Query: A New Low Cost Leaky Wave Coplanar Waveguide Continuous Transverse Stub Antenna Array Using Metamaterial-Based Phase Shifters for Beam Steering

Relevant passages:
Surface-Wave Launchers for Beam Steering and Application to Planar Leaky-Wave Antennas <=SEP=> A linear array of surface-wave launchers (SWLs) is presented for surface-wave (SW) beam steering on a grounded dielectric slab (GDS). Specifically, a two-element array of directive Yagi-Uda like SWL antennas (operating between 21-24 GHz) is investigated. By varying the relative phase difference between SWL elements, the excited SW field distribution can be steered. In addition, a six-element linear array of SWLs with nonuniform weighting is also presented. If metallic gratings are placed on top of the GDS, the bound SW mode can be transformed into a radiated leaky-wave (LW) mode and directive beam patterns can be generated in the far field. A specific elliptical grating configuration is also investigated demonstrating single frequency cylindrical-sector LW beam steering. These slotted SWL arrays can be useful for novel SW beam scanning designs, millimeter wave power distribution systems and new LW antennas.

Holographic artificial impedance surfaces for conformal antennas <=SEP=> We have developed a method for generating arbitrary radiation patterns from antennas on complex objects. The object is coated with an artificial impedance surface consisting of a lattice of sub-wavelength metal patches on a grounded dielectric substrate. The effective surface impedance depends on the size of the patches, and can be varied as a function of position. Using holography, the surface impedance is designed to generate any desired radiation pattern from currents in the surface. With this technique we can create antennas with novel properties such as radiation toward angles that would otherwise be shadowed

Planar Leaky-Wave Antenna Designs Offering Conical-Sector Beam Scanning and Broadside Radiation Using Surface-Wave Launchers <=SEP=> Two planar antenna designs that utilize surface-waves for leaky-wave excitation are investigated. Specifically, a surface-wave launcher is implemented to excite cylindrical surface-waves, which are bound to a grounded dielectric slab. By the addition of circular metallic strip gratings, a partially reflecting surface is realized, providing appropriate conditions for 2-D leaky-wave radiation. In particular, two designs are investigated: a continuous circular strip and a segmented circular strip grating. Results illustrate conical-sector beam scanning for the continuous circular strip grating between 2022 GHz, while broadside radiation is observed at 21.2 GHz by the segmented circular strip design.

Low-Cost Phased-Array Antenna Using Compact Tunable Phase Shifters Based on Ferroelectric Ceramics <=SEP=> A low-cost phased-array antenna at 10 GHz is presented for a scan angle of ±50°. The array employs continuously tunable phase shifters based on a screen printed barium-strontium-titanate thick-film ceramic. Due to the use of artificial transmission line topology, the proposed phase-shifter design has a very compact size (3 mm × 2.8 mm) for 342° total phase shift. In the frequency range from 8 to 10 GHz, it exhibits a figure of merit >;52°/dB, which is among the best of phase shifters based on ferroelectric thick films. In a prototyped phased array, the RF circuit consists of a feeding network, phase shifters, and antenna elements, which are integrated into one planar metallization layer. Furthermore, a simple way for routing bias lines for phase shifters is demonstrated using high resistive electrodes. Using screen printed thick films and applying a simplified fabrication process for the RF and bias circuitry can reduce the total expense of phased arrays considerably.

Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations <=SEP=> Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of an array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods—including their performance comparison—and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing. It is presented at a level appropriate to nonexperts in the field and contains a large reference list to probe further.

Architectural Mismatch or Why it's hard to build systems out of existing parts <=SEP=> Many would argue that future breakthroughs in software productivity will depend on our ability to combine existing pieces of software to produce new applications. An important step towards this goal is the development of new techniques to detect and cope with mismatches in the assembled parts. Some problems of composition are due to low-level issues of interoperability, such as mismatches in programming languages or database schemas. However, in this paper we highlight a different, and in many ways more pervasive, class of problem: architectural mismatch. Specifically, we use our experience in building a family of software design environments from existing parts to illustrate a variety of types of mismatch that center around the assumptions a reusable part makes about the structure of the application in which is to appear. Based on this experience we show how an architectural view of the mismatch problem exposes some fundamental, thorny problems for software composition and suggests possible research avenues needed to solve them.

Use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer <=SEP=> Objective: To evaluate the use of the scored Patient-Generated Subjective Global Assessment (PG-SGA) as a nutrition assessment tool in patients with cancer.Design: An observational study assessing the nutritional status of patients with cancer.Setting: Oncology ward of a private tertiary Australian hospital.Subjects: Seventy-one cancer patients aged 18–92 y.Intervention: Scored PG-SGA questionnaire, comparison of scored PG-SGA with subjective global assessment (SGA), sensitivity, specificity.Results: Some 24% (17) of 71 patients were well nourished, 59% (42) of patients were moderately or suspected of being malnourished and 17% (12) of patients were severely malnourished according to subjective global assessment (SGA). The PG-SGA score had a sensitivity of 98% and a specificity of 82% at predicting SGA classification. There was a significant difference in the median PG-SGA scores for each of the SGA classifications (P<0.001), with the severely malnourished patients having the highest scores. Re-admission within 30 days of discharge was significantly different between SGA groups (P=0.037). The mortality rate within 30 days of discharge was not significantly different between SGA groups (P=0.305). The median length of stay of well nourished patients (SGA A) was significantly lower than that of the malnourished (SGA B+C) patients (P=0.024).Conclusion: The scored PG-SGA is an easy to use nutrition assessment tool that allows quick identification and prioritisation of malnutrition in hospitalised patients with cancer.Sponsors: The Wesley Research Institute.

Comparison of Discovery Service Architectures for the Internet of Things <=SEP=> In the emerging Internet of Things rich data on real-world objects and events will be generated in vast amounts and stored in widely distributed databases. In truly global and dynamic application scenarios, intermediate brokers are needed to find these data, even if the exact location and form of storage are initially unknown to the requester. Discovery Services are aimed to fill this gap: they respond to requests for data on specific objects with a list of corresponding data providers. In this paper, we frame functional requirements for Discovery Services, and perform an overview and analysis of five established approaches for implementing Discovery Services that are taken from literature and industrial practice. In order to compare their characteristics, we develop a quality framework based on literature review and an ISO standard for software quality.

KPB-SIFT: a compact local feature descriptor <=SEP=> Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.

CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors <=SEP=> Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CatchTartan to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CatchTartan outperforms the baselines on both the accuracy and speed. We apply CatchTartan to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.

Stronger Security of Authenticated Key Exchange <=SEP=> Recent work by Krawczyk [12] and Menezes [16] has highlighted the importance of understanding well the guarantees and limitations of formal security models when using them to prove the security of protocols. In this paper we focus on security models for authenticated key exchange (AKE) protocols. We observe that there are several classes of attacks on AKE protocols that lie outside the scope of the Canetti-Krawczyk model. Some of these additional attacks have already been considered by Krawczyk [12]. In an attempt to bring these attacks within the scope of the security model we extend the Canetti-Krawczyk model for AKE security by providing significantly greater powers to the adversary. Our contribution is a more compact, integrated, and comprehensive formulation of the security model. We then introduce a new AKE protocol called NAXOS and prove that it is secure against these stronger

Effectiveness of a multi-layer foam dressing in preventing sacral pressure ulcers for the early acute care of patients with a traumatic spinal cord injury: comparison with the use of a gel mattress. <=SEP=> Individuals with spinal cord injury are at risk of sacral pressure ulcers due to, among other reasons, prolonged immobilisation. The effectiveness of a multi-layer foam dressing installed pre-operatively in reducing sacral pressure ulcer occurrence in spinal cord injured patients was compared to that of using a gel mattress, and stratified analyses were performed on patients with complete tetraplegia and paraplegia. Socio-demographic and clinical data were collected from 315 patients admitted in a level-I trauma centre following a spinal cord injury between April 2010 and March 2016. Upon arrival to the emergency room and until surgery, patients were transferred on a foam stretcher pad with a viscoelastic polymer gel mattress (before 1 October 2014) or received a multi-layer foam dressing applied to their sacral-coccygeal area (after 1 October 2014). The occurrence of sacral pressure ulcer during acute hospitalisation was similar irrespective of whether patients received the dressing or the gel mattress. It was found that 82% of patients with complete tetraplegia receiving the preventive dressing developed sacral ulcers as compared to only 36% of patients using the gel mattress. Although multi-layer dressings were suggested to improve skin protection and decrease pressure ulcer occurrence in critically ill patients, such preventive dressings are not superior to gel mattresses in spinal cord injured patients and should be used with precaution, especially in complete tetraplegia.

Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses <=SEP=> This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. Note to Practitioners-The motivation of this work grows from the need to increase the flexibility and efficiency of current multi-AGV systems. In particular, in the current state-of-the-art the AGVs are guided through a manually defined roadmap with ad-hoc strategies for coordination. This is translated into high setup time requested for installation and the impossibility to easily respond to dynamic changes of the environment. The proposed method aims at managing all the design, setup and control process in an automatic manner, decreasing the time for setup and installation. The flexibility is also increased by considering a coordination strategy for the fleet of AGVs not based on manual ad-hoc rules. Simulations are performed in order to compare the proposed approach to the current industrial one. In the future, industrial aspects, as the warehouse management system, will be integrated in order to achieve a real and complete industrial functionality.

Robust odometry estimation for RGB-D cameras <=SEP=> The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.

Standard high-resolution pelvic MRI vs. low-resolution pelvic MRI in the evaluation of deep infiltrating endometriosis <=SEP=> To compare the capabilities of standard pelvic MRI with low-resolution pelvic MRI using fast breath-hold sequences to evaluate deep infiltrating endometriosis (DIE). Sixty-eight consecutive women with suspected DIE were studied with pelvic MRI. A double-acquisition protocol was carried out in each case. High-resolution (HR)-MRI consisted of axial, sagittal, and coronal TSE T2W images, axial TSE T1W, and axial THRIVE. Low-resolution (LR)-MRI was acquired using fast single shot (SSH) T2 and T1 images. Two radiologists with 10 and 2 years of experience reviewed HR and LR images in two separate sessions. The presence of endometriotic lesions of the uterosacral ligament (USL), rectovaginal septum (RVS), pouch of Douglas (POD), and rectal wall was noted. The accuracies of LR-MRI and HR-MRI were compared with the laparoscopic and histopathological findings. Average acquisition times were 24 minutes for HR-MRI and 7 minutes for LR-MRI. The more experienced radiologist achieved higher accuracy with both HR-MRI and LR-MRI. The values of sensitivity, specificity, PPV, NPV, and accuracy did not significantly change between HR and LR images or interobserver agreement for all of the considered anatomic sites. LR-MRI performs as well as HR-MRI and is a valuable tool for the detection of deep endometriosis extension. • High- and low-resolution MRI perform similarly in deep endometriosis evaluation • Low-resolution MRI significantly reduces the duration of the examination • Radiologist experience is fundamental for evaluating deep pelvic endometriosis

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. <=SEP=> Importance
A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.


Objective
To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.


Design, Setting, and Participants
Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.


Exposures
Use of a deep learning system.


Main Outcomes and Measures
Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.


Results
In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).


Conclusions and Relevance
In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Group discussion as interactive dialogue or as serial monologue: the influence of group size. <=SEP=> Current models draw a broad distinction between communication as dialogue and communication as monologue. The two kinds of models have different implications for who influences whom in a group discussion. If the discussion is like interactive dialogue, group members should be influenced most by those with whom they interact in the discussion; if it is like serial monologue, they should be influenced most by the dominant speaker. The experiments reported here show that in small, 5-person groups, the communication is like dialogue and members are influenced most by those with whom they interact in the discussion. However, in large, 10-person groups, the communication is like monologue and members are influenced most by the dominant speaker. The difference in mode of communication is explained in terms of how speakers in the two sizes of groups design their utterances for different audiences.

Attacking the Washington, D.C. Internet Voting System <=SEP=> In 2010, Washington, D.C. developed an Internet voting pilot project that was intended to allow overseas absentee voters to cast their ballots using a website. Prior to deploying the system in the general election, the District held a unique public trial: a mock election during which anyone was invited to test the system or attempt to compromise its security. This paper describes our experience participating in this trial. Within 48 hours of the system going live, we had gained nearcomplete control of the election server. We successfully changed every vote and revealed almost every secret ballot. Election officials did not detect our intrusion for nearly two business days—and might have remained unaware for far longer had we not deliberately left a prominent clue. This case study—the first (to our knowledge) to analyze the security of a government Internet voting system from the perspective of an attacker in a realistic pre-election deployment—attempts to illuminate the practical challenges of securing online voting as practiced today by a growing number of jurisdictions.

Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification <=SEP=> After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.

Web-based statistical fact checking of textual documents <=SEP=> User generated content has been growing tremendously in recent years. This content reflects the interests and the diversity of online users. In turn, the diversity among internet users is also reflected in the quality of the content being published online. This increases the need to develop means to gauge the support available for content posted online. In this work, we aim to make use of the web-content to calculate a statistical support score for textual documents. In the proposed algorithm, phrases representing key facts are extracted to construct basic elements of the document. Search is used thereon to validate the support available for these elements online, leading to assigning an overall score for each document. Experimental results have shown a difference between the score distribution of factual news data and false facts data. This indicates that the approach seems to be a promising seed for distinguishing different articles based on the content.

Aspect identification and sentiment analysis based on NLP <=SEP=> This paper proposes method for aspect identification and sentiment analysis. This paper deals with the problem of sentence level. We use semantic syntax tree to extract NP nodes, a method based on slack to identify aspect and mini-support function to narrow aspect; to analyze the sentiment for the aspect we use dictionary-based method. Experimental results show that the proposed approach achieves the state-of-the-art.

Effectiveness of Blog Advertising: Impact of Communicator Expertise, Advertising Intent, and Product Involvement <=SEP=> Blog advertising, which refers to the paid sponsorship of bloggers to review, promote, or sell products in their blog writing, is becoming prevalent. This paper investigates the impact of three critical factors on blog advertising effectiveness: communicator expertise, advertising intent, and product involvement. An experiment with a 2×2×2 factorial design was used to test their interaction effects on advertising effectiveness. The results indicate that, for low-involvement products, there is better advertising effectiveness when low-expertise communicators are explicit about the advertising intent or when high-expertise communicators are implicit about the advertising intent. But for high-involvement products, the results show that when low-expertise communicators are explicit about the advertising intent, the outcome is lesser advertising effectiveness. For such products, advertising effectiveness does not differ when high-expertise communicators are implicit or explicit about the advertising intent. Based on these results, some implications for further research and practice are given.

A study of interactive 3D point location in a computer simulated virtual environment <=SEP=> The present study investigated the ability to inter actively locate points in a three dimensional computer environment using a six degree of freedom input device. Four different visu al feedback modes were tested: fixed viewpoint monoscopic persp ective, fixed viewpoint stereoscopic perspective, head-tracked mo noscopic perspective and head-tracked stereoscopic perspecti ve. Targets were located at plus and minus 10cm along the X, Y or Z axes, from a fixed starting location. Data about the time to complete the task and positioning accuracy (error) are gathered for each trial. In addition, subjective feedback regarding the apparat us, visual mode and task difficulty was solicited from subject s. The results indicate that stereoscopic performance is superior to monoscopic performance and that asymmetries exist both across and within axes. Head tracking had no appreciable effect upon performance. Subjective feedback regarding performance is usuall y consistent with objective measures, but some inconsistencies a re present.

Development and Evaluation of a Bluetooth EKG Monitoring Sensor <=SEP=> With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient 's heart anywhere cellular coverage is available

The Bitcoin Backbone Protocol: Analysis and Applications <=SEP=> Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove two of its fundamental properties which we call common prefix and chain quality in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the “hashing power” of the adversary relative to network synchronicity; we show our results to be tight under high synchronization. Next, we propose and analyze applications that can be built “on top” of the backbone protocol, specifically focusing on Byzantine agreement (BA) and on the notion of a public transaction ledger. Regarding BA, we observe that Nakamoto’s suggestion falls short of solving it, and present a simple alternative which works assuming that the adversary’s hashing power is bounded by 1/3. The public transaction ledger captures the essence of Bitcoin’s operation as a cryptocurrency, in the sense that it guarantees the liveness and persistence of committed transactions. Based on this notion we describe and analyze the Bitcoin system as well as a more elaborate BA protocol, proving them secure assuming high network synchronicity and that the adversary’s hashing power is strictly less than 1/2, while the adversarial bound needed for security decreases as the network desynchronizes. Finally, we show that our analysis of the Bitcoin backbone protocol for synchronous networks extends with relative ease to the recently considered “partially synchronous” model, where there is an upper bound in the delay of messages that is unknown to the honest parties.

On the use of MapReduce for imbalanced big data using Random Forest <=SEP=> In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space and time requirements. Furthermore, real-world data applications usually present a class distribution where the samples that belong to one class, which is precisely the main interest, are hugely outnumbered by the samples of the other classes. This circumstance , known as the class imbalance problem, complicates the learning process as the standard learning techniques do not correctly address this situation. In this work, we analyse the performance of several techniques used to deal with imbal-anced datasets in the big data scenario using the Random Forest classifier. Specifically, oversampling, undersampling and cost-sensitive learning have been adapted to big data using MapReduce so that these techniques are able to manage datasets as large as needed providing the necessary support to correctly identify the underrepresented class. The Random Forest classifier provides a solid basis for the comparison because of its performance, robustness and versatility. An experimental study is carried out to evaluate the performance of the diverse algorithms considered. The results obtained show that there is not an approach to imbalanced big data classification that outperforms the others for all the data considered when using Random Forest. Moreover, even for the same type of problem, the best performing method is dependent on the number of mappers selected to run the experiments. In most of the cases, when the number of splits is increased, an improvement in the running times can be observed, however, this progress in times is obtained at the expense of a slight drop in the accuracy performance obtained. This decrement in the performance is related to the lack of density problem, which is evaluated in this work from the imbalanced data point of view, as this issue degrades the performance of classifiers in the imbalanced scenario more severely than in standard learning.

PhoneyC: A Virtual Client Honeypot <=SEP=> The number of client-side attacks has grown significantly in the past few years, shifting focus away from defendable positions to a broad, poorly defended space filled with vulnerable clients. Just as honeypots enabled deep research into server-side attacks, honeyclients can permit the deep study of client-side attacks. A complement to honeypots, a honeyclient is a tool designed to mimic the behavior of a userdriven network client application, such as a web browser, and be exploited by an attacker’s content. These systems are instrumented to discover what happened and how. This paper presents PhoneyC, a honeyclient tool that can provide visibility into new and complex client-side attacks. PhoneyC is a virtual honeyclient, meaning it is not a real application but rather an emulated client. By using dynamic analysis, PhoneyC is able to remove the obfuscation from many malicious pages. Furthermore, PhoneyC emulates specific vulnerabilities to pinpoint the attack vector. PhoneyC is a modular framework that enables the study of malicious HTTP pages and understands modern vulnerabilities and attacker techniques.

The treatment of osteoporotic thoracolumbar severe burst fractures with short pedicle screw fixation and vertebroplasty. <=SEP=> BACKGROUND
To investigate the clinical and radiological results of short pedicle screw fixation and vertebroplasty in osteoporotic thoracolumbar severe burst fractures.


METHODS
From September 2006 to August 2010, 19 consecutive patients sustained osteoporotic thoracolumbar severe burst fractures with or without neurologic deficit and were included in this prospective study. All patients underwent short pedicle screw fixation and vertebroplasty. Segmental kyphosis, AVBHr and PVBHr, and Canal compromise were calculated on radiographs pre-operatively, post-operative and at final follow up. VAS, ODI and SF-36 were calculated pre-operatively and at final follow up.


RESULTS
Mean operative time was 70.8 min (range 60~100 min) and mean blood loss was 92 ml (range 60~160 ml). The mean duration of their hospital stay was 4.5 days (range 3-7 days). The operative incisions were healing well. Average follow up time was 40.1 months (range 24~72 months). The AVBHr was corrected from preoperative (48.1 ± 6.8) % to postoperative (94.1 ± 1.7) % (P < 0.001). The PVBHr was corrected from preoperative (62.7 ± 4.8) % to postoperative (92.8 ± 1.8) % (P < 0.001). Canal compromise was corrected from preoperative (37.3 ± 5.8) % to postoperative (5.9 ± 2.3) % (P < 0.001). The segmental kyphosis was corrected from preoperative (20.6 ± 5.3) degree to postoperative (2.0 ± 3.2) degree (P < 0.001). VAS scores were reduced from preoperative 7.21 ± 0.86 to 2.21 ± 0.98 at final follow up (P < 0.001). SF-36 Bodily pain was reduced from preoperative 75.31 ± 13.85 to 13.74 ± 13.24 at final follow up (P < 0.001), and SF-36 Role Physical was reduced from preoperative 59.21 ± 26.63 to 19.74 ± 22.94 at final follow up (P < 0.001). The ODI scores were reduced from preoperative 81.68 ± 4.44 to 15.37 ± 5.54 at final follow up (P < 0.001). All 4 patients with partial neurological deficit initially had improvement. Cement leakage was observed in 3 cases (two anterior to vertebral body and one into the disc without sequela). There were no instances of instrumentation failure and no patient had persistent postoperative back pain.


CONCLUSIONS
Vertebroplasty and short pedicle screw fixation has the advantages of both radiographic and functional results for treating osteoporotic thoracolumbar severe burst fractures using a purely posterior approach.

An efficient centralized scheduling algorithm in IEEE 802.15.4e TSCH networks <=SEP=> IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) standard has gained a lot of attention within the Industrial Internet of Things research community due to its effectiveness in improving reliability and providing ultra-low power consumption for industrial applications, and in which its communication is orchestrated by a schedule. Despite its relevance, the standard leaves out of its scope in defining how the schedule is built, updated and maintained. This open issue is one of the trending topics in the IETF 6TiSCH WG, that still need to be addressed. This work focuses on scheduling in TSCH networks in a centralized manner where the gateway makes time and frequency slot allocation. This paper formulates the scheduling problem as a throughput maximization problem and delay minimization problem. We propose a graph theoretical approach to solve the throughput maximization problem in a centralized way. The combinatorial properties of the scheduling problem are addressed by providing an equivalent maximum weighted bipartite matching (MWBM) problem to reduce the computational complexity and also adopting the Hungarian algorithm in polynomial time. Simulation results are provided to evaluate the performance of the proposed scheme.

Effects of Presowing Pulsed Electromagnetic Treatment of Tomato Seed on Growth, Yield, and Lycopene Content <=SEP=> The use of magnetic field as a presowing treatment has been adopted by researchers as a new environmental friendly technique. The aim of this study was to determine the effect of magnetic field exposure on tomato seeds covering a range of parameters such as transplanting percentage, plant height, shoot diameter, number of leaves per plant, fresh weight, dry weight, number of flowers, yield, and lycopene content. Pulsed electromagnetic field was used for 0, 5, 10, and 15 minutes as a presowing treatment of tomato seeds in a field experiment for two years. Papimi device (amplitude on the order of 12.5 mT) has been used. The use of pulsed electromagnetic field as a presowing treatment was found to enhance plant growth in tomato plants at certain duration of exposure. Magnetic field treatments and especially the exposure of 10 and 15 minutes gave the best results in all measurements, except plant height and lycopene content. Yield per plant was higher in magnetic field treatments, compared to control. MF-15 treatment yield was 80.93% higher than control treatment. Lycopene content was higher in magnetic field treatments, although values showed no statistically significant differences.