Miracle-dz/UniFAR-FTUs huggingface dataset:
MobiPag: Integrated Mobile Payment, Ticketing and Couponing Solution Based on NFC †

Mobile payments still remain essentially an emerging technology, seeking to fill the gap between the envisioned potential and widespread usage. In this paper, we present an integrated mobile service solution based on the near field communication (NFC) protocol that was developed under a research project called MobiPag. The most distinctive characteristic of Mobipag is its open architectural model that allows multiple partners to become part of the payment value-chain and create solutions that complement payments in many unexpected ways. We describe the Mobipag architecture and how it has been used to support a mobile payment trial. We identify a set of design lessons resulting from usage experiences associated with real-world payment situations with NFC-enabled mobile phones. Based on results from this trial, we identify a number of challenges and guidelines that may help to shape future versions of NFC-based payment systems. In particular, we highlight key challenges for the initial phases of payment deployments, where it is essential to focus on scenarios that can be identified as more feasible for early adoption. We also have identified a fundamental trade-off between the flexibility supported by the Mobipag solution and the respective implications for the payment process, particularly on the users' mental model.
A Method of Video Quality Assessment for CMMB Video Test Sequences

Since CMMB is an important application in wireless communication field -- its video quality plays a critical role for widely use. In assessment of video sequence, classic method often applies algebra method in making a compute model, such as PSNR, which often result in difficult for alignment of video sequence numbers and leads to complex computation problems. In this paper, it presents a novel method bases in high dimensional space. The method considers an image as a point in high dimensional space. It uses special kernels to covering some image points from original image, then a noised image is put into space, the method is performed to find the most similar one in high-dimensional space and gives its similarity. Experimental results show that the propose method make the measurement easily and meet the real noised image sequence. The proposed method is constructive and this work provides a way for video quality assessment.
EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition

The detection of epileptic seizures is of primary interest for the diagnosis of patients with epilepsy. Epileptic seizure is a phenomenon of rhythmicity discharge for either a focal area or the entire brain and this individual behavior usually lasts from seconds to minutes. The unpredictable and rare occurrences of epileptic seizures make the automated detection of them highly recommended especially in long term EEG recordings. The present work proposes an automated method to detect the epileptic seizures by using an unsupervised method based on k-means clustering end Ensemble Empirical Decomposition (EEMD). EEG segments are obtained from a publicly available dataset and are classified in two categories “seizure” and “non-seizure”. Using EEMD the Marginal Spectrum (MS) of each one of the EEG segments is calculated. The MS is then divided into equal intervals and the averages of these intervals are used as input features for k-Means clustering. The evaluation results are very promising indicating overall accuracy 98% and is comparable with other related studies. An advantage of this method that no training data are used due to the unsupervised nature of k-Means clustering.
Joint State Theorems for Public-Key Encryption and Digital Signature Functionalities with Local Computation

Composition theorems in simulation-based approaches allow to build complex protocols from sub-protocols in a modular way. However, as first pointed out and studied by Canetti and Rabin, this modular approach often leads to impractical implementations. For example, when using a functionality for digital signatures within a more complex protocol, parties have to generate new verification and signing keys for every session of the protocol. This motivates to generalize composition theorems to so-called joint state theorems, where different copies of a functionality may share some state, e.g., the same verification and signing keys. In this paper, we present a joint state theorem which is more general than the original theorem of Canetti and Rabin, for which several problems and limitations are pointed out. We apply our theorem to obtain joint state realizations for three functionalities: public-keyencryption, replayable public-key encryption, and digital signatures. Unlike most other formulations, our functionalities model that cipher texts and signatures are computed locally, rather than being provided by the adversary. To obtain the joint state realizations, the functionalities have to be designed carefully. Other formulations are shown to be unsuitable. Our work is based on a recently proposed, rigorous model for simulation-based security by K{\"u}sters, called the IITM model. Our definitions and results demonstrate the expressivity and simplicity of this model. For example, unlike Canetti's UC model, in the IITM model no explicit joint state operator needs to be defined and the joint state theorem follows immediately from the composition theorem in the IITM model.
Time and order estimation of paintings based on visual features and expert priors

Time and order are considered crucial information in the art domain, and subject of many research efforts by historians. In this paper, we present a framework for estimating the ordering and date information of paintings and drawings. We formulate this problem as the embedding into a one dimension manifold, which aims to place paintings far or close to each other according to a measure of similarity. Our formulation can be seen as a manifold learning algorithm, albeit properly adapted to deal with existing questions in the art community. To solve this problem, we propose an approach based in Laplacian Eigenmaps and a convex optimization formulation. Both methods are able to incorporate art expertise as priors to the estimation, in the form of constraints. Types of information include exact or approximate dating and partial orderings. We explore the use of soft penalty terms to allow for constraint violation to account for the fact that prior knowledge may contain small errors. Our problem is tested within the scope of the PrintART project, which aims to assist art historians in tracing Portuguese Tile art "Azulejos" back to the engravings that inspired them. Furthermore, we describe other possible applications where time information (and hence, this method) could be of use in art history, fake detection or curatorial treatment.
Development of Industrial SFF System Using a New Selective Dual-Laser Sintering Process

In order to develop more elaborate and speedy system for large objects than existing selective laser sintering (SLS), this study applies a new selective dual-laser sintering process. It contains a 3-axis dynamic focusing scanner system for scanning large area instead of the existing fθ lens. As sintering parameters, the sintering temperature, the laser beam power and the layer thickness have a great influence on sintering of the polymer and metal powder. This paper will address the development of a solid freeform fabrication (SFF) system employing the dual laser system. Experiments were performed to evaluate the effect of a scanning path and to fabricate the large-sized object.
Variance of Size in Regular Graph Tries

Graph tries are a generalization of classical digital trees: instead of being built from strings, a G-trie is built from label functions on the graph G. In this work, we determine leading order asymptotics for the variance of the size of a G-trie built on a memoryless source on a uniform alphabet distribution, where G is a member of a large class of infinite, M-regular directed, acyclic graphs with M > 1 fixed. In particular, this covers the cases of trees and grids. We find that, in such tries, the variance is of order Θ(nρ'), for some ρ' depending on G which is minimized when G is a tree. We also give an explicit expression for ρ' in the case where G is a grid, with M = 2.
Linear programming approach for online advertising

Online advertising has seen exponential growth since its inception over 15 years ago, resulting in 2013 for the first time ever to exceed broadcast television advertising revenues. This success has arisen largely from the transformation of the advertising industry from a lowtech, human intensive way of doing work (that were common place for much of the 20th century and the early days of online advertising) to highly optimised, mathematical, machine learning-centric processes that form the backbone of many current online advertising systems. Online advertising is a complex problem, especially from machine learning point of view. It contains multiple parties (advertisers, users, publishers and ad-networks), which interact with each other harmoniously but exhibit a conflict of interest when it comes to risk and revenue objectives. It is highly dynamic in terms of the rapid change of user information needs and the frequent modifications of ads campaigns. It is very large scale, with billions of keywords, tens of millions of ads, billions of users, millions of advertisers where events such as clicks and actions can be extremely rare. The goal of this paper is to overview the state of the art in online advertising and to propose a linear programming model for scheduling online ads. We tested the proposed system on the web site Time.mk and in this paper we present the results and improvements of the click-through rates (CTR) of the proposed approach.
Stress Relaxation of Cartilage Under Simple Shear and Compression: Experiments and Finite Element Analyses

Articular cartilage is a hydrated connective tissue consisting of a relatively small number of chondrocytes surrounded by a saturated extracellular matrix comprised mainly of type-II collagen fibrils and proteoglycans. As a deformable fluid saturated material, cartilage is most often modeled using biphasic or poroelastic theories [1,2]. The ultimate goal of this work is to evaluate biomechanical properties of native and tissue engineered cartilage under combined compression and shear. The purpose of this investigation was to determine stress and deformation fields in cartilage under compression and simple shear and relate these to measured results.Copyright © 2012 by ASME
PATTERNS OF SPECIATION IN DROSOPHILA

To investigate the time course of speciation, we gathered literature data on 119 pairs of closely related Drosophila species with known genetic distances, mating discrimination, strength of hybrid sterility and inviability, and geographic ranges. Because genetic distance is correlated with divergence time, these data provide a cross‐section of taxa at different stages of speciation.
Dielectric, electric and thermal properties of carboxylic functionalized multiwalled carbon nanotubes impregnated polydimethylsiloxane nanocomposite

The dielectric, electric and thermal properties of carboxylic functionalized multiwalled carbon nanotubes (F-MWCNT) incorporated into the polydimethylsiloxane (PDMS) were evaluated to determine their potential in the field of electronic materials. Carboxylic functionalization of the pristine multi walled carbon tubes (Ps-MWCNT) was confirmed through Fourier transform infrared spectroscopy, X-ray diffraction patterns for both Ps-MWCNTs and F-MWCNTs elaborated that crystalline behavior did not change with carboxylic moieties. Thermogravimetric and differential thermal analyses were performed to elucidate the thermal stability with increasing weight % addition of F-MWCNTs in the polymer matrix. Crystallization/glass transition / melting temperatures were evaluated using differential scanning calorimeter and it was observed that glass transition and crystallization temperatures were diminished while temperatures of first and second melting transitions were progressed with increasing F-MWCNT concentration in the PDMS matrix. Scanning electron microscopy and energy dispersive x-ray spectroscopy were carried out to confirm the morphology, functionalization, and uniform dispersion of F-MWCNTs in the polymer matrix. Electrical resistivity at temperature range (100–300°C), dielectric loss (tanδ) and dielectric parameters (ϵ/ ϵ//) were measured in the frequency range (1MHz–3GHz). The measured data simulate that the aforementioned properties were influenced by increasing filler contents in the polymer matrix because of the high polarization of conductive F-MWCNTs at the reinforcement/polymer interface.
BioNumbers—the database of key numbers in molecular and cell biology

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology—the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.
Master of Puppets: Analyzing And Attacking A Botnet For Fun And Profit

A botnet is a network of compromised machines (bots), under the control of an attacker. Many of these machines are infected without their owners' knowledge, and botnets are the driving force behind several misuses and criminal activities on the Internet (for example spam emails). Depending on its topology, a botnet can have zero or more command and control (C&C) servers, which are centralized machines controlled by the cybercriminal that issue commands and receive reports back from the co-opted bots. In this paper, we present a comprehensive analysis of the command and control infrastructure of one of the world's largest proprietary spamming botnets between 2007 and 2012: Cutwail/Pushdo. We identify the key functionalities needed by a spamming botnet to operate effectively. We then develop a number of attacks against the command and control logic of Cutwail that target those functionalities, and make the spamming operations of the botnet less effective. This analysis was made possible by having access to the source code of the C&C software, as well as setting up our own Cutwail C&C server, and by implementing a clone of the Cutwail bot. With the help of this tool, we were able to enumerate the number of bots currently registered with the C&C server, impersonate an existing bot to report false information to the C&C server, and manipulate spamming statistics of an arbitrary bot stored in the C&C database. Furthermore, we were able to make the control server inaccessible by conducting a distributed denial of service (DDoS) attack. Our results may be used by law enforcement and practitioners to develop better techniques to mitigate and cripple other botnets, since many of findings are generic and are due to the workflow of C&C communication in general.
Health and Quality of Life Outcomes

Background: This study assesses the validity and reliability of the Spanish version of DN4 questionnaire as a tool for differential diagnosis of pain syndromes associated to a neuropathic (NP) or somatic component (non-neuropathic pain, NNP). Methods: A study was conducted consisting of two phases: cultural adaptation into the Spanish language by means of conceptual equivalence, including forward and backward translations in duplicate and cognitive debriefing, and testing of psychometric properties in patients with NP (peripheral, central and mixed) and NNP. The analysis of psychometric properties included reliability (internal consistency, inter-rater agreement and test-retest reliability) and validity (ROC curve analysis, agreement with the reference diagnosis and determination of sensitivity, specificity, and positive and negative predictive values in different subsamples according to type of NP). Results: A sample of 164 subjects (99 women, 60.4%; age: 60.4 ± 16.0 years), 94 (57.3%) with NP (36 with peripheral, 32 with central, and 26 with mixed pain) and 70 with NNP was enrolled. The questionnaire was reliable [Cronbach's alpha coefficient: 0.71, inter-rater agreement coefficient: 0.80 (0.71–0.89), and test-retest intra-class correlation coefficient: 0.95 (0.92–0.97)] and valid for a cut-off value ≥ 4 points, which was the best value to discriminate between NP and NNP subjects. Discussion: This study, representing the first validation of the DN4 questionnaire into another language different than the original, not only supported its high discriminatory value for identification of neuropathic pain, but also provided supplemental psychometric validation (i.e. testretest reliability, influence of educational level and pain intensity) and showed its validity in mixed pain syndromes. Published: 4 December 2007 Health and Quality of Life Outcomes 2007, 5:66 doi:10.1186/1477-7525-5-66 Received: 17 September 2007 Accepted: 4 December 2007 This article is available from: http://www.hqlo. com/content/5/1/66 © 2007 Perez et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
High-efficiency coupling-insensitive transcutaneous power and data transmission via an inductive link

A new approach is presented for transmitting RF power and signal via an inductive link. Such an approach optimizes the power efficiency of the overall transmission scheme comprising the power amplifier plus the inductive link. Power amplification is based on the single ended class E concept. The power amplification stage is self-oscillating, and thus the oscillation frequency is influenced by the coupling of the coils. The resulting operating frequency offset yields improved power transmission performance of the circuit, since the oscillation frequency tracks the absolute transmission efficiency maximum. A detailed analysis is given. Realization of the approach requires a minimal number of circuit components. Experimental and theoretical results are in good agreement. <<ETX>>
Utilizing Automatic Predicate-Argument Analysis for Concept Map Mining

Concept maps can be used to provide concise and structured summaries of documents. Motivated by their usefulness in many application scenarios, several approaches have been suggested for concept map mining, the automatic extraction of concept maps from text. However, a major bottle-neck of previous work is the common pattern-based approach used to extract concepts and relations from documents which is either limited in coverage or requires a laborious deﬁnition of large sets of patterns. Drawing upon recent advances in automatic predicate-argument analysis, we propose to replace pattern-based extraction by using predicate-argument structures. Our experiments compare three different representations with previous work and show that using predicate-argument structures leads to a better extraction performance while being much easier to use.
Drive-by Disclosure: A Large-Scale Detector of Drive-by Downloads Based on Latent Behavior Prediction

Drive-by downloads continue to be the basis for many kinds of large-scale web attacks. The detection of Drive-by downloads and heap spraying attacks has been receiving serious research attention. The appearance of complex obfuscation patterns make the two primary challenges preventing the development of large-scale, real-time detectors of drive-by downloads become contradictory. On one hand, fabrication of disguised transformations (massively and heavily obfuscated scripts) thwarts capabilities of static analysis. On the other hand, dynamic analysis incurs excessive overhead along with other limitations. To ameliorate this situation, we propose Drive-by Disclosure, a novel complementary solution to bridge the gap between dynamic and static approaches. Drive-by Disclosure leverages availability of AST representation to predict script's latent behaviors statically. This approach facilitates distinction between scripting practices of drive-by downloads and disguised transformations. Subsequently, in order to reliably detect drive-by downloads, dynamic analysis will only be applied to the scripts that are identified as disguised. Compared to the state-of-the-art solutions, Drive-by Disclosure minimizes analysis overhead of JSAND to less than 24%. Also, it improves JSAND's detection rate by more than 29 absolute percentage points. Further, the combination of JSAND and Drive-by Disclosure attains two times better accuracy than Cujo.
Clustering Music Recordings Based on Genres

Existing systems for automatic genre classification follows a supervised framework that extracts genre-specific information from manually-labeled music data and then identifies unknown music data. However, such systems may not be suitable for personal music management, because manually labeling music based on individually-defined genres can be labor intensive and subject to inconsistence from time to time. This work studies an unsupervised paradigm for music genre classification. It is aimed to partition a collection of unknown music recordings into several clusters such that each cluster contains recordings in only one genre, and different clusters represent different genres. This enables users to organize their personal music database without needing specific knowledge about genre. We investigate how to measure the genre similarities between music recordings and estimate the genre population size of a music collection. Our experiment results show the feasibility of clustering music recordings by genre.
RefaFlex: safer refactorings for reflective Java programs

If programs access types and members through reflection, refactoring tools cannot guarantee that refactorings on those programs are behavior preserving. Refactoring approaches for highly reflective languages like Smalltalk therefore check behavior preservation using regression testing. In this paper we propose RefaFlex, a novel and more defensive approach towards the refactoring of reflective (Java) programs. RefaFlex uses a dynamic program analysis to log reflective calls during test runs and then uses this information to proactively prevent the programmer from executing refactorings that could otherwise alter the program's behavior. This makes re-running test cases obsolete: when a refactoring is permitted, tests passing originally are guaranteed to pass for the refactored program as well. In some cases, we further re-write reflective calls, permitting refactorings that would otherwise have to be rejected. We have implemented RefaFlex as an open source Eclipse plugin and offer extensions for six Eclipse refactoring tools addressing naming, typing, and accessibility issues. Our evaluation with 21,524 refactoring runs on three open source programs shows that our tool successfully prevents 1,358 non-behaviour-preserving refactorings which the plain Eclipse refactorings would have incorrectly permitted.