Miracle-dz/UniFAR-FTUs huggingface dataset:
Twister: the development of a peer-to-peer microblogging platform

This paper proposes a new microblogging architecture based on peer-to-peer networks overlays. The proposed platform comprises three mostly independent overlay networks. The first provides distributed user registration and authentication and is based on the Bitcoin protocol. The second one is a Distributed Hash Table (DHT) overlay network providing key/value storage for user resources and tracker location for the third network. The last network is a collection of possibly disjoint ‘swarms’ of followers, based on the BitTorrent protocol, which can be used for efficient near-instant notification delivery to many users. By leveraging existing and proven technologies, twister provides a new microblogging platform offering security, scalability and privacy features. A built-in mechanism provides incentive for entities that contribute processing time to run the user registration network, rewarding such entities with the privilege of sending a single unsolicited (‘promoted’) message to the entire network. The number of unsolicited messages per day is defined in order to not upset users. Security and scalability assumptions have been confirmed in a real-world deployment.
A New Signal Detection Method for Spatially Multiplexed MIMO Systems and Its VLSI Implementation

This brief proposes a new signal detection method called QR ordered successive interference cancellation (OSIC) with candidates (QOC) method for spatially multiplexed multiple-input-multiple-output (MIMO) systems. By using the OSIC algorithm and the maximum-likelihood (ML) metric, the proposed method achieves near-ML performance without requiring a large number of candidates. Although the proposed method can be used for both hard and soft decoding systems, it is particularly useful for soft decoding systems since the log-likelihood-ratio (LLR) values for all the bits can efficiently be computed without using LLR estimation. The proposed method is also suitable for very large scale integration (VLSI) implementation since it leads to a fixed throughput system. A QOC detector for a 4 times4 16-quadrature-amplitude-modulation (16-QAM) MIMO system has been designed and synthesized with a 0.13-mum complimentary metal-oxide-semiconductor (CMOS) technology. The implementation results show that the proposed detector reduces the hardware complexity by 73% relative to a K-best detector.
A Single Analog-to-Digital Converter That Converts Two Separate Channels (I and Q) in a Broadband Radio Receiver

An analog-to-digital converter (ADC) architecture that simultaneously converts two channels is presented. The ADC is intended for use in portable broadband radio receivers that employ in-phase (I) and quadrature (Q) signal paths and will provide an optimal combination of low cost, low power, and high performance. The architecture is pipeline based and employs two separate first stages followed by shared stages for the remainder of the pipeline. A clock generation system for generating all of the required nonoverlapping clock phases is also presented. A prototype ADC with 10 bit resolution and a 40 MHz sample rate that employs the proposed ADC architecture has been fabricated using a 90 nm all-digital CMOS process and occupies an area of 1.727 mm2 for a per-channel area of 0.864 mm2. The measured performance for the two-channel ADC is a peak signal-to-noise ratio (SNR) and signal-to-noise-plus-distortion ratio (SNDR) of 58.4 dB and 56.5 dB, respectively, and differential nonlinearity (DNL) and integral nonlinearity (INL) of -0.48/+0.58 LSB and plusmn1 LSB, respectively, with a power dissipation of 50 mW (including analog, digital, and clock generator power) from a 2.5 V supply (1.2 V for the digital section), giving a per-channel power dissipation of 25 mW.
Holistic Approach for Multimodal Lecture Video Retrieval

Many organizations and universities provide distance learning by recording classroom lectures and making them available to students over the Internet. A repository generally contains hundreds of such lecture videos. Each lecture video is typically a more than hour’s duration and is often huge. It is sometimes clumsy for students to search through an entire video, or across many videos, in order to find portions of their immediate interest. It is desirable to have a system that takes user-given keywords as a query and provides a results of relevant lecture videos and highlight the specific part within the video where that concept is explained. The lecture recordings having great variety of media or modalities such as video, audio, lecture media, presentation behavior and formats. This paper propose a technique to automatically generate text metadata and tags for Multimodal lecture videos. This is based on generating text transcripts automatically using a text recognition engine and automatic indexing and search of the transcripts. Proposed system takes keywords from users as a query and returns a list of videos which provide a link to video and concept of video using timestamps as the results. KeywordsContent based Video Retrieval, Multimodel Recordings, Video Segmentation, Optical Character Recougnision
Electrostatic Fiber Spreader.

Abstract : A fiber spreader is provided which reduces fiber damage that occurs during spreading. The fiber spreader includes a thin electrically grounded support sheet for supporting a layer of fibers on one surface thereof. The fibers supported on the support sheet are guided by a plurality of guide rollers past at least one corona discharge region at which the fibers are spread substantially uniformly. A motor driven spool advances the support sheet past the corona discharge region. The fibers are attached to the support sheet by an electrostatic attraction. The guide rollers are positioned so as to provide at least one region in which the sheet and the fibers are physically separated to allow for the uniform spreading of the fibers. After the fibers are spread the fibers and the support sheet are united again and collected on a motor-driven spool.
EVALUATION OF INDUCTIVE VOLTAGE DIVIDERS AND 10 nF CAPACITORS IN A CAPACITANCE CALIBRATION METHOD

Researchers within the Quantum Electrical Metrology Division at the National Institute of Standards and Technology (NIST) have implemented a calibration procedure for four-terminal-pair capacitance standards from 0.01 μF to 100 μF [1], [2]. This method depends on the accurate characterization of a 1 nF capacitor and an Inductive Voltage Divider (IVD) 1:10 ratio from 100 Hz to 100 kHz. This paper discusses a procedure that evaluates the IVD ratio and offers an enhancement over the current method. Also the evaluation of the 10 nF capacitor is presented and a measurement procedure modification is suggested that significantly improves the result.
Motion Estimation in Ultrasound Images Using Time Domain Cross Correlation With Prior Estimates

In this paper we introduce a new speckle tracking method that is based on the standard time-domain cross correlation strain estimation (TDE). We call this method time-domain cross-correlation with prior estimates (TDPE), because it uses prior displacement estimates of neighboring windows to speed up computation. TDPE has all the advantages of TDE, but is much faster. Simulations, as well as experiments with phantoms and tissue, indicate that TDPE is capable of reliably estimating tissue displacement and strain over a large range of displacements in real time. The computational efficiency of TDPE is compared with current time-efficient methods that have been used in real time strain imaging systems. The results show that TDPE is the most time efficient algorithm to date, and is roughly 10 times faster than the TDE. The implementation of TDPE on an Ultrasonix RP500 ultrasound machine runs at 30 fps for strain images of 16 000 pixels
Numerical simulations of the flow of wood polypropylene composites with wall slipping in a profile die: The significance of material data

This paper demonstrates the importance of the careful selection of the input material data for the calculation of the flow behavior of wood polypropylene composites. To this goal, the rheological data of un-dried samples were measured, utilizing a commercial high pressure capillary rheometer equipped with slit dies of different gap heights. The data were incorporated in finite-difference and finite-element methods in order to predict the pressure drop along a profile die and compared to measurements on an extruder at different flow rates.While the un-dried sample is expected to undergo wall slippage during flow, the simulation results on the slit and profile dies indicate that neither a fully slipping plug flow nor a fully adhering shear flow is capable of providing reasonable results. By utilizing ANSYS Polyflow software, a combination of both flow types was incorporated in a 3-dimensional FEM analysis to simulate the pressure drops. The results show that by using the shear viscosity data from capillary .
Identity management framework for cloud based internet of things

Internet of Things is emerging as next generation technology with a vision of a connected world where everything is connected whether it is a person, a thing or a device. The connected things are able to exchange data including their identities, physical properties and information gathered from the environment. Hence they actively participate in decision making. The identification technologies like RFID have empowered the concept of Internet of Things by enabling the unique identification of things. The cloud computing technology has made the tasks of processing huge amount of data produced by the devices easier. But in order to make the system scalable, it must be able to handle the devices that are growing day by day. Hence there is a need of proper identity management. This paper discusses requirement of identity management and then presents a framework for identity management for Cloud based Internet of Things.
Gaussian processes

In these notes, we will talk about a different flavor of learning algorithms, known as Bayesian methods. Unlike classical learning algorithm, Bayesian algorithms do not attempt to identify “best-fit” models of the data (or similarly, make “best guess” predictions for new test inputs). Instead, they compute a posterior distribution over models (or similarly, compute posterior predictive distributions for new test inputs). These distributions provide a useful way to quantify our uncertainty in model estimates, and to exploit our knowledge of this uncertainty in order to make more robust predictions on new test points. We focus on regression problems, where the goal is to learn a mapping from some input space X = R of n-dimensional vectors to an output space Y = R of real-valued targets. In particular, we will talk about a kernel-based fully Bayesian regression algorithm, known as Gaussian process regression. The material covered in these notes draws heavily on many different topics that we discussed previously in class (namely, the probabilistic interpretation of linear regression, Bayesian methods, kernels, and properties of multivariate Gaussians). The organization of these notes is as follows. In Section 1, we provide a brief review of multivariate Gaussian distributions and their properties. In Section 2, we briefly review Bayesian methods in the context of probabilistic linear regression. The central ideas underlying Gaussian processes are presented in Section 3, and we derive the full Gaussian process regression model in Section 4.
Stable Matching with Evolving Preferences

We consider the problem of stable matching with dynamic preference lists. At each time step, the preference list of some player may change by swapping random adjacent members. The goal of a central agency (algorithm) is to maintain an approximately stable matching (in terms of number of blocking pairs) at all times. The changes in the preference lists are not reported to the algorithm, but must instead be probed explicitly by the algorithm. We design an algorithm that in expectation and with high probability maintains a matching that has at most $O((log (n))^2)$ blocking pairs.
Quality Evaluation of Olives, Olive Pomace and Olive Oil by Infrared Spectroscopy

Olive oil extraction starts by crushing olives and ends by obtaining olive oil, vegetative water and partially de-oiled olive pomace (Petrakis 2006; Di Giovacchino 2000). In the industry it is important to know the amount of oil and water present in both olive fruits and olive pomace. In fact, the amount of oil is the parameter that establishes the price of raw materials and by-products and is critical for the optimization of extraction procedures.
Suppressor mutations in the Glutamine Dumper1 protein dissociate disturbance in amino acid transport from other characteristics of the Gdu1D phenotype

Intracellular amino acid transport across plant membranes is critical for metabolic pathways which are often split between different organelles. In addition, transport of amino acids across the plasma membrane enables the distribution of organic nitrogen through the saps between leaves and developing organs. Amino acid importers have been studied for more than two decades, and their role in this process is well-documented. While equally important, amino acid exporters are not well-characterized. The over-expression of GDU1, encoding a small membrane protein with one transmembrane domain, leads to enhancement of amino acid export by Arabidopsis cells, glutamine secretion at the leaf margin, early senescence and size reduction of the plant, possibly caused by the stimulation of amino acid exporter(s). Previous work reported the identification of suppressor mutations of the GDU1 over-expression phenotype, which affected the GDU1 and LOG2 genes, the latter encoding a membrane-bound ubiquitin ligase interacting with GDU1. The present study focuses on the characterization of three additional suppressor mutations affecting GDU1. Size, phenotype, glutamine transport and amino acid tolerance were recorded for recapitulation plants and over-expressors of mutagenized GDU1 proteins. Unexpectedly, the over-expression of most mutated GDU1 led to plants with enhanced amino acid export, but failing to display secretion of glutamine and size reduction. The results show that the various effects triggered by GDU1 over-expression can be dissociated from one another by mutagenizing specific residues. The fact that these residues are not necessarily conserved suggests that the diverse biochemical properties of the GDU1 protein are not only born by the characterized transmembrane and VIMAG domains. These data provide a better understanding of the structure/function relationships of GDU1 and may enable modifying amino acid export in plants without detrimental effects on plant fitness.
Biodegradable Fibers of Poly‐L, DL‐lactide 70/30 Produced by Melt Spinning

Melt spinning of poly-L,DL-lactide 70/30 has been studied. Fiber having diameter lower than 120 micron exhibited tensile modulus and strength in the range of 3-4 GPa and 130-180 MPa, respectively. Maximum attainable modulus and strength of 4.7 GPa and 205 MPa were predicted, according to a proposed equation in dependence on the draw ratio. In vitro degradation performed in PBS solution at 37 °C, showed that after 4 weeks fibers maintained adequate properties for tissue engineering applications.
A Plurality of Sparse Representations Is Better Than the Sparsest One Alone

Cleaning of noise from signals is a classical and long-studied problem in signal processing. Algorithms for this task necessarily rely on an a priori knowledge about the signal characteristics, along with information about the noise properties. For signals that admit sparse representations over a known dictionary, a commonly used denoising technique is to seek the sparsest representation that synthesizes a signal close enough to the corrupted one. As this problem is too complex in general, approximation methods, such as greedy pursuit algorithms, are often employed. In this line of reasoning, we are led to believe that detection of the sparsest representation is key in the success of the denoising goal. Does this mean that other competitive and slightly inferior sparse representations are meaningless? Suppose we are served with a group of competing sparse representations, each claiming to explain the signal differently. Can those be fused somehow to lead to a better result? Surprisingly, the answer to this question is positive; merging these representations can form a more accurate (in the mean-squared-error (MSE) sense), yet dense, estimate of the original signal even when the latter is known to be sparse. In this paper, we demonstrate this behavior, propose a practical way to generate such a collection of representations by randomizing the Orthogonal Matching Pursuit (OMP) algorithm, and produce a clear analytical justification for the superiority of the associated Randomized OMP (RandOMP) algorithm. We show that while the maximum a posteriori probability (MAP) estimator aims to find and use the sparsest representation, the minimum mean- squared-error (MMSE) estimator leads to a fusion of representations to form its result. Thus, working with an appropriate mixture of candidate representations, we are surpassing the MAP and tending towards the MMSE estimate, and thereby getting a far more accurate estimation in terms of the expected lscr2 -norm error.
A Document-Oriented Paradigm for the Construction of Content-Intensive Applications

In this paper we describe our work on the formulation of a document-oriented paradigm for improving the construction and maintenance of content-intensive applications (i.e. applications that make intensive use of the information provided by the experts in a given domain: the contents). According to this paradigm, the development of a content-intensive application must be the result of close collaboration between two kinds of actors: domain experts and developers. The goal of this collaboration is the authoring of (i) a set of documents describing the most relevant aspects of the application (i.e. the contents and other relevant customizable features); (ii) a grammar describing a domain-specific markup language that will be used to make the structure and the data in these documents explicit and (iii) a suitable processor for this language. The final running application will be automatically produced by processing the marked documents with this processor. The use of this paradigm in the development of content-intensive applications can increase the initial cost of application production, but in the long run it can substantially improve maintenance and portability, and promote information and software reuse as well. We have successfully applied this paradigm to the development of educational and hypermedia applications, and knowledge-based systems. From these experiences, we have found that the feasibility of the paradigm depends to a great extent on having mechanisms that enable the incremental definition of the markup languages and the incremental construction of their processors. This has led us to the formulation of a document-oriented approach for the development of content-intensive applications tightly coupled with these principles of incremental formulation and operationalization of domain-specific markup languages.
Detection of traffic signs using posterior classifier combination

Mobile mapping of environment information from a moving platform plays an important role in the automatic acquisition of GIS (Geographic Information Systems). The extraction of railway infrastructure from video frames captured on a driving train requires a robust visual object detection system that provides both high localization accuracy and the capability to cope with uncertain information. This work presents a radial basis functions (RBF) neural network that models appearance based object recognition of traffic lights and railway signs within a probabilistic framework. A comparison of different classifier combination strategies demonstrates that a classifier prioritization scheme based on an information theoretic selection criterion provides the best recognition performance.
Rationalizable Strategic Behavior and the Problem of Perfection

This paper explores the fundamental problem of what can be inferred about the outcome of a noncooperative game, from the rationality of the players and from the information they possess. The answer is summarized in a solution concept called rationalizability. Strategy profiles that are rationalizable are not always Nash equilibria; conversely, the information in an extensive form game often allows certain "unreasonable" Nash equilibria to be excluded from the set of rationalizable profiles. A stronger form of rationalizability is appropriate if players are known to be not merely "rational" but also "cautious. " "WHAT CONSTITUTES RATIONAL BEHAVIOR in a noncooperative strategic situation? " This paper explores the issue in the context of a wide class of finite noncooperative games in extensive form. The traditional answer relies heavily upon the idea of Nash equilibrium (Nash [17]). The position developed here, however, is that as a criterion for judging a profile of strategies to be "reasonable" choices for players in a game, the Nash equilibrium property is neither necessary nor sufficient. Some Nash equilibria are intuitively unreasonable, and not all reasonable strategy profiles are Nash equilibria. The fact that a Nash equilibrium can be intuitively unattractive is well-known: the equilibrium may be "imperfect. " Introduced into the literature by Selten [20], the idea of imperfect equilibria has prompted game theorists to search for a narrower definition of equilibrium. While this research, some of which will be discussed here, has been extremely instructive, it remains inconclusive. Theorists often agree about what should happen in particular games, but to capture this intuition in a general solution concept has proved to be very difficult. If this paper is successful it should make some progress in that direction. The other side of the coin has received less scrutiny. Can all non-Nash profiles really be excluded on logical grounds? I believe not. The standard justifications for considering only Nash profiles are circular in nature, or make gratuitous assumptions about players' decision criteria or beliefs. The following discussion of these points is extremely brief, due to space constraints; more detailed arguments may be found in Pearce [18].
A Variable Cluster of Ethylene Response Factor–Like Genes Regulates Metabolic and Developmental Acclimation Responses to Submergence in Rice[W]

Submergence-1 (Sub1), a major quantitative trait locus affecting tolerance to complete submergence in lowland rice (Oryza sativa), contains two or three ethylene response factor (ERF)–like genes whose transcripts are regulated by submergence. In the submergence-intolerant japonica cultivar M202, this locus encodes two ERF genes, Sub1B and Sub1C. In the tolerant near-isogenic line containing the Sub1 locus from the indica FR13A, M202(Sub1), the locus additionally encodes the ERF gene Sub1A. During submergence, the tolerant M202(Sub1) displayed restrained leaf and internode elongation, chlorophyll degradation, and carbohydrate consumption, whereas the enzymatic activities of pyruvate decarboxylase and alcohol dehydrogenase were increased significantly compared with the intolerant M202. Transcript levels of genes associated with carbohydrate consumption, ethanolic fermentation, and cell expansion were distinctly regulated in the two lines. Sub1A and Sub1C transcript levels were shown to be upregulated by submergence and ethylene, with the Sub1C allele in M202 also upregulated by treatment with gibberellic acid (GA). These findings demonstrate that the Sub1 region haplotype determines ethylene- and GA-mediated metabolic and developmental responses to submergence through differential expression of Sub1A and Sub1C. Submergence tolerance in lowland rice is conferred by a specific allele variant of Sub1A that dampens ethylene production and GA responsiveness, causing quiescence in growth that correlates with the capacity for regrowth upon desubmergence.