Miracle-dz/UniFAR-FTUs huggingface dataset:
AIS Electronic

Cloud Computing technology brings a fundamental change from On-premises software to Software as a Service (SaaS) within the software industry. To stay competitive, well-established companies need to transform and adjust strategies. Based on five case studies and Christensen’s theory for managing disruptive innovations, this study focuses on software companies in the B2C market. The study analyses their transformation strategy in terms of an On-premises provider to a company offering SaaS. Although Christensen’s recommendations are partly applicable there were additional strategies that proved to be valuable in practice. Eight strategies were derived for software companies in the B2C market to better cope with the transformation process. Finally, the study was able to draw a comparison between transformation strategies in the B2C and B2B market. Although the software market as a whole was affected by Cloud Computing technology, transformation strategies in these different markets varied significantly.
Tapta: A user-driven translation system for patent documents based on domain-aware Statistical Machine Translation

This paper presents a study conducted in the course of implementing a project in the World Intellectual Property Organization (WIPO) on assisted translation of patent abstracts and titles from English to French. The tool (called ‘Tapta’) is trained on an extensive corpus of manually translated patents. These patents are classified, each class belonging to one of the 32 predefined domains. The trained Statistical Machine Translation (SMT) tool uses this additional information to propose more accurate translations according to the context. The performance of the SMT system was shown to be above the current state of the art, but, in order to produce an acceptable translation, a human has to supervise the process. Therefore, a graphical user interface was built in which the translator drives the automatic translation process. A significant experiment with human operators was conducted within WIPO, the output was judged to be successful and a project to use Tapta in production is now under discussion.
6D Pose Estimation of Transparent Object from Single RGB Image

—Transparent objects are one of the most common objects in everyday life. Estimating pose of these objects are required to pick and manipulate such objects. However, due to the absorption and refraction of light, it is hard to capture depth image of transparent object. In this paper, we address this problem using synthetic dataset to train deep neural network and estimate pose of known transparent objects. Synthetic dataset contains depth map of transparent object which we created in realistic looking environment. Also combining domain randomized and photorealistic images, we create desired amount of annotated data in order to network operate successfully against real world data. We conducted experiment on 3D printed transparent objects in the real environment. For future work, we are planning to build random bin picking system for transparent object.
Typical Properties of Winners and Losers in Discrete Optimization

We present a probabilistic analysis of a large class of combinatorial optimization problems containing all binary optimization problems defined by linear constraints and a linear objective function over $\{0,1\}^n$. Our analysis is based on a semirandom input model that preserves the combinatorial structure of the underlying optimization problem by parameterizing which input numbers are of a stochastic and which are of an adversarial nature. This input model covers various probability distributions for the choice of the stochastic numbers and includes smoothed analysis with Gaussian and other kinds of perturbation models as a special case. In fact, we can exactly characterize the smoothed complexity of binary optimization problems in terms of their worst-case complexity: A binary optimization problem has polynomial smoothed complexity if and only if it admits a (possibly randomized) algorithm with pseudo-polynomial worst-case complexity. Our analysis is centered around structural properties of binary optimization problems, called winner, loser, and feasibility gap. We show that if the coefficients of the objective function are stochastic, then the gap between the best and second best solution is likely to be of order $\Omega(1/n)$. Furthermore, we show that if the coefficients of the constraints are stochastic, then the slack of the optimal solution with respect to this constraint is typically of order $\Omega(1/n^2)$. We exploit these properties in an adaptive rounding scheme that increases the accuracy of calculation until the optimal solution is found. The strength of our techniques is illustrated by applications to various \npc-hard optimization problems from mathematical programming, network design, and scheduling for which we obtain the first algorithms with polynomial smoothed/average-case complexity.
Web Forum Crawling Techniques

The web contains large data and it contains innumerable websites that is monitored by a tool or a program known as Crawler. The main goal of this paper is to focus on the web forum crawling techniques. In this paper, the various techniques of web forum crawler and challenges of crawling are discussed. The paper also gives the overview of web crawling and web forums.
Associated dataset: Impacts of Atmospheric Nitrogen Deposition and Coastal Nitrogen Fluxes on Oxygen Concentrations in Chesapeake Bay

The dataset includes model outputs used in publication Da et al. (2018), which used the EstuarineCarbon-Biogeochemistry model embedded in the Regional-Ocean-Modeling-System (ChesROMS-ECB) to examine the relative impact of direct atmospheric dissolved inorganic nitrogen (DIN) deposition and DIN from the continental shelf on the Chesapeake Bay dissolved oxygen. Model simulations highlight that DIN from the atmosphere has roughly the same impact on hypoxia as the same gram-for-gram change in riverine DIN loading. DIN concentrations on the continental shelf has a similar overall impact on hypoxia as DIN from the atmosphere. DOI: I would like a DOI assigned to this dataset Funding: Funding of this research was provided by the U.S. National Oceanic and Atmospheric Administration Interdisciplinary Science Program (NNX14AF93G), the National Science Foundation (OCE-1259187), and by sponsors of the computing facilities at the College of William and Mary (including contributions from the National Science Foundation, the Commonwealth of Virginia Equipment Trust Fund, and the Office of Naval Research).
A User Preference Tree based Personalized Route Recommendation System for Constraint Tourism and Travel

Personalized recommendation systems recommend the target destination based on user-generated data from social media and geo-tagged photos that are currently available as a most pertinent source. This paper proposes a tourism destination recommendation system which uses heterogeneous data sources that interprets both texts posted on social media and images of tourist places visited and shared by tourists. For this purpose, we propose an enhanced user profile that uses User-Location Vector with LDA and Jaccard Coefficients. Moreover, a new Tourist Destination tree is constructed using the posts extracted from TripAdvisor where each node of the destination tree consists of tourist destination data. Finally, we build a personalized recommendation system based on user preferences, A* algorithm and heuristic shortest path algorithm with cost optimization based on the backtracking based Travelling Salesman Problem solution, tourist destination tree and tree-based hybrid recommendations. Here, the 0/1 knapsack algorithm is used for recommending the best Tourist Destination travel route plans according to the travel time and cost constraints of the tourists. The experimental results obtained from this work depict that the proposed User Centric Personalized destination and travel route recommendation system is providing better recommendation of tourist places than the existing systems by handling multiple heterogeneous data sources efficiently for recommending optimal tour plans with minimum cost and time.
Data and text mining A new method to measure the semantic similarity of GO terms

Motivation: Although controlled biochemical or biological vocabularies, such as Gene Ontology (GO) (http://www.geneontology.org), address the need for consistent descriptions of genes in different data sources, there is still no effective method to determine the functional similarities of genes based on gene annotation information from heterogeneous data sources. Results: To address this critical need, we proposed a novel method to encode a GO term’s semantics (biological meanings) into a numeric value by aggregating the semantic contributions of their ancestor terms (including this specific term) in the GO graph and, in turn, designed an algorithm to measure the semantic similarity of GO terms. Based on the semantic similarities of GO terms used for gene annotation, we designed a new algorithm to measure the functional similarity of genes. The results of using our algorithm to measure the functional similarities of genes in pathways retrieved from the saccharomyces genome database (SGD), and the outcomes of clustering these genes based on the similarity values obtained by our algorithm are shown to be consistent with human perspectives. Furthermore, we developed a set of online tools for gene similarity measurement and knowledge discovery. Availability: The online tools are available at: http://bioinformatics. clemson.edu/G-SESAME Contact: jzwang@cs.clemson.edu Supplementary information: http://bioinformatics.clemson.edu/ Publication/Supplement/gsp.htm
Surrogate-Based Optimization using Multifidelity Models with Variable Parameterization

Engineers are increasingly using high-fidelity models for numerical optimization. However, the computational cost of these models, combined with the large number of objective function and constraint evaluations required by optimization methods, can render such optimization computationally intractable. Surrogate-based optimization (SBO) optimization using a lower-fidelity model most of the time, with occasional recourse to the high-fidelity model is a proven method for reducing the cost of optimization. One branch of SBO uses lower-fidelity physics models of the same system as the surrogate. Until now however, surrogates using a different set of design variables from that of the high-fidelity model have not been available to use in a provably convergent numerical optimization. New methods are herein developed and demonstrated to reduce the computational cost of numerical optimization of variableparameterization problems, that is, problems for which the low-fidelity model uses a different set of design variables from the high-fidelity model. Four methods are presented to perform mapping between variable-parameterization spaces, the last three of which are new: space mapping, corrected space mapping, a mapping based on proper orthogonal decomposition (POD), and a hybrid between POD mapping and space mapping. These mapping methods provide links between different models of the same system and have further applications beyond formal optimization strategies. On an unconstrained airfoil design problem, it achieved up to 40% savings in highfidelity function evaluations. On a constrained wing design problem it achieved 76% time savings, and on a bat flight design problem, it achieved 45% time savings. On a large-scale practical aerospace application, such time savings could represent weeks. Thesis Supervisor: Karen Willcox Title: Associate Professor of Aeronautics and Astronautics Thesis Supervisor: Robert Haimes Title: Principal Research Engineer
Информатика и информационные технологии NEURAL DISTRIBUTED AUTOASSOCIATIVE MEMORIES: A SURVEY

discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.
Environmental Interference Cancellation of Speech with the Radial Basis Function Networks : An Experimental Comparison Nima Hatami

(RBFN) for solving the problem of environmental interference cancellation of speech signal. We show that the Second Order Thin-Plate Spline (SOTPS) kernel cancels the interferences effectively. For make comparison, we test our experiments on two conventional most used RBFN kernels: the Gaussian and First order TPS (FOTPS) basis functions. The speech signals used here were taken from the OGI Multi-Language Telephone Speech Corpus database and were corrupted with six type of environmental noise from NOISEX-92 database. Experimental results show that the SOTPS kernel can considerably outperform the Gaussian and FOTPS functions on speech interference cancellation problem.
Deformation Analysis to Detect and Quantify Active Lesions in 3D Medical Image Sequences

: Evaluating precisely the temporal variations of tumor volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery and patients follow-up. In this paper, we present a volumetric analysis technique, combining precise rigid registration of 3D medical images, non-rigid deformation computation and (cid:29)ow (cid:28)eld analysis. Our analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of our approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest, which can be automatized. We distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction e(cid:27)ects re(cid:29)ecting a change of mass within the tissue; a real lesion being generally the combination of both e(cid:27)ects. The method is tested with synthesized 3D image sequences and applied, in a (cid:28)rst attempt to quantify in-vivo a mass e(cid:27)ect, to the analysis of a real patient case with Multiple Sclerosis.
THRESHOLDING BASED DETECTION OF FINE AND SPARSE DETAILS

Fine and sparse details, as defined in this study, occur in several applications where image processing can be applied. The definition of fine and sparse details is given in the domain of gray level histograms by establishing sufficient statistical properties. In experiments, artificial data was generated from a model based on the definition, and several thresholding methods were compared. Based on the experiments on artificial data, it seems that the minimum error thresholding by Kittler and Illingworth outperforms the other methods. The result was verified using images from IGT picking paper printability assessment where small surface defects must be detected. Furthermore, image enhancement and a minor adjustment to the minimum error thresholding are introduced to achieve optimal performance in IGT picking paper printability assessment.
An Eecient Parallel Algorithm for the General Planar Monotone Circuit Value Problem

A planar monotone circuit (PMC) is a Boolean circuit that can be embedded in the plane and that contains only AND and OR gates. Goldschlager, Cook & Dymond and others have developed NC 2 algorithms to evaluate a special layered form of a PMC. These algorithms require a large number of processors (((n 6), where n is the size of the input circuit). Yang, and more recently, Delcher & Kosaraju have given NC algorithms for the general planar monotone circuit value problem. These algorithms use at least as many processors as the algorithms for the layered case. In this paper, we give an eecient parallel algorithm that evaluates a general PMC of size n in polylog time using only a linear number of processors on an EREW PRAM. Our parallel algorithm is the best possible to within a polylog factor, and is a substantial improvement over the earlier algorithms for the problem. Our algorithm uses several novel techniques to perform the evaluation, including the use of the dual of the plane embedding of the circuit to determine the propagation of values within the circuit.
Design of File System Architecture with Cluster Formation Along With Mount Table: A Review

--------------------------------------------------ABSTRACT ------------------------------------------------------A keyword cluster is used in the hash table to find corresponding cluster having all related files. And then all the files related to that keyword are displayed. Then, upon selection of one file, the whole content belonging to that file is displayed. This research is focused on solutions to get or retrieve data from large data source in less time and at less computation cost. Query optimization filters important words as keywords and then these are passed to hash table, i.e. indexing technique is used which finds corresponding value. In a cluster, same type of data is presented in different types of file formats . There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale Data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, description & comparison of both paradigms on an open source version of MR as well as on parallel DBMS have been carried out. The observed performance of these DBMSs was strikingly better. This gives a boost to the idea of implementing clustering in Query Optimization.
Indexing of Reading Paths for a Structured Information Retrieval on the Web

In this paper, we present a hyperdocument model taking into account the essential aspects of information on the Web: content, composition (logical structure) and nonlinear reading (hypertext structure). We have developed a Structured Information Retrieval System (SIRS) based on this model. Its phases of indexing and querying are based on a “reading paths” point of view of the Web: a Web site is considered as a set of potential reading paths, instead of a set of atomic and flat pages. We have developed an specific algorithm to index the reading paths. We present some experiments aiming at evaluating the interest of our indexing process of reading paths.
Hybrid modelling and performance evaluation of switched discrete-event systems

Motivated by the integrated complexity of real-time intelligent control and optimization of industrial/manufacturing processes, this paper discusses hybrid modelling and asymptotic periodic be- havior of a class of switched discrete event systems, and shows how to evaluate the asymptotic performance/efficiency of such systems. We prove that, under some mild conditions, the switched discrete event system will achieve asymptotic periodic dynamics, and its perfor- mance/efficiency can be evaluated by calculating the eigenvalue of certain matrix in max-plus algebra. Illustrative examples are pro- vided.
Multi-Level Multi-Perspective Dynamic Decision Making in Medicine

ion Mechanisms in the Transition View While modeling a problem in the Transition View, a physician may have a notion of a hierarchical relatio ship between various actions or states. He may wish model the problem at a level of detail that he is com fortable with and iteratively abstract or refine th action or state space. We identify two types of actio abstraction and refinement operations. • Sequential Action Abstraction -This operation is used when actions A 1, A2,. , An, are always executed in a strict sequence and the modeler wants to repres them by a single action A. The Transition Views fo each of the sub-actions must be combined into a sin Transition View. The new view will contain the same states as the original views. The transition probabili matrix for the abstract action is a multiplication of th component matrices in the given order. • Sequential Action RefinementThis is the reverse operation of Sequential Abstraction, that is, decom posing an abstract action A into a sequence of su actions, A1, A2,. , An. In this case, if we know the transitions of some of the refined actions, we can infe Actions
Infrastructure-Free Collaborative Indoor Positioning Scheme for Time-Critical Team Operations

Indoor localization is the key infrastructure for indoor location-aware applications. In this paper, we consider an emergency scenario, where a team of soldiers or first responders perform time-critical missions in a large and complex building. In particular, we consider the case where infrastructure-based localization is not feasible for various reasons such as installation/management costs, a power outage, and terrorist attacks. We design a novel algorithm called the collaborative indoor positioning scheme (CLIPS), which does not require any pre-existing indoor infrastructure. Given that users are equipped with a signal strength map for the intended area for reference, CLIPS uses this map to compare and extract a set of feasible positions from all positions on the map when the device measures signal strength values at run time. Dead reckoning is then performed to remove invalid candidate coordinates, eventually leading to only correct positions. The main departure from existing peer-assisted localization algorithms is that our approach does not require any infrastructure or manual configuration. We perform testbed experiments and extensive simulations, and our results verify that our proposed scheme converges to an accurate set of positions much faster than existing noncollaborative solutions.
Mathematical Structures in Computer Science

We propose a relational computing paradigm based on Eilenberg machines, an eﬀective version of Eilenberg’s X-machines suitable for general non-deterministic computation. An Eilenberg machine generalizes a ﬁnite-state automaton, seen as its control component, with a computation component over a data domain speciﬁed as a relational algebra, its actions being interpreted as binary relations over the data domain. We show various strategies for the sequential simulation of our relational machines, using variants of the reactive engine . In a particular case of ﬁnite machines , we show that bottom–up search yields an eﬃcient complete simulator. Relational machines may be composed in a modular fashion, since atomic actions of one machine can be mapped to the characteristic relation of other relational machines acting as its parameters. The control components of machines can be compiled from regular expressions. Several such translations have been proposed in the literature, which we brieﬂy survey.