Miracle-dz/UniFAR-FTUs huggingface dataset:
BUSTER - An Information Broker for the Semantic Web

Among the most fundamental requirements for Semantic Web applications is their ability to access and to interpret information sources that are geographically distributed, as well as highly heterogeneous with respect to syntax, structure and semantics (Visser and Klein 2002). In order to be able to decide whether an information source is relevant to a specific information request and to resolve semantic heterogeneities during the subsequent integration of this information source, Semantic Web applications must have access to metadata that describe the semantics of an information source. The Bremen University Semantic Translator for Enhanced Retrieval (BUSTER) is an information broker middleware that was built to tackle the challenges of the Semantic Web. BUSTER is able to access heterogeneous and distributed information sources and to assess their conceptual, spatial, and temporal relevance with respect to a specific information request. BUSTER can also be used to integrate heterogeneous information through the resolution of structural, syntactical, and semantic heterogeneities. In this article, we focus on the application of BUSTER as a tool for intelligent search and information retrieval in the Semantic Web. The BUSTER search module supports the specification of queries of the type concept @ location in time. In addition to the conceptual semantics, the system evaluates the spatial as well as the temporal relevance of an information source. A typical example for an information request would be: “Find a conference-hotel near the Thüringer Wald for the period between Christmas and New Year 2003”. In order to be able to reason about conceptual (“Which concepts are subsumed by the concept specified in the query? ”), spatial (“Which regions are in or near the specified geographic region? ”), and temporal relevance (“Which dates fall within the given time period? ”), BUSTER utilizes metadata that provide formal descriptions of the respective context of an information source. While well-established methods to formalize and to reason about conceptual semantics could be used, the inclusion of spatial and temporal aspects demanded the development of new representation schemes and reasoning mechanisms. Given the distributed structure of the Semantic Web and its implicit open world assumption, a major challenge was to provide a framework for the interoperability and integration of formal conceptual, spatial, and temporal representations. In the following, we will give a brief introduction to the methods and solutions we developed to meet these challenges. BUSTER – An Information Broker for the Semantic Web
Constituencies for Users: How to Develop them by Interpreting Logs of Web Site Access

The number of electronic journals is growing as rapidly as the World Wide Web on which many are published. Readership of an electronic journal is important to quantify, just as it is for a printed journal. In maintaining the Journal of Interactive Media in Education (JIME), a scholarly electronic journal open to all, we require readership statistics more meaningful than the variations on the theme of "number of hits" given by many log analysis packages. Important aspects of JIME's open access are the decisions to not require subscription, nor the use of hidden tracking aids such as cookies. Readership information is acquired from the server log of client requests. We are investigating the identification of user sessions from such logs, and the development of classification of sessions into constituencies of readership. In this paper, we present the result of manual analysis from which we are developing automatic analysis mechanisms.
Pharmacologic doses of ascorbate act as a prooxidant and decrease growth of aggressive tumor xenografts in mice

Ascorbic acid is an essential nutrient commonly regarded as an antioxidant. In this study, we showed that ascorbate at pharmacologic concentrations was a prooxidant, generating hydrogen-peroxide-dependent cytotoxicity toward a variety of cancer cells in vitro without adversely affecting normal cells. To test this action in vivo, normal oral tight control was bypassed by parenteral ascorbate administration. Real-time microdialysis sampling in mice bearing glioblastoma xenografts showed that a single pharmacologic dose of ascorbate produced sustained ascorbate radical and hydrogen peroxide formation selectively within interstitial fluids of tumors but not in blood. Moreover, a regimen of daily pharmacologic ascorbate treatment significantly decreased growth rates of ovarian (P < 0.005), pancreatic (P < 0.05), and glioblastoma (P < 0.001) tumors established in mice. Similar pharmacologic concentrations were readily achieved in humans given ascorbate intravenously. These data suggest that ascorbate as a prodrug may have benefits in cancers with poor prognosis and limited therapeutic options.
Computationally Efficient Subspace-Based Method for Two-Dimensional Direction Estimation With L-Shaped Array

In order to mitigate the effect of additive noises and reduce the computational burden, we propose a new computationally efficient cross-correlation based two-dimensional (2-D) direction-of-arrivals (DOAs) estimation (CODE) method for noncoherent narrowband signals impinging on the L-shaped sensor array structured by two uniform linear arrays (ULAs). By estimating the azimuth and elevation angles independently with a one-dimensional (1-D) subspace-based estimation technique without eigendecomposition, where the null spaces are obtained through a linear operation of the matrices formed from the cross-correlation matrix between the received data of two ULAs, then the pair-matching of estimated azimuth and elevation angles is accomplished by searching the minimums of a cost function of the azimuth and elevation angles, where the computationally intensive and time-consuming eigendecomposition process is avoided. Further the asymptotic mean-square-error (MSE) expressions of the azimuth and elevation estimates are derived. The effectiveness of proposed method and the theoretical analysis are verified through numerical examples, and it is shown that the proposed CODE method performs well at low signal-to-noise ratio (SNR) and with a small number of snapshots.
ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis

ChIA-PET2 is a versatile and flexible pipeline for analyzing different types of ChIA-PET data from raw sequencing reads to chromatin loops. ChIA-PET2 integrates all steps required for ChIA-PET data analysis, including linker trimming, read alignment, duplicate removal, peak calling and chromatin loop calling. It supports different kinds of ChIA-PET data generated from different ChIA-PET protocols and also provides quality controls for different steps of ChIA-PET analysis. In addition, ChIA-PET2 can use phased genotype data to call allele-specific chromatin interactions. We applied ChIA-PET2 to different ChIA-PET datasets, demonstrating its significantly improved performance as well as its ability to easily process ChIA-PET raw data. ChIA-PET2 is available at https://github.com/GuipengLi/ChIA-PET2.
Chapter I An Overview of Image and Video Segmentation in the Last 40 Years

The history of segmentation of digital images using computers could be traced back 40 years. Since then, this field has evolved very quickly and has undergone great change. In this chapter, the position of image segmentation in the general scope of image techniques is first introduced; the formal definition and extension of image segmentation as well as three layers of research on image segmentation are then explained. Based on the introduction and explanations, statistics for a number of developed algorithms is provided, the scheme for classifying different segmentation algorithms is discussed and a summary of existing survey papers for image segmentation is presented. These discussions provide a general rendering of research and development of image segmentation in the last 40 years.
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections

Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms used to derive the performance measures
Efficient safe-region construction for moving top-K spatial keyword queries

Many real-world applications have requirements to support moving spatial keyword queries. For example a tourist looks for top-k "seafood restaurants" while walking in a city. She will continuously issue moving queries. However existing spatial keyword search methods focus on static queries and it calls for new effective techniques to support moving queries efficiently. In this paper we propose an effective method to support moving top-k spatial keyword queries. In addition to finding top-k answers of a moving query, we also calculate a safe region such that if a new query with a location falling in the safe region, we can directly use the answer set to answer the query. To this end, we propose an effective model to represent the safe region and devise efficient search algorithms to compute the safe region. We have implemented our method and experimental results on real datasets show that our method achieves high efficiency and outperforms existing methods significantly.
In Situgel: Development, Evaluation and Optimization Using 3 Factorial Design

Development of sustained release oral dosage forms is beneficial for optimal therapy regarding the efficacy, safety and patient compliance. Among oral dosage form, liquid dosage forms are more prone to low bioavailability because of their quick transit from the gastrointestinal tract. To produce sustained release formulation of an oral liquid formulation could be successfully augmented substantially through a strategy of liquid in-situ gelling system. The purpose of the present work was to develop oral in situ gelling system using Sodium alginate for in situ gelation of ambroxol-HCl. The formulation variables like concentration of polymer and calcium chloride will be optimized using factorial design. The promising formulations will be evaluated for pH, drug content, in vitro gelation, in vitro drug release, stability.
IR Image Based Eye Gaze Estimation

In this paper, we present a novel approach to estimate the human's eye gaze from a single infrared spectrum image of two eyes. Observing that the pupil contour is a circle, we estimate the normal direction of this circle, considered as the eye gaze, from its elliptical image. By assuming that the two gaze vectors of both eyes and the vector which is determined by the two centers of the right and left pupil are coplanar, we can calculate the both eye gaze. Compared to most existing 3-D gaze estimation algorithms, our approach does not require those of face feature information such as the eye corners. Since the focal length can be calculated by our "Coplanar Constraint", ours does not need to know the exact focal length of the camera. The extensive experiments over simulated images and real images demonstrate the feasibility and the effectiveness of our method.
( Re ) live Photos : Generating Videos with GANs

Along with the iPhone 6s, Apple announced a new feature, dubbed ‘Live Photos’: along with every picture, the iPhone would take a short video in the surrounding time frame, which when combined with the photo, could make photos appear to ‘come to life’. Our goal is to also bring photos to life without requiring the original video data. We implement several architectures and analyze their tradeoffs in producing these videos. We first highlight WGAN, which improves stability when training GANs, and describe our various models using WGANs. We then delve into our LSTM network for future frame prediction. Although there is no standard metric to measure video generation quantitatively, our qualitative measurements show our models are able to produce some realistic videos.
PIF: A Personalized Fine-Grained Spam Filtering Scheme With Privacy Preservation in Mobile Social Networks

Mobile social network (MSN) emerges as a promising social network paradigm that enables mobile users' information sharing in the proximity and facilitates their cyber-physical-social interactions. As the advertisements, rumors, and spams spread in MSNs, it is necessary to filter spams before they arrive at the recipients to make the MSN energy efficient. To this end, we propose a personalized fine-grained filtering scheme (PIF) with privacy preservation in MSNs. Specifically, we first develop a social-assisted filter distribution scheme, where the filter creators send filters to their social friends (i.e., filter holders). These filter holders store filters and decide to block spams or relay the desired packets through coarse-grained and fine-grained keyword filtering schemes. Meanwhile, the developed cryptographic filtering schemes protect creator's private information (i.e., keyword) embedded in the filters from directly disclosing to other users. In addition, we establish a Merkle Hash tree to store filters as leaf nodes where filter creators can check if the distributed filters need to be updated by retrieving the value of root node. It is demonstrated that the PIF can protect users' private keywords included in the filter from disclosure to others and detect forged filters. We also conduct the trace-driven simulations to show that the PIF can not only filter spams efficiently but also achieve high delivery ratio and low latency with acceptable resource consumption.
Research on Clustering Algorithm for Massive Data Based on Hadoop Platform

With the concepts of cloud computing springing up, the researches of data mining clustering algorithm which is based on cloud computing become a research focus for scholars both at home and abroad. This article aiming at the extensive data clustering problem, using cloud computing technology, according to Hadoop platform does a deep research based on cloud computing platforms Hadoop and parallel K-means clustering algorithm. And it puts forward a kind of mass data clustering model based on Hadoop and new ideas of parallel K-means algorithm.
A budget-based algorithm for efficient subgraph matching on Huge Networks

As social network and RDF data grow dramatically in size to billions of edges, the ability to scalably answer queries posed over graph datasets becomes increasingly important. In this paper, we consider subgraph matching queries which are often posed to social networks and RDF databases — for such queries, we want to find all matching instances in a graph database. Past work on subgraph matching queries uses static cost models which can be very inaccurate due to long-tailed degree distributions commonly found in real world networks. We propose the BudgetMatch query answering algorithm. BudgetMatch costs and recosts query parts adaptively as it executes and learns more about the search space. We show that using this strategy, BudgetMatch can quickly answer complex subgraph queries on very large graph data. Specifically, on a real world social media data set consisting of 1.12 billion edges, we can answer complex subgraph queries in under one second and significantly outperform existing subgraph matching algorithms.
Simulation and 3D visualisation of physical phenomena on mobile devices

Educational simulations are breaking out from traditional areas of use and emerging as an increasingly important tool for education. Even though, they are very practical tool for assisting in the classrooms, they have been supported mostly on desktop environments and didn’t fulfill the desired adoption. For this reason we created the mobile application that is a tool for presenting physics simulations. It is supported by different devices, including mobile phones and tablets, taking advantage of the different display size, and computational power characteristics of the devices. To make this application reachable not just from desktop computers, we used jQuery Mobile, framework that is optimized for touch devices. We have also provided the multiuser support of the simulations, offering users the possibility to manipulate shared real-time simulations. For that purpose we implemented WebSockets, technology that is facilitating live content and the creation of real-time simulations.
An Economic Analysis of Bangladesh’s Foreign Exchange Reserves

Focusing on global best practices and local circumstances, this paper offers an economic analysis of issues pertaining to Bangladesh’s foreign exchange reserves. Although its reserves are not strikingly higher than what is required, the paper highlights the fact that they are the result of an ‘investment drought’ in the country. Like a typical two-handed economist, the author recommends, albeit with a skewed preference for first option, that: a) Bangladesh should expedite its financial sector reform using its reserves as insurance; and b) if it does not intend to markedly disturb its reform equilibrium, the country can channel part of its reserves for alternatives uses as the cost of the reserves build-up is growing following the collapse in interest rates in the United States. In the absence of required managerial know-how, the question of alternative uses of the country’s reserves requires further research as it is a trade-off between return and liquidity.
The Ergodic Rate Density of MIMO Ad-hoc Networks

We analyze the scaling laws of the Ergodic Rate Density (ERD) in ALOHA Wireless Ad-hoc Networks (WANETs) that utilize Multiple-In-Multiple-Out (MIMO) techniques. We present a novel lower bound that support spatial-multiplexing, interference-cancellation and beamforming. The bound is used to present expressions of the optimum system parameters as function of the number of antennas and the path-loss factor. When the density of active users can be tuned to maximize the performance, we show that the ERD scales linearly with the number of antennas and that single stream transmission optimizes the network performance. When the active users density is limited and it's below 70% of the optimal density, spatial multiplexing was shown to have better performance than single stream transmission. Simulation results show the practically of the novel bound and the accuracy of the estimated parameters.
Categorical Maximum Entropy model 3 . 1 Categorical MaxEnt Model Specification

Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and on US census datasets, and demonstrate its feasibility using an epidemic simulation application.
Review of methods for time interval measurements with picosecond resolution

This paper is a review of methods and techniques used for precise measurement of time intervals (TIs) or precise conversion of TIs to digital data. The following methods are described: the counter method and averaging, time stretching, time-to-amplitude conversion followed by analogue-to-digital conversion, the Vernier method, conversion utilizing tapped delay lines, and interpolation methods. Special attention has been paid to converters utilizing integrated delay lines for digital conversion of TIs, including designs with phase-locked loop and delay-locked loop circuits. This review is illustrated by design examples and contains a comprehensive list of references.
Meta-heuristics Applied to Power Systems

This paper describes a number of real applications of meta-heuristics (in this case, Simulated Annealing) and Genetic Algorithms to Power System problems. The research work was developed in the framework of European projects and industrial contracts and addresses areas as: planning and operation of electrical distribution systems, wind park layout, unit commitment of isolated systems with renewable energy sources and voltage collapse in interconnected systems. The combinatorial nature comes naturally in Power Systems, since most of the decision variables are binary or integer due to technical reasons. On the other hand, a common characteristic to these problems is the presence of technical constraints, which poses difficulties to the application of meta-heuristics, leading to the need of penalty factors in the evaluation functions. The extended abstract also includes feature selection for security analysis using Artificial Neural Networks, a related topic, although not really an application of meta-heuristics. The abstract is organized as follows. Regarding each topic, the corresponding problem is briefly described, followed by the presentation of the approach and, in some cases, a summary of the results. Global conclusions and references complete the extended abstract.