Miracle-dz/UniFAR-FTUs huggingface dataset:
Verification of a Security Authentication Protocol Using Real-time Confirmation of Agents

Abstract Recently a wide range of devices using RFID have been developed and operated. RFID technology uses IC chips and wireless communication to manage information of diverse entities, and generally consists of readers, tags and databases. Tags convey information, which is in turn read by readers, and is managed in databases. As the wireless communication section between a reader and a tag uses radio waves, it is vulnerable to security issues, regarding which lots of studies have explored security protocols. Nonetheless, due to the difficulties in implementing such protocols in practice, most of studies have gone no further than theorem proving of their theories. Such theorem-proving approaches, however, often end up with quite a few difficulties in application to real systems because of vulnerabilities discovered by other subsequent investigators. This paper applied random numbers, session keys, hash functions and real-time agents to the communication session in order to design a security protocol. The CasperFDR formal verification tool was used to test the safety of the proposed protocol. In a nutshell, the proposed protocol passed the verification test, indicating that it is a secure protocol against deadlocks and live-locks.
An experimental study of graph classification using prototype selection

In structural pattern recognition, a major drawback of graph based representation is the lack of algorithmic tools. To overcome this lack, we embed graphs in vector spaces by means of prototype selection and graph edit distance, thus making them available to all algorithms of statistical pattern recognition that operate on feature vectors. In previous work a similar procedure was applied. However, the only classifier used within this framework was support vector machine (SVM). In the present paper, we significantly extend the scope of the previous work and present an experimental study where, in addition to SVM, a number of other well established classifiers from statistical pattern recognition are used for graph classification. On a total of five different graph data sets of diverse nature it is demonstrated that the proposed graph embedding in conjunction with standard classifiers from statistical pattern recognition has great potential to outperform classification methods applied in the original graph domain.
Effects of Neuraxial Blockade May Be Difficult To Study Using Large Randomized Controlled Trials: The PeriOperative Epidural Trial (POET) Pilot Study

Background Early randomized controlled trials have suggested that neuraxial blockade may reduce cardiorespiratory complications after non-cardiothoracic surgery, but recent larger trials have been inconclusive. We conducted a pilot study to assess the feasibility of conducting a large multicentre randomized controlled trial in Canada. Methodology/Principal Findings After Research Ethics Board approvals from the participating institutions, subjects were recruited if they were ≥45 years old, had an expected hospital stay ≥48 hours, were undergoing a noncardiothoracic procedure amenable to epidural analgesia, met one of six risk criteria, and did not have contraindications to neuraxial blockade. After informed consent, subjects were randomly allocated to combined epidural analgesia (epidural group) and neuraxial anesthesia, with or without general anesthesia, or intravenous opioid analgesia (IV group) and general anesthesia. The primary outcomes were the rate of recruitment and the percents of eligible patients recruited, crossed over, and followed completely. Feasibility targets were defined a priori. A blinded, independent committee adjudicated the secondary clinical outcomes. Subjects were followed daily while in hospital and then at 30 days after surgery. Analysis was intention-to-treat. Over a 15-month period, the recruitment rate was 0.5±0.3 (mean±SEM) subjects per week per centre; 112/494 (22.7%) eligible subjects were recruited at four tertiary-care teaching hospitals in Canada. Thirteen (26.5%) of 49 subjects in the epidural group crossed over to the IV group; seven (14.3%) were due to failed or inadequate analgesia or complications from epidural analgesia. Five (9.8%) of 51 subjects in the IV group crossed over to the epidural group but none were due to inadequate analgesia or complications. Ninety-eight (97.0%) of 101 subjects were successfully followed up until 30 days after their surgery. Conclusion/Significance Of the criteria we defined for the feasibility of a full-scale trial, only the follow-up target was met. The other feasibility outcomes did not meet our preset criteria for success. The results suggest that a large multicentre trial may not be a feasible design to study the perioperative effects of neuraxial blockade. Trial Registration ClinicalTrials.gov NCT 0221260 Controlled-Trials.com ISRCTN 35629817
Calibrating an Overhead Video Camera

In this section we discuss how to calibrate an overhead video camera used for tracking robots moving on a rectangular field. First, the radial distortion of the camera must be eliminated. The resulting corrected image represents a projective transformation of the field’s surface to the camera’s imaging plane. We show how to find the projective transformation, and how to recover from this information the position of the camera and the rotation of its coordinate system in relation to world’s coordinates. We discuss the numerical errors which arise in the computations and how to find an optimal solution. We also show how to track robots of different heights. 1 Overhead Camera calibration In the RoboCup small-size league, mobile robots are tracked using one or more video cameras overlooking the field from a height of three to four meters. The image registered in each camera provides pixel information from which the positions of robots and other objects in the field can be computed. The robots are marked with blobs of different colors. The geometry of the scene provides all necessary information for tracking the robots, sometimes at 60 frames per second. Tracking the robots can be done best when the image from the video camera is as undistorted as possible. However, once the video camera has been placed above the field, there is no guarantee that the symmetry axis of the optics will be pointing downwards perfectly. The camera’s coordinate system axis could be misaligned with the coordinate system of the field (which we call “world coordinates”). Also, wide angle lenses, necessary for capturing the field from only 3 or 4 meters height, introduce a significant amount of radial distortion in the image. The distance of objects to the center of the coordinate system is underestimated sometimes by as much as 30% to 40%. It is necessary to correct all these imaging artifacts before the coordinates of objects on the field can be recovered. This chapter provides all the necessary information for computing the best camera-to-field coordinates transformation (so that we can infer from pixel data where our robots are located), as well as the inverse field-to-camera transformation. We proceed as follows: – We eliminate the radial distortion, calibrating the camera before it is used, and applying a corrective transformation to the pixel’s coordinates,
Improved MMSE estimation of correlated MIMO channels using a structured correlation estimator

Channel estimation is an important and challenging task in MIMO communications. The minimum mean-square-error (MMSE) channel estimator is able to exploit spatial correlation of the MIMO channel but requires prior estimation of the channel correlation matrix. In this paper, we investigate pilot-based MMSE channel estimation including channel correlation estimation. We propose an MMSE channel estimator using a structured correlation estimator and demonstrate its advantages over conventional MMSE estimators. Simulation results show that the proposed channel estimator outperforms conventional channel estimators in the case of strong spatial correlation and at low SNR.
Evaluation of Analytical Methods for Connectivity Map Data

Connectivity map data and associated methodologies have become a valuable tool in understanding drug mechanism of action (MOA) and discovering new indications for drugs. However, few systematic evaluations have been done to assess the accuracy of these methodologies. One of the difficulties has been the lack of benchmarking data sets. Iskar et al. (PLoS. Comput. Biol. 6, 2010) predicted the Anatomical Therapeutic Chemical (ATC) drug classification based on drug-induced gene expression profile similarity (DIPS), and quantified the accuracy of their method by computing the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve. We adopt the same data and extend the methodology, by using a simpler eXtreme cosine (XCos) method, and find it does better in this limited setting than the Kolmogorov-Smirnov (KS) statistic. In fact, for partial AUC (a more relevant statistic for actual application to repositioning) XCos does 17% better than the DIPS method (p=1.2e-7). We also observe that smaller gene signatures (with 100 probes) do better than larger ones (with 500 probes), and that DMSO controls from within the same batch obviate the need for mean centering. As expected there is heterogeneity in the prediction accuracy amongst the various ATC codes. We find that good transcriptional response to drug treatment appears necessary but not sufficient to achieve high AUCs. Certain ATC codes, such as those corresponding to corticosteroids, had much higher AUCs possibly due to strong transcriptional responses and consistency in MOA.
Extremes of Interferon-Stimulated Gene Expression Associate with Worse Outcomes in the Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) severity may be influenced by heterogeneity of neutrophil activation. Interferon-stimulated genes (ISG) are a broad gene family induced by Type I interferons, often as a response to viral infections, which evokes extensive immunomodulation. We tested the hypothesis that over- or under-expression of immunomodulatory ISG by neutrophils is associated with worse clinical outcomes in patients with ARDS. Genome-wide transcriptional profiles of circulating neutrophils isolated from patients with sepsis-induced ARDS (n = 31) and healthy controls (n = 19) were used to characterize ISG expression. Hierarchical clustering of expression identified 3 distinct subject groups with Low, Mid and High ISG expression. ISG accounting for the greatest variability in expression were identified (MX1, IFIT1, and ISG15) and used to analyze a prospective cohort at the Colorado ARDS Network site. One hundred twenty ARDS patients from four urban hospitals were enrolled within 72 hours of initiation of mechanical ventilation. Circulating neutrophils were isolated from patients and expression of ISG determined by PCR. Samples were stratified by standard deviation from the mean into High (n = 21), Mid, (n = 82) or Low (n = 17) ISG expression. Clinical outcomes were compared between patients with High or Low ISG expression to those with Mid-range expression. At enrollment, there were no differences in age, gender, co-existing medical conditions, or type of physiologic injury between cohorts. After adjusting for age, race, gender and BMI, patients with either High or Low ISG expression had significantly worse clinical outcomes than those in the Mid for number of 28-day ventilator- and ICU-free days (P = 0.0006 and 0.0004), as well as 90-day mortality and 90-day home with unassisted breathing (P = 0.02 and 0.004). These findings suggest extremes of ISG expression by circulating neutrophils from ARDS patients recovered early in the syndrome are associated with poorer clinical outcomes.
Joint Proceedings of the Workshops On the Globalization of Modeling Languages ( GEMOC 2013 ) and Towards the Model Driven Organization ( AMINO 2013 )

The first edition of GEMOC workshop was co-located with the MODELS 2013 conference in Miami, FL, USA. The workshop provided an open forum for sharing experiences, problems and solutions related to the challenges of using of multiple modeling languages in the development of complex softwarebased systems. During the workshop, concrete language composition artifacts, approaches, and mechanisms were presented and discussed, ideas and opinions exchanged, and constructive feedback provided to authors of accepted papers. A major objective was to encourage collaborations and to start building a community that focused on providing solutions that support what we refer to as the globalization of domain-specific modeling languages, that is, support coordinated use of multiple languages throughout the development of complex systems. This report summarizes the presentations and discussions that took place in the first GEMOC 2013 workshop.
Effective Channel Gain Estimation in cellular wireless networks

Interferences in cellular wireless networks (CWN) between central station (CS) and the terminal station (TS) have a direct impact on available throughput and represents the main capacity limitation, especially in networks with full frequency reuse among all CSs. More and more attention is being focused on a way of minimizing interferences. We are introducing a new Coordinated Channel Gain Estimation (CCGE) method, which dynamically estimates the channel characteristics such as path loss, shadowing and fading, often also referred to as channel gain (CG) between each TS and CS on backhaul point-to-multipoint (PMP) OFDMA-based CWN. The method is usable for smart CSs synchronization where CSs run in the cooperative mode to avoid interferences. We performed five different simulations, with the results displaying extremely good CG estimation.
An empirical study of crude oil market

In this thesis I have tried to identify the risks and opportunities crude oil market offers. For this purpose I tested the performance of Univariate and Multivariate GARCH models. The first part of the work describes univariate GARCH models and their application to commodities markets. Physical ownership of the commodity carries an associated flow of services. The net flow of these services per unit of time is called ’convenience yield’. Since convenience yield appears as a factor which cannot be directly observed, the GARCH process was used for its modelling. The GARCH model has been found to provide a good fit for the convenience yield process. It paves the way for new term structure models of commodities prices and its empirical testing. The second part of my work uses multivariate GARCH models for hedging in oil market. For this reason, bivariate GARCH models have been estimated for cash and future prices. Taking into account the cointegration relation between these variables improves hedging performance. I test performance of cointegrated GARCH model in and out of sample and find it superior to simple bivariate GARCH model.
Tracking Articulated Objects Using Structure

This paper extends our previous work in rigid object tracking using a spring system to track articulated objects. The idea is to represent each part of an articulated object with a spring system and connect them via articula- tion points. Articulation points are detected automatically by observing the motion of the object and integrated into the spring systems of the rigid parts. Experiments with real and synthetic video sequences show promising results and im- provements over the rigid, previous approach. In a detailed discussion the parameters of the approach are analyzed and future plans are mentioned.
What Are They Thinking? Automated Analysis of Student Writing about Acid–Base Chemistry in Introductory Biology

Students’ writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid–base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses.
BlueCRM: A New Trend of Customer Relationship Management Systems

In this paper a new proactive customer relationship management system (CRM), BlueCRM, is proposed. The proposed system employs Bluetooth as an automatic identification token in addition to its common use as a communication channel. The system offers a low-cost proactive CRM system which is the future trend of such systems. The implementation of the prototype system comprises two sides: a database management program and an intuitive user interface in the software side. In the hardware side there are a Bluetooth-enabled device and a Bluetooth dongle. The architecture encloses an important advantage over the previous solutions due to its feasibility and ease of deployment. Furthermore, the architecture can be flexibly implemented either as a stand-alone or as integrated part of an existing information system.
Ensuring consistency file authentication over encrypted files in the cloud

Cloud security becomes the biggest research issue in the today's world where the cloud stored file content access need to be limited to only the authorized users and also it need to be protected from the cloud servers where the file contents are stored. In the existing method, a protocol is introduced which is based on the Deterministic Finite Automata (DFA) authentication which allows a clients to authenticate the server behaviours whether they are involved in any malicious activities are not. However this lacks from the file update, in which it will be complex to update the file contents which are modified and accessed by the clients concurrently. In this work, the fork consistency approach is integrated with the existing protocol to achieve a consistency property.
You, the people : the United Nations, transitional administration, and state-building

Introduction 1. Colonies and Occupied Territories: Transitional Administrations Through the Twentieth Century 2. Power and Change: The Evolution of United Nations Complex Peace Operations 3. Peace and Security: The Use of Force to Maintain Law and Order 4. Consultation and Accountability: Building Democracy Through Benevolent Autocracy 5. Justice and Reconciliation: The Rule of Law in Post-Conflict Territories 6. Relief and Reconstruction: The Politics of Humanitarian and Development Assistance 7. Elections and Exit Strategies: No Exit Without a Strategy, or No Strategy Without an Exit 8. 'You, the People': The Future of State-Building
Network overlays for efficient control of large scale dynamic groups

Scalable data distribution in large-scale dynamic collaborative systems requires efficient, low overhead communication control. We propose efficient algorithms for clustering network nodes dynamically based on their communication interest. Several group communication architectures have been proposed to date without considering the constraints imposed by the communication infrastructure. Among these, distributed hash tables are scalable and resilient data structures used for data dissemination control. However DHT's are not optimized for high dynamics of network node interest and real-time end-to-end performance requirements. This paper proposes efficient control algorithms for large-scale collaborative systems optimized for scalability as well as end-to-end data dissemination. Network node communication interest is modeled as a multi-dimensional attribute space partitioned into interest cells mapped to multicast communication groups. The proposed control algorithms use proximity-based clustering of network nodes and hierarchical communication interest aggregation. We show that network overlay control algorithms achieve scalability and low overhead with a controlled degradation of end-to-end data path performance
Neural Geometry for Constrained Optimization

Many engineering problems require the on-line solution of constrained optimization problems. This paper proposes, as an original contribution, particular neural architectures, called neural solids, whose interconnections represent the required constraints. The first neural solid presented here is the neural triangle whose learning law solves simple orthogonality problems. This solid is the basis for more complex architectures solving problems with orthogonality constraints: the paper presents and yields examples of the neural decomposer which can be used for computer vision applications and the EXIN NSVD neural network which yields the singular value decomposition of a given matrix. These neural networks are not only important because they give a fast solution of the optimization, but, above all, because they introduce a new technique which can be extended to more complex constrained optimization problems. Keywords— Backpropagation, Features, Hopfield networks, Image Matching, Neural Networks, Occlusion Map, Pattern Recognition, Self Organization, Structure from Motion.
Declining trust in growing China: A dilemma between growth and socio-economic damage

Declining general trust has become a serious social issue in China in recent years. This paper attempts to understand and analyze this social phenomenon from a social interaction perspective. Based on a repeated prisoners´ dilemma game on networks, it finds that the evolution of general trust is dependent on changes of the social interaction structure, and the increases of both social and spatial distance may explain a decrease of the levels of cooperation and general trust. In addition, we find that the traditional Chinese family and clan networks culture has an ambiguous effect on general trust, and simple reactive social "homing behavior" might be critical for China´s future economic development. In order to recover the general trust level, a major strategic option for China, and for fast growing countries in economic transition in general, is to (re-)develop appropriate network structures and properties, as our model indicates.