Miracle-dz/UniFAR-FTUs huggingface dataset:
E cient Numerical Error Bounding for Replicated Network Services

The goal of this work is to use database techniques to support replicated network services that accept updates to numerical records from multiple locations. Given the high overhead of maintaining strong consistency, many replicated services can tolerate divergence of their shared data, as long as the numerical error is bounded. Target distributed services include replicated stock quotes services, online auctions, distributed sensor systems, wide-area resource accounting and load balancing for replicated servers. While these target systems are broader than typical database applications, this work demonstrates how variants of existing database techniques can support these applications. We present two algorithms to e ciently bound absolute error using only local information. Split-Weight AE separately bounds negative and positive weights, while CompoundWeight AE bounds them together. The two algorithms can be combined to provide good performance and low space overhead. Our Inductive RE bounds relative error also based on local information, taking advantage of the fact that the divergence was properly bounded prior to each invocation of the algorithm to reduce required wide-area communication. We discuss two optimizations that enable the algorithms to scale to thousands of data items and hundreds of replicas.
Applying Max-Sum to Asymmetric Distributed Constraint Optimization

We study the adjustment and use of the Max-sum algorithm for solving Asymmetric Distributed Constraint Optimization Problems (ADCOPs). First, we formalize asymmetric factor-graphs and apply the different versions of Max-sum to them. Apparently, in contrast to local search algorithms, most Max-sum versions perform similarly when solving symmetric and asymmetric problems and some even perform better on asymmetric problems. Second, we prove that the convergence properties of Max-sum_ADVP (an algorithm that was previously found to outperform other Max-sum versions) and the quality of the solutions it produces are dependent on the order between nodes involved in each constraint, i.e., the inner constraint order (ICO). A standard ICO allows to reproduce the properties achieved for symmetric problems, and outperform previously proposed local search ADCOP algorithms. Third, we demonstrate that a non-standard ICO can be used to balance exploration and exploitation, resulting in the best performing Maxsum version on both symmetric and asymmetric standard benchmarks.
INCENTIVE DRIVEN INFORMATION SHARING IN DELAY TOLERANT MOBILE NETWORKS

Now a days, with the rapid advancement of wireless technologies, mobile wireless devices such as smart phone‟s, PDAs, and Notebooks are available and they are playing very important roles in our daily life. Such gadgets permit individuals to get to data any place at whatever time subsequent to these gadgets have increasingly larger storage and support multiple network interfaces including cellular, WLAN, and Bluetooth. Thus, besides using such devices to make phone calls and send text messages, clients can use these gadgets to get to and store intriguing information things, for example, news clips, sports events, finance forecast, and trending tweets. Generally, peoples are using such devices for making bank transactions, keep in touch with friends, family members, relatives etc. Clients can likewise store such data and offer with each other by means of deft peer to peer links. (for instance Bluetooth ,WiFi). In this survey paper first I have described our proposed schemes i.e , Multi-Receiver Incentive-Based Dissemination (MuRIS ) in brief, also I have described various previous implemented approach with their pros and cons. I have described in brief MobiCent, Incentive-Aware Data Dissemination in DTN, MOPS, SMART, Pi protocol with their brief introduction and their advantages and limitation. I have also described the comparison of previous approach.
Theoretical Computer Science

Linear functions have gained great attention in the run time analysis of evolutionary computation methods. The corresponding investigations have provided many effective tools for analyzing more complex problems. So far, the runtime analysis of evolutionary algorithms has mainly focused on unconstrained problems, but problems occurring in applications frequently involve constraints. Therefore, there is a strong need to extend the current analyses and used methods for analyzing unconstrained problems to a setting involving constraints. In this paper, we consider the behavior of the classical ( 1 + 1 ) Evolutionary Algorithm on linear functions under linear constraint. We show tight bounds in the case where the constraint is given by the OneMax function and the objective function is given by either the OneMax or the BinVal function. For the general case we present upper and lower bounds.
New Review of Hypermedia and Multimedia

The local binary pattern (LBP) is a robust but computationally simple approach in texture analysis. However, LBP performs poorly in the presence of noise and large illumination variation. Thus, a local adaptive image descriptor termed as LAID is introduced in this proposal. It is a ternary pattern and is able to generate persistent codes to represent microtextures in a given image, especially in noisy conditions. It can also generate stable texture codes if the pixel intensities change abruptly due to the illumination changes. Experimental results also show the superiority of the proposed method over other state-of-the-art methods.
High-fidelity fast volumetric brain MRI using a hybrid denoising generative adversarial network (HDnGAN)

Purpose To improve the signal-to-noise ratio (SNR) of highly accelerated volumetric MRI while preserve realistic textures using a generative adversarial network (GAN). Methods A hybrid GAN for denoising entitled “HDnGAN” with a 3D generator and a 2D discriminator was proposed to denoise 3D T 2 -weighted fluid-attenuated inversion recovery (FLAIR) images acquired in 2.75 minutes ( R =3×2) using wave-controlled aliasing in parallel imaging (Wave-CAIPI). HDnGAN was trained on data from 25 multiple sclerosis patients by minimizing a combined mean squared error and adversarial loss with adjustable weight λ. Results were evaluated on eight separate patients by comparing to standard T2-SPACE FLAIR images acquired in 7.25 minutes ( R =2×2) using mean absolute error (MAE), peak SNR (PSNR), structural similarity index (SSIM), and VGG perceptual loss, and by two neuroradiologists using a five-point score regarding gray-white matter contrast, sharpness, SNR, lesion conspicuity, and overall quality.
Submission from CMU for Blizzard Challenge 2018

In this paper we present the entry from CMU to Blizzard speech synthesis challenge 2018. We begin with a description of build process for our base voice. We then present the following modifications to base voice: (1) Since the data is chosen from children’s stories, we employ Rhetorical Structure Theory to obtain relationships between sentences. We specifically model the contrastive relationship between the sentences within a paragraph. (2) The original speaker attempts to use different ways of speaking depending on character and the situation in the story. To model this, we condition our acoustic model on the character and quote type information. (3) For improving the voice quality we present ‘segmental wavenet’ a variant of the popular autoregressive framework Wavenet.
Cross-View Action Recognition Over Heterogeneous Feature Spaces

In cross-view action recognition, what you saw in one view is different from what you recognize in another view, since the data distribution even the feature space can change from one view to another. In this paper, we address the problem of transferring action models learned in one view (source view) to another different view (target view), where action instances from these two views are represented by heterogeneous features. A novel learning method, called heterogeneous transfer discriminant-analysis of canonical correlations (HTDCC), is proposed to discover a discriminative common feature space for linking source view and target view to transfer knowledge between them. Two projection matrices are learned to, respectively, map data from the source view and the target view into a common feature space via simultaneously minimizing the canonical correlations of interclass training data, maximizing the canonical correlations of intraclass training data, and reducing the data distribution mismatch between the source and target views in the common feature space. In our method, the source view and the target view neither share any common features nor have any corresponding action instances. Moreover, our HTDCC method is capable of handling only a few or even no labeled samples available in the target view, and can also be easily extended to the situation of multiple source views. We additionally propose a weighting learning framework for multiple source views adaptation to effectively leverage action knowledge learned from multiple source views for the recognition task in the target view. Under this framework, different source views are assigned different weights according to their different relevances to the target view. Each weight represents how contributive the corresponding source view is to the target view. Extensive experiments on the IXMAS data set demonstrate the effectiveness of HTDCC on learning the common feature space for heterogeneous cross-view action recognition. In addition, the weighting learning framework can achieve promising results on automatically adapting multiple transferred source-view knowledge to the target view.
Visualization of Biomolecular Structures: State of the Art

Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three-dimensional, complex, large, and time-varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail, and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The report concludes with an outlook on promising and important research topics to enable further success in advancing the knowledge about interaction of molecular structures.
An Ontology Based Question Answering System on Software Test Document Domain

reasoning tasks is an important aim in Computer Science. Semantic Web is one step towards it. The use of ontologies to enhance the information by semantically is the current trend. Huge amount of domain specific, unstructured on-line data needs to be expressed in machine understandable and semantically searchable format. Currently users are often forced to search manually in the results returned by the keyword-based search services. They also want to use their native languages to express what they search. In this paper, an ontology-based automated question answering system on software test documents domain is presented. The system allows users to enter a question about the domain by means of natural language and returns exact answer of the questions. Conversion of the natural language question into the ontology based query is the challenging part of the system. To be able to achieve this, a new algorithm regarding free text to ontology based search engine query conversion is proposed. The algorithm is based on investigation of suitable question type and parsing the words of the question sentence.
UNM Digital Repository UNM Digital Repository

—In this paper, we present an extension of the multi-criteria decision making based on the Analytic Hierarchy Process (AHP) which incorporates uncertain knowledge matrices for generating basic belief assignments (bba’s). The combination of priority vectors corresponding to bba’s related to each (sub)- criterion is performed using the Proportional Conﬂict Redistribution rule no. 5 proposed in Dezert-Smarandache Theory (DSmT) of plausible and paradoxical reasoning. The method presented here, called DSmT-AHP, is illustrated on very simple examples.
Effects of Root Distribution and Root Water Compensation on Simulated Water Use in Maize Influenced by Shallow Groundwater

Groundwater, root distribution, and compensation impact transpiration and NPP. Maize is less reliant on the compensated root water uptake mechanism as roots extend deeper. The impact of drought on corn lessens as roots become deeper. Maize increase its carbon fixation during dry years due to the compensated root water uptake. Shallow‐rooted corn benefits more from groundwater subsidy than deeper rooted corn.
Head-driven Generation with Hpsg

As HPSG is head-driven, with clear semantic heads, semantic head-driven generation should be simple. We adapt van Noord's Prolog generator for use with an HPSG grammar in ProFIT. However, quantiiers and context factors are diicult to include in head-driven generation. We must adopt recent theoretical proposals for lexicalized scoping and context. With these revisions, head-driven generation with HPSG is not so simple, but it is possible.
Perception System for Naturally Interacting Humanoid Robots

The development of humanoid robots rises many challenges in the areas of mechatronics, control and sensor processing. Several partial solutions in the mentioned areas are available but still many unsolved problems do exist. This paper presents a basic concept for a sensor processing system for humanoid robots. The proposed perception system consists of three basic components: information capturing, information memory and information extraction. The capturing module interprets the measurements of the sensors and generates basic statements about the environment. This information is combined to a compact description of objects of interest which is stored in a short term memory. The short term memory is used to extract time depended information from the object descriptions. Experimental results performed with the humanoid robot ROMAN and a behavior-based control architecture show the capabilities of the proposed approach.
TR 08-021 A Comparative Evaluation of Anomaly Detection Techniques for Sequence Data

Anomaly detection has traditionally dealt with record or transaction type data sets. But in many real domains, data naturally occurs as sequences, and therefore the desire of studying anomaly detection techniques in sequential data sets. The problem of detecting anomalies in sequence data sets is related to but different from the traditional anomaly detection problem, because the nature of data and anomalies are different than those found in record data sets. While there are many surveys and comparative evaluations for traditional anomaly detection, similar studies are not done for sequence anomaly detection. We investigate a broad spectrum of anomaly detection techniques for symbolic sequences, proposed in diverse application domains. Our hypothesis is that symbolic sequences from different domains have distinct characteristics in terms of the nature of sequences as well as the nature of anomalies which makes it important to investigate how different techniques behave for different types of sequence data. Such a study is critical to understand the relative strengths and weaknesses of different techniques. Our paper is one such attempt where we have comparatively evaluated 7 anomaly detection techniques on 10 public data sets, collected from three diverse application domains. To gain further understanding in the performance of the techniques, we present a novel way to generate sequence data with desired characteristics. The results on the artificially generated data sets help us in experimentally verifying our hypothesis regarding different techniques.
Expert Systems With Applications

A primary research direction for recommender systems is matrix completion, which attempts to recover the missing values in a user–item rating matrix. There are numerous approaches for rating tasks, which are mainly classified into latent factor models and neighborhood-based models. Most neighborhood-based models seek similar neighbors by computing similarities in the original data space for final predictions. In this paper, we propose a new neighborhood-based interpolation model with a kernelized matrix completion framework, with the impact weights provided by neighbors computed in a new Hilbert space containing more features. In our model, the kernel function is combined with a similarity measurement to achieve better approximation for unknown ratings. Furthermore, we extend our model with a non-linear multi-kernel framework which learns weights automatically to improve the model. Finally, we conduct extensive experiments on several real-world datasets. The outcomes show that the proposed methods work effectively and improve the performance of the rating prediction task compared to both the traditional and state-of-the-art approaches.
Medical Document Clustering Using Ontology-Based Term Similarity Measures

Recent research shows that ontology as background knowledge can improve document clustering quality with its concept hierarchy knowledge. Previous studies take term semantic similarity as an important measure to incorporate domain knowledge into clustering process such as clustering initialization and term re-weighting. However, not many studies have been focused on how different types of term similarity measures affect the clustering performance for a certain domain. In this article, we conduct a comparative study on how different term semantic similarity measures including path-based, informationcontent-based and feature-based similarity measure affect document clustering. Term re-weighting of document vector is an important method to integrate domain ontology to clustering process. In detail, the weight of a term is augmented by the weights of its co-occurred concepts. Spherical k-means are used for evaluate document vector re-weighting on two real-world datasets: Disease10 and OHSUMED23. Experimental results on nine different semantic measures have shown that: (1) there is no certain type of similarity measures that significantly outperforms the others; (2) Several similarity measures have rather more stable performance than the others; (3) term re-weighting has positive effects on medical document clustering, but might not be significant when documents are short of terms. Xiaodan Zhang Drexel University, USA Liping Jing The University of Hong Kong, China Xiaohua Hu Drexel University, USA Michael Ng Hong Kong Baptist University, China Jiali Xia Jiangxi University of Finance and Economics, China Xiaohua Zhou Drexel University, USA Medical Document Clustering Using Ontology-Based Term Similarity Measures
On a General Method for Maximizing and Minimizing among Certain Geometric Problems (Extended Abstract)

Problems concerned with finding inscribing or circumscribing polygons that maximize some measurement are considered such as: Find an area maximizing triangle inscribed in a given convex polygon. Algorithms solving a number of these problems in linear time are presented. They use the common approach of finding an initial solution with respect to a fixed bounding point and then iteratively transforming this solution into a new solution with respect to a new point. The generality of this approach is discussed and several open problems are noted.
Ransomware Activity and Blockchain Congestion

Theory predicts that a cryptocurrency may fail if blockchain congestion causes users to exit. I examine this prediction using congestion episodes caused by more than 9,000 triggers for ransomware attacks over a four-year period. Congestion leads to exit by some users due to increased transaction costs. Such users often migrate from the blockchain to crypto exchanges. Migration places a constraint on mining rewards, potentially leading to the failure of a cryptocurrency in the long run.
NEURAL NETWORKS IN ECONOMICS Background, Applications and New Developments RALF HERBRICH MAX KEILBACH

Neural Networks – originally inspired from Neuroscience – p rovide powerful models for statistical data analysis. Their most prominent feature is their a b lity to “learn” dependencies based on a finite number of observations. In the context of Neural Ne tworks the term “learning” means that the knowledge acquired from the samples can be gen eralized to as yet unseen observations. In this sense, a Neural Network is often calle d a Learning Machine. As such, Neural Networks might be considered as a metaphor for an agen t who learns dependencies of his environment and thus infers strategies of behavior ba sed on a limited number of observations. In this contribution, however, we want to abstract from the biological origins of Neural Networks and rather present them as a purely mathematical mo del. The paper is organized as follows: In Section 2 we will summar ize the main results of Statistical Learning Theory which provide a basis for understa nding the generalization properties of existing Neural Network learning algorithms. In Section 3 we will introduce basic concepts and techniques of Neural Network Learning. Then, in Section 4, we will give an overview of existing applications of Neural Networks in Economics. R ecently, however, the ideas from Statistical Learning Theory, as introduced in Section 2, ha ve lead the way to the so called Support Vector learning, which will be described in Section 5 for the task of classification. Finally, in Section 6 we leave the review track and present a s tate–of–the–art application of this technique to the problem of learning the preference structu e from a given set of observations. In contrast to cardinal utility theory our approach is based on ordinal utilities and requires as input only objects together with their respective relation (i.e. preference ranking). Throughout the paper, we will support the theoretical arguments by exam ples.
Final Year Project Report Compiler for CP

CP is a new programming language based on the Compositional Programming. CP allows writing highly modular code. It allows extensibility in multiple dimensions without modifying existing code. CP also has a document-authoring language called ExT embedded in it. ExT, by virtue of being embedded in CP, is also extensible. New features and language constructs can be modularly added to ExT. In its current form, CP is an interpreted language with its interpreter written in PureScript. It is possible to write and execute programs in CP. However, the execution times can be undesirably long. This project aims to solve this problem, and does that in two ways. The first of them is creating in a new and faster parser for CP. The current parser, which is a part of the CP interpreter, is built using the parser combinator library of PureScript. Parser combinators are a great way to prototype parsers, but they can be inefficient and slow. In this a project, ANTLR, a parser generator has been used to develop a new parser. The new parser generated is written in JavaScript, which allows it to be easily integrated and deployed with the existing in-browser interpreter. Experiments show that the new parser is upto 99% less time than the existing one. The second contribution this project makes to speed up CP is by designing and implementing a compilation technique for the merge operator. CP elaborates to Fi+ as an intermediate language during interpretation (and also compilation). The merge operator is an important component of the Fi+ calculus and facilitates the modularity of CP. The compiler for merge operator will be part of the complete compiler for Fi+, and therefore contribute to making CP faster.