Miracle-dz/UniFAR-FTUs huggingface dataset:
New Features Acquisition of Text with Cloud-LDA Model

This paper probes into how to improve Information Retrieval by changing the feature distribution of the text. It introduces Cloud Model theory into Latent Dirichlet Allocation(LDA) Model and build a new feature selection system. LDA Model is used to mine the underlying topical structure. Each topic is associated with a multinomial distribution over words which are semantic related. But there is doubt that themes are relevant with each other in the light of semantics. Based on LDA model presented probability distribution of vocabulary in text, the new system with Cloud Model theory can automatically simulate feature set whose contribution degree is high in the text. Results show this feature set has less features but higher classification accuracy, thus obviously better than currently popular feature selection methods. If the query is matched to words with high contribution degree, the more these words are, the more relevant the article searched out is with the query. NTCIR-5 (the 5th NII Test Collection for IR Systems) collections of Experiment on SLIR (Single Language IR) show that this method achieves an obvious improvement compared with some other methods in IR.
A differential reference voltage source and its output buffer used in high-speed high-precision pipelined ADC

An analysis of performance degradation due to limited precision of reference voltage for pipelined ADC (Analog to Digital Converter) is presented in this paper. For the MDAC (multiplying D/A converter) and subADC in pipelined ADC have very different requirements for the precision of reference voltage, an improved differential reference voltage source and its buffer circuit is proposed. The reference voltage for MDAC and subADC are provided and designed the output buffer separately, so that to reduce the crosstalk between MDAC and subADC. Also, the driving ability of the proposed circuit is controllable with the help of programmable bias circuit which regulates the circuit's entire power consumption. Spectre post-layout simulation results show that the reference source has the minimum power of 15mW, and the entire set-up time is 5.842ns; has the maximum power of 58mW, and the entire set-up time is 1.036ns. The reference source can meet the requirement of 14-bit 80MSPS to 450MSPS pipelined ADC.
Studying Object Naming with Online Photos and Caption

We explore what names people use to describe visual concepts and why these names are chosen. Choosing object names has been a topic of interest in cognitive psychology, but a systematic, data-driven approach for naming at the scale of thousands of objects does not yet exist. First, we find that visual context has interpretable effects on visual naming, by analysing the MSCOCO dataset that has manually annotated objects and captions containing the natural language names for the object. We show that taking into account other objects as context helps improve the prediction of object names. We then analyse the naming patterns on a large dataset from Flickr, using automatically detected concepts. Preliminary results indicate that naming patterns can be identified on a large scale, but contrary to the conventional wisdom in cognitive psychology, are not dominated by genus for animals. We further validate the automatic method with a pilot Amazon Mechanical Turk naming experiment, and explore the impact of automatic concept detectors with t-SNE visualizations.
Single case research designs

ingle-case research designs are a diverse and powerful set of procedures useful for demonstrating causal relations among clinical phenomena. Although such designs are flexible, efficient, and have been used to make key advances since the earliest days of psychological science, they are currently extremely underused by psychological scientists and clinicians. We review the historical and theoretical foundations of single-case research designs, including case studies, quasi-experimental designs, and experimental designs used in single-case or small-group studies. We also summarize the methodological requirements, primary design options, and challenges and limitations of each approach. Recent examples of each design are provided to illustrate the usefulness of these valuable—albeit somewhat forsaken—research methods for scientific and clinical advancement.
Objective evaluation of speech enhancement using compressive sensing algorithm

Most of the accurate method for the speech enhancement design mainly focuses on quality and intelligibility to produce high performance level by using compression techniques. A novel speech enhancement algorithm using compressive sensing (CS) is different paradigm from compression technique with low-dimensional geometry for transmission or storage. The CS algorithm, can directly acquire compressed data signals and replace samples by more general measurements of the uniform rate digitization with signal sparsity model. Perceptual evaluation of speech quality (PESQ) is an objective evaluation of speech enhancement algorithm used to measure the enhanced speech quality. All provable good measurement, with random matrics in CS algorithm, can enhance speech signal. Objective evaluation on various dB SNR shows that the proposed algorithm exhibits better noise reduction ability over conventional approaches without obvious degradation of the speech signal quality.
Real-time dense appearance-based SLAM for RGB-D sensors

In this work a direct dense approach is proposed for real-time RGB-D localisation and tracking. The direct RDB-D localisation approach is demonstrated on a low cost sensor which exploits projective IR light within indoor environments. This type of device has recently been the object of much interest and one advantage is that it provides dense 3D environment maps in real-time via embedded computation. To date all existing tracking approaches using these sensors have been based on a sparse set of geometric features which have to be detected and matched across images in time. These techniques have a downfall in that errors made in matching and extraction cannot be recovered through a purely geometric optimization model. Here a direct 3D tracking approach is proposed such that an error is based directly on the intensity of pixels so that no feature extraction and matching are required. Original work in this direction has been carried out on direct dense tracking for stereo cameras and it is shown here how this approach is ideal for the general class of RGB-D cameras. Some advantages of an IR depth sensor include more accurate maps in untextured environments, however, the sensor is limited to indoor environments and close ranges. The system is made robust via statistical M-estimation along with a saliency selection criterion which favors more interesting pixels. Experimental validation is provided which compares the proposed RGB-D localisation and mapping system with ground truth laser odometry within complex indoor environments.
How BLAST Works : The Basics

The comparison of nucleotide or protein sequences from the same or different organisms is a very powerful tool in molecular biology. By finding similarities between sequences, scientists can infer the function of newly sequenced genes, predict new members of gene families, and explore evolutionary relationships. Now that whole genomes are being sequenced, sequence similarity searching can be used to predict the location and function of protein-coding and transcriptionregulation regions in genomic DNA. Basic Local Alignment Search Tool (BLAST) (1, 2) is the tool most frequently used for calculating sequence similarity. BLAST comes in variations for use with different query sequences against different databases. All BLAST applications, as well as information on which BLAST program to use and other help documentation, are listed on the BLAST homepage. This chapter will focus more on how BLAST works, its output, and how both the output and program itself can be further manipulated or customized, rather than on how to use BLAST or interpret BLAST results.
Multi-patch convolution neural network for iris liveness detection

Attacking iris systems with fake iris patterns has become the largest security risk of iris recognition systems. Therefore iris liveness detection which discriminate genuine or fake iris images is of significant importance to iris recognition systems. However, the state-of-the-art algorithms mainly rely on hand-crafted texture features which can only identify fake iris images with single pattern. This paper proposes a Multi-patch Convolution Neural Network (MCNN) that is capable of handling different types of fake iris images. MCNN directly learns the mapping function between raw pixels of the input iris patch and the labels. The outputs of each patch are fed into a decision layer which determines the final decision. Our proposed algorithm automatically learns the features to detect hybrid pattern of fake iris images rather than handcraft. The decision layer helps to improve the robustness and accuracy for iris liveness detection. Experimental results demonstrate an extremely higher accuracy of iris liveness detection than other state-of-the-art algorithms. The proposed MCNN remarkably achieve the best results with nearly 100% accuracy on ND-Contact and CAISA-Iris-Fake datasets.
A Novel Architecture for Scalable, High Throughput, Multi-standard LDPC Decoder

This paper presents a bottom up approach for implementing high throughput, scalable, layered LDPC decoding architecture for multi-standard applications. A generic implementation of fully parallel check node along with a block level Channel Memory organization scheme are two elements of novelty of this work. The proposed decoder IP core is synthesizable for all codes defined by WiMAX (WiFi) standards. Synthesis results are presented based on 130 nm standard cell ASIC technology.
Using objected‐oriented and UML tools for hardware design: A case study

This paper argues for the adoption of object‐oriented design and UML tools for nonsoftware designs, i.e., systems, hardware and algorithms: This is a controversial position. It presents a case study, the design of a heating, ventilation, and air conditioning system, using UML tools. This case study also shows the incremental elaboration used to progress from the requirements model, to the analysis model, to the design model, etc. The paper finally discusses some difficulties that must be overcome in order to apply UML tools to system designs. © 2002 Wiley Periodicals, Inc. Syst Eng 6: 28–48, 2003.
Control-flow refinement and progress invariants for bound analysis

Symbolic complexity bounds help programmers understand the performance characteristics of their implementations. Existing work provides techniques for statically determining bounds of procedures with simple control-flow. However, procedures with nested loops or multiple paths through a single loop are challenging. In this paper we describe two techniques, control-flow refinement and progress invariants, that together enable estimation of precise bounds for procedures with nested and multi-path loops. Control-flow refinement transforms a multi-path loop into a semantically equivalent code fragment with simpler loops by making the structure of path interleaving explicit. We show that this enables non-disjunctive invariant generation tools to find a bound on many procedures for which previous techniques were unable to prove termination. Progress invariants characterize relationships between consecutive states that can arise at a program location. We further present an algorithm that uses progress invariants to compute precise bounds for nested loops. The utility of these two techniques goes beyond our application to symbolic bound analysis. In particular, we discuss applications of control-flow refinement to proving safety properties that otherwise require disjunctive invariants. We have applied our methodology to over 670,000 lines of code of a significant Microsoft product and were able to find symbolic bounds for 90% of the loops. We are not aware of any other published results that report experiences running a bound analysis on a real code-base.
Time Consistent Policy in Markov Switching Models

In this paper we consider the quadratic optimal control problem with regime shifts and forward-looking agents. This extends the results of Zampolli (2003) who considered models without forward-looking expectations. Two algorithms are presented: The first algorithm computes the solution of a rational expectation model with random parameters or regime shifts. The second algorithm computes the time-consistent policy and the resulting Nash-Stackelberg equilibrium. The formulation of the problem is of general form and allows for model uncertainty and incorporation of policymakerâ€™s judgement. We apply these methods to compute the optimal (non-linear) monetary policy in a small open economy subject to (symmetric or asymmetric) risks of change in some of its key parameters such as inflation inertia, degree of exchange rate pass-through, elasticity of aggregate demand to interest rate, etc. We normally find that the time-consistent response to risk is more cautious. Furthermore, the optimal response is in some cases non-monotonic as a function of uncertainty. We also simulate the model under assumptions that the policymaker and the private sector hold the same beliefs over the probabilities of the structural change and different beliefs (as well as different assumptions about the knowledge of each otherâ€™s reaction function).
Particle-Based Non-Newtonian Fluid Animation with Heating Effects

In this paper, we propose a new particle-based model for non-Newtonian fluid animation with heating effects. The new model has three contributions. The first contribution is a new particle dynamics method, which is more accurate in animating rotational non-Newtonian fluid motions than previous non-Newtonian fluid models in computer graphics. In addition, the particle dynamics method includes a new Smooth Particle Hydrodynamics (SPH)-based projection method to enforce fluid incompressibility. The second contribution is a new particle resampling method. It is observed that fluid deformations may cause poor particle distribution which in turn causes inaccurate fluid modeling. To address this problem, in our particle re-sampling method, particles in our model are down-sampled and then up-sampled such that a well distribution of particles is attained. The third contribution is a new SPH-based heat transfer method for animating non-Newtonian fluids, whereas heating effects have been animated in previous graphical models for elastic and plasto-elastic materials as well as Newtonian fluids. In the end, many animations are produced with the proposed model to demonstrate the contributions of the new approach.
An IDS for Web Applications

This work presents a WEB-IDS that combine both anomaly and misuse detection approach. This mixed solution is really interesting because merges the two complementary methods used to recognize attacks; we solved the usual conflicts presented by this choice and obtained an higher results accuracy. Our tool starts with the misuse-based module and its results are passed to the anomaly detection module: in this way the system has an high reactivity, less false negatives, it is simplier to solve conflicts between the two modules and the anomaly based module do not need to process dangerous events recognised by the first module. Our system does not need any specific setting, but only a training period. There are also different auto-setting tresholds for the different resources that reduce false alarms. The system is implemented as system service and tested with a real dataset by a services company.
Chapter 12 MULTI-START METHODS

Heuristic search procedures that aspire to find global optimal solutions to hard combinatorial optimization problems usually require some type of diversification to overcome local optimality. One way to achieve diversification is to re-start the procedure from a new solution once a region has been explored. In this chapter we describe the best known multi-start methods for solving optimization problems. We propose classifying these methods in terms of their use of randomization, memory and degree of rebuild. We also present a computational comparison of these methods on solving the linear ordering problem in terms of solution quality and diversification power.
The First Stage in Two-Stage Template Matching

This paper formulates the problem encountered in the first stage of two-stage, binary template matching as a set of hypotheses to be tested, including a hypothesis of ``no object. '' Two new statistics R and G are proposed, based on a likelihood ratio, and are compared to the sum of absolute differences and a correlation measure by analytical approximations and Monte Carlo experiments. Statistical power and a measure of sensitivity to the true location of the object are the criteria. Parameters are the numbers of 1's in object and image, subtemplate size, and parameters reflecting intensity distortion between template and object. One of the proposed statistics R is much more computationally intensive than the other G. Although R is more powerful than G and the other statistics, G is generally more sensitive to the true object location. Statistic G is also more powerful than the sum of absolute differences and correlation. All statistics are robust to incomplete knowledge of distortion parameters. Experiments on Landsat images confirm the sensitivity of G and recommend it for application in the first stage.
Translation Algorithms by Means of Language Intersection

Synchronous rewriting grammars have been successfully exploited as translation models in machine translation applications. In this article we consider the problem of the design of translation algorithms based on synchronous context-free grammars. We revisit a methodology for the design of parsing algorithms based on the idea of language intersection, that has fully been developed in the parsing community, and show how to apply it in the design of translation algorithms. We argue that the intersection methodology above can also be viewed as a framework for the comparison of translation algorithms and for the formal analysis of their properties. On this line, we observe how superficially different translation algorithms that have been proposed in the literature can be viewed as special cases of application of the intersection methodology. We also use our framework to reformulate and improve some results already presented in the literature.
Nested Depth First Search Algorithms for Symmetry Reduction in Model Checking

We propose an algorithm for model checking under weak fairness that exploits symmetry for state space reduction. As intermediate results we also discuss two other algorithms which deal separately with weak fairness and symmetry reduction. The algorithms presented in this chapter are based on the Nested Depth First Search (NDFS) algorithm by Courcoubetis, Vardi, Wolper and Yannakakis. We argue that the worst case time complexity of our algorithm for model checking under weak fairness with symmetry is of the same order of magnitude as the time complexity of the algorithms known in the literature. Moreover, as these algorithms require finding all strongly connected components (SCC) in the state space graph, our algorithm preserves the advantages of the NDFS over the SCC approach.
Mixture and distribuition of different water qualities: An experiment on vertical structure in a complex market

We report results from experimental markets in which two different qualities of water are supplied to two types of consumers: households and farmers. In the scenarios studied, we vary strategic complexity (and centralization) by varying the number of agents per market. Centralization of information by a multiproduct monopolist (scenario 1) improves market performance with respect to a duopoly (scenario 2). A downstream coordinator (scenario 3) succeeds in mitigating upstream market power. In a complex setup like ours, some centralization on the supply or the demand side may enhance market efficiency.
Women’s Experiences and Preferences in Relation to Infertility Counselling: A Multifaith Dialogue

Background Religion and spirituality are a fundamental part of culture and influence how individuals experience and interpret infertility counselling. Thus far, little research has examined the influence of religiosity on the experience of infertility, and to our knowledge no study exists investigating the responses of religious infertile women to counselling. In this study we explored Muslim and Christian women’s experiences and preferences with regard to infertility counselling. Materials and Methods Using a grounded theory approach, 30 infertile women affiliated to different denominations of Islam (Shiite and Sunni) and Christianity (Protestantism, Catholicism, Orthodoxies) were interviewed. Data were collected through semi-structured in-depth interviews at fertility clinics in the UK and Iran, and analyzed using the Straussian mode of grounded theory. Results Emerging categories included: Appraising the meaning of infertility religiously, applying religious coping strategies, and gaining a faith-based strength. These were encompassed in the core category of ‘relying on a higher being’. Religious infertile women experienced infertility as an enriching experience for spiritual growth. This perspective helped them to acquire a feeling of self- confidence and strength to manage their emotions. Hence, they relied more on their own religious coping strategies and less on formal support resources like counselling services. However, they expected counsellors to be open to taking time to discuss their spiritual concerns in counselling sessions. Conclusion In addition to focusing on clients’ psychosocial needs, infertility counsellors should also consider religious and spiritual issues. Establishing a sympathetic and accepting relationship with infertile women will allow them to discuss their religious perspectives, which consequently may enhance their usage of counselling services.
Fast XML document filtering by sequencing twig patterns

XML-enabled publish-subscribe (pub-sub) systems have emerged as an increasingly important tool for e-commerce and Internet applications. In a typical pub-sub system, subscribed users specify their interests in a profile expressed in the XPath language. Each new data content is then matched against the user profiles so that the content is delivered only to the interested subscribers. As the number of subscribed users and their profiles can grow very large, the scalability of the service is critical to the success of pub-sub systems. In this article, we propose a novel scalable filtering system called iFiST that transforms user profiles of a twig pattern expressed in XPath into sequences using the Prüfer's method. Consequently, instead of breaking a twig pattern into multiple linear paths and matching them separately, FiST performs holistic matching of twig patterns with each incoming document in a bottom-up fashion. FiST organizes the sequences into a dynamic hash-based index for efficient filtering, and exploits the commonality among user profiles to enable shared processing during the filtering phase. We demonstrate that the holistic matching approach reduces filtering cost and memory consumption, thereby improving the scalability of FiST.