Miracle-dz/UniFAR-FTUs huggingface dataset:
The Replica Management System: a scheme for flexible and dynamic replication

The actual gains achieved by replication are a complex function of the number of replicas, the placement of those replicas, the replication protocol, the nature of the transactions performed on the replicas, and the availability and performance characteristics of the machines and networks composing the system. This paper describes the design and implementation of the Replica Management System, which allows a programmer to specify the quality of service required for replica groups in terms of availability and performance. From the quality of service specification, information about the replication protocol to be used, and data about the characteristics of the underlying distributed system, the RMS computes an initial placement and replication level. As machines and communications systems are detected to have failed or recovered, or performance characteristics change, the RMS can be re-invoked to compute an updated mapping of replicas which preserves the desired quality of service. The result is a flexible, dynamic and dependable replication system. <<ETX>>
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.
Demonstration of target vibration estimation in synthetic aperture radar imagery

Synthetic-aperture radar (SAR) can be used to remotely estimate ground target vibrations by exploiting the Doppler in the returned signals. Recent studies suggest that time-frequency signal-processing tools can retrieve the vibration signature from the returned SAR signals. A vibration estimation method based on the fractional Fourier transform (FRFT) was reported earlier and it was tested on simulated SAR data. In this paper, a first-time demonstration of the FRFT-based vibration estimation method is reported using real SAR data collected by the Lynx (Ku-band) SAR system. The vibrating target is an aluminum triangular trihedral with lateral length of 15 inches. The FRFT-based algorithm is shown to successfully retrieve a 3 mm peak-to-peak amplitude, 5 Hz vibration of the target from real SAR data.
Dopamine Modulates the Excitability of Projection Neurons in the Robust Nucleus of the Arcopallium in Adult Zebra Finches

Background The nervous system in songbirds is an accessible system for studying vocal learning and memory in vertebrates. In the song system, the anterior forebrain pathway (AFP) is essential for song learning and the vocal motor pathway (VMP) is necessary for song production. The premotor robust nucleus of the arcopallium (RA) located in the VMP receives input from the AFP. The RA receives dopaminergic innervations from the periaqueductal gray and ventral tegmental area–substantia nigra pars compacta, but the physiological functions of this projection remain unclear. In this study, we investigated the effects of dopamine (DA) on the excitability of projection neurons (PNs) in the RA. Methodology We recorded the electrophysiological changes from neurons in brain slices of male adult zebra finches using a whole-cell recording technique. Conclusions/Significance We found that DA significantly increased the excitability of RA PNs. Furthermore, a D1-like receptor agonist increased the excitability of RA PNs, and a D1-like receptor antagonist suppressed the excitability induced by DA. However, a D2-like receptor agonist had no effect on the excitability of RA PNs. Moreover, the D2-like receptor agonist did not change the excitability induced by the D1 receptor agonist. These findings suggest that DA can significantly increase the excitability of RA PNs and that D1 receptors play the main role in regulating the excitability of RA PNs in response to DA, thereby providing direct evidence toward understanding the mechanism of DA signal mediation by its receptors to modulate the excitability of RA PNs.
Exploring floating stereoscopic driver-car interfaces with wide field-of-view in a mixed reality simulation

In this paper, we propose a floating, multi-layered, wide field-of-view user interface for car drivers. It utilizes stereoscopic depth and focus blurring to highlight items with high priority or urgency. Individual layers are additionally used to separate groups of UI elements according to importance or context. Our work is motivated by two main prospects: a fundamentally changing driver-car interaction and ongoing technology advancements for mixed reality devices. A working prototype has been implemented as part of a custom driving simulation and will be further extended. We plan evaluations in contexts ranging from manual to fully automated driving, providing context-specific suggestions. We want to determine user preferences for layout and prioritization of the UI elements, perceived quality of the interface and effects on driving performance.
Bringing Software Tools to the Web — Architecture Issues and Porting Experience

Due to the popularity of the World Wide Web, many users demand that software tools be brought up and made available in the Web. There are challenging problems we have to solve to meet this demand. This paper discusses the architectural issues and technical decisions involved in porting a stand-alone software tool so that it can run on the Web interactively. We examine the general architectures required for this set-up, and present our experience in bringing a stand-alone tool into the presence of the Web. In particular, we describe the procedure to bring a computation-intensive software reliability analysis tool, CASRE, to execute on the Web. The resulting software, called Web-CASRE, takes advantage of the Tcl/Tk plugin facility to download a Tcl script for its front-end user interface, while communicating with its back-end problem solver. The design decisions regarding communications protocol, security policy, data storage, and consistent computation environment are also discussed in this paper.
Array-based Genome Comparison of Arabidopsis Ecotypes using Hidden Markov Models

Abstract: Arabidopsis thaliana is an important model organism in plant biology with a broad geographic distribution including ecotypes from Africa, America, Asia, and Europe. The natural variation of different ecotypes is expected to be reflected to a substantial degree in their genome sequences. Array comparative genomic hybridization (Array-CGH) can be used to quantify the natural variation of different ecotypes at the DNA level. Besides, such Array-CGH data provides the basics to establish a genome-wide map of DNA copy number variation for different ecotypes. Here, we present a new approach based on Hidden Markov Models (HMMs) to predict copy number variations in Array-CGH experiments. Using this approach, an improved genome-wide characterization of DNA segments with decreased or increased copy numbers is obtained in comparison to the routinely used segMNT algorithm. The software and the data set used in this case study can be downloaded from http://dig.ipk-gatersleben.de/HMMs/ACGH/ACGH.html.
A cognitive radio based Internet access framework for disaster response network deployment

In this paper, we propose a cognitive radio based Internet access framework for disaster response network deployment in challenged environments. The proposed architectural framework is designed to help the existent but partially damaged networks to restore their connectivity and to connect them to the global Internet. This architectural framework provides the basis to develop algorithms and protocols for the future cognitive radio network deployments in challenged environments.
Text-based over-representation analysis of microarray gene lists with annotation bias

A major challenge in microarray data analysis is the functional interpretation of gene lists. A common approach to address this is over-representation analysis (ORA), which uses the hypergeometric test (or its variants) to evaluate whether a particular functionally defined group of genes is represented more than expected by chance within a gene list. Existing applications of ORA have been largely limited to pre-defined terminologies such as GO and KEGG. We report our explorations of whether ORA can be applied to a wider mining of free-text. We found that a hitherto underappreciated feature of experimentally derived gene lists is that the constituents have substantially more annotation associated with them, as they have been researched upon for a longer period of time. This bias, a result of patterns of research activity within the biomedical community, is a major problem for classical hypergeometric test-based ORA approaches, which cannot account for such bias. We have therefore developed three approaches to overcome this bias, and demonstrate their usability in a wide range of published datasets covering different species. A comparison with existing tools that use GO terms suggests that mining PubMed abstracts can reveal additional biological insight that may not be possible by mining pre-defined ontologies alone.
XML-based NLP Tools for Analysing and Annotating Medical Language

We describe the use of a suite of highly flexible XML-based NLP tools in a project for processing and interpreting text in the medical domain. The main aim of the paper is to demonstrate the central role that XML mark-up and XML NLP tools have played in the analysis process and to describe the resultant annotated corpus of MEDLINE abstracts. In addition to the XML tools, we have succeeded in integrating a variety of non-XML 'off the shelf' NLP tools into our pipelines, so that their output is added into the mark-up. We demonstrate the utility of the annotations that result in two ways. First, we investigate how they can be used to improve parse coverage of a hand-crafted grammar that generates logical forms. And second, we investigate how they contribute to automatic lexical semantic acquisition processes.
Controlled release of anti-diabetic drug Gliclazide from poly(caprolactone)/poly(acrylic acid) hydrogels

Drug Gliclazide (Glz) has limited solubility and low bioavailability. In order to obtain a controlled release of this drug and to improve its bioavailability, the drug has been loaded into poly(caprolactone) (PCL)/poly(acrylic acid) (PAAc) hydrogels, prepared by free radical polymerization of acrylic acid in the presence of poly(caprolactone) in acetone medium using azo-isobutyronitrile as initiator and N,N′ methylene bisacrylamide as cross-linking agent. The swelling behaviour of these hydrogels has been investigated in the physiological gastric and intestinal fluids to obtain an optimum composition suitable for delivery of a biologically active compound. The gels were loaded with anti-diabetic drug Glz and a detailed investigation of release of drug has been carried out. Various kinetic models have been applied on the release data. Finally, the Albino wistar rats were treated for Streptozotocin plus nicotinamide – induced diabetes using a Glz-loaded PCL/PAAc hydrogel. The results indicated a fair reduction in the glucose level of rats.
TorWard: Discovery of malicious traffic over Tor

Tor is a popular low-latency anonymous communication system. However, it is currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we design and implement a novel system, TorWard, for the discovery and systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints and allows the investigation to be performed in a sensitive environment such as a university campus. An IDS (Intrusion Detection System) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and effectiveness of TorWard. Our data shows that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), DoS (Denial-of-Service) attack traffic, spam, and others. Around 200 known malware have been identified. To the best of our knowledge, we are the first to perform malicious traffic categorization over Tor.
Compact Equal and Unequal Dual-Frequency Power Dividers Based on Composite Right-/Left-Handed Transmission Lines

A novel approach using fully integrated composite right-/left-handed (CRLH) transmission lines (TLs) is proposed to design equal and unequal dual-frequency Wilkinson power dividers (WPDs) with controllable frequency ratio. Theoretical analysis indicates that the frequency ratio and the power-splitting ratio k2 of the divider can be controlled by changing the element values and the number of the constructing cells of the CRLH TLs. Design formulas are presented to calculate the element values of the cells for a given frequency ratio. Dispersion relation, transmission/reflection coefficient, and Bloch impedance studies show that the Π-shaped unit cells have smaller size than the T-shaped counterparts but the same performance. For demonstration, one equal and one unequal dual-frequency WPDs operating at 2.45 and 5.2 GHz, respectively, are implemented. Measurements show that both the equal and unequal dividers have 17.5% and 26% relative bandwidths at 2.45 and 5.2 GHz, respectively.
Power-law stochastic neighbor embedding

Stochastic neighbor embedding (SNE) aims to transform the observations in high-dimensional space into a low-dimensional space which preserves neighbor identities by minimizing the Kullback-Leibler divergence of the pairwise distributions between two spaces where Gaussian distributions are assumed. Data visualization could be improved by adopting the t-SNE where Student t distribution is used in the low-dimensional space. However, data pairs in the latent space are forced to be squeezed due to the loss of dimensions. This study incorporates the power-law distribution into construction of the p-SNE. Such an unsupervised p-SNE increases the physical forces in neighbor embedding so that the neighbors in the low-dimensional space can be adjusted flexibly to reflect the neighboring in the high-dimensional space. The experiments on three learning tasks illustrate that the manifold or data structure using the proposed p-SNE is preserved in better shape than that using SNE and t-SNE.
Collective memory building in Wikipedia: the case of North African uprisings

Since December 2010, a series of protests and uprisings have shocked North African countries such as Tunisia, Egypt, Libya, Syria, Yemen and more. In this paper, focusing mainly on the Egyptian revolution, we provide evidence of the intense edit activity occurred during these uprisings on the related Wikipedia pages. Thousands of people provided their contribution on the content pages and discussed improvements and disagreements on the associated talk pages as the traumatic events unfolded. We propose to interpret this phenomenon as a process of collective memory building and argue how on Wikipedia this can be studied empirically and quantitatively in real time. We explore and suggest possible directions for future research on collective memory formation of traumatic and controversial events in Wikipedia.
Election Tally Sheets Processing System

In paper based elections, manual tallies at polling station level produce myriads of documents. These documents share a common form-like structure and a reduced vocabulary worldwide. On the other hand, each tally sheet is filled by a different writer and on different countries, different scripts are used. We present a complete document analysis system for electoral tally sheet processing combining state of the art techniques with a new handwriting recognition subprocess based on unsupervised feature discovery with Variational Autoencoders and sequence classification with BLSTM neural networks. The whole system is designed to be script independent and allows a fast and reliable results consolidation process with reduced operational cost.
The nucleolar protein Esf2 interacts directly with the DExD/H box RNA helicase, Dbp8, to stimulate ATP hydrolysis

While 18 putative RNA helicases are involved in ribosome biogenesis in Saccharomyces cerevisiae, their enzymatic properties have remained largely biochemically uncharacterized. To better understand their function, we examined the enzymatic properties of Dpb8, a DExD/H box protein previously shown to be required for the synthesis of the 18S rRNA. As expected for an RNA helicase, we demonstrate that recombinant Dbp8 has ATPase activity in vitro, and that this activity is dependent on an intact ATPase domain. Strikingly, we identify Esf2, a nucleolar putative RNA binding protein, as a binding partner for Dbp8, and show that it enhances Dbp8 ATPase activity by decreasing the KM for ATP. Thus, we have uncovered Esf2 as the first example of a protein co-factor that has a stimulatory effect on a nucleolar RNA helicase. We show that Esf2 can bind to pre-rRNAs and speculate that it may function to bring Dbp8 to the pre-rRNA, thereby both regulating its enzymatic activity and guiding Dbp8 to its site of action.
Functioning Nanomachines Seen in Real-Time in Living Bacteria Using Single-Molecule and Super-Resolution Fluorescence Imaging

Molecular machines are examples of “pre-established” nanotechnology, driving the basic biochemistry of living cells. They encompass an enormous range of function, including fuel generation for chemical processes, transport of molecular components within the cell, cellular mobility, signal transduction and the replication of the genetic code, amongst many others. Much of our understanding of such nanometer length scale machines has come from in vitro studies performed in isolated, artificial conditions. Researchers are now tackling the challenges of studying nanomachines in their native environments. In this review, we outline recent in vivo investigations on nanomachines in model bacterial systems using state-of-the-art genetics technology combined with cutting-edge single-molecule and super-resolution fluorescence microscopy. We conclude that single-molecule and super-resolution fluorescence imaging provide powerful tools for the biochemical, structural and functional characterization of biological nanomachines. The integrative spatial, temporal, and single-molecule data obtained simultaneously from fluorescence imaging open an avenue for systems-level single-molecule cellular biophysics and in vivo biochemistry.
All-values symbolic execution

This paper discusses and exemplifies our ideas on all-values symbolic execution, an alternative strategy to the traditional all-paths style of symbolic execution. All-values symbolic execution focuses on enumerating the (symbolic) values that may derive from the symbolic execution of program statements. It exploits program dependencies to optimize the symbolic execution of those statements that can be executed with the same symbolic inputs on multiple (up to infinite) paths. Although a fully working implementation and a thorough evaluation are yet to come, this paper illustrates with simple, but representative examples that the proposed technique can boost the efficiency of symbolic execution, and suite interesting new applications.
A hybrid network intrusion detection technique using random forests

Intrusion detection is important in network security. Most current network intrusion detection systems (NIDSs) employ either misuse detection or anomaly detection. However, misuse detection cannot detect unknown intrusions, and anomaly detection usually has high false positive rate. To overcome the limitations of both techniques, we incorporate both anomaly and misuse detection into the NIDS. In this paper, we present our framework of the hybrid system. The system combines the misuse detection and anomaly detection components in which the random forests algorithm is applied. We discuss the advantages of the framework and also report our experimental results over the KDD'99 dataset. The results show that the proposed approach can improve the detection performance of the NIDSs, where only anomaly or misuse detection technique is used.
From Monolithic Systems to a Federated e-Learning Cloud System

Pervasive high speed networks and emerging cloud computing concepts offer a new capability and an opportunity to rethink approaches for delivering information services. However, little attention has been paid to how the legacy monolithic systems can be migrated to the cloud computing paradigm. The paper shares our experience and solutions on building a federated cloud infrastructure for E-learning services. The federated cloud system provides the ability to integrate, manage, and use third-party resources from multiple institutions and combines the resources into one infrastructure. We also provide experience and performance data from six month real trace of the system. While the discussion and experiments focus on cloud paradigm with education services, this research can serve as a reference design for other service providers who want to deploy services in the form of cloud computing.
Spectrum Leasing for OFDM-Based Cognitive Radio Networks

In this paper, we propose an Orthogonal Frequency Division Multiplexing (OFDM)-based cooperative communication scheme joint with spectrum leasing. In the proposed scheme, the Primary User (PU) leases some parts of its available resources (in both time and frequency) as a revenue of cooperation to the Secondary Users (SUs) who act as relays for the primary transmission. The optimization of parameters that control the amount of leased resources from the PU to the SU is investigated. An iterative framework is proposed for the PU to find its appropriate relays. Moreover, a dynamic noncooperative game is considered at the relays to compete among themselves for controlling their powers and the Nash Equilibrium (NE) is suggested as the solution of this competition. Analytical results show that the PU achieves a higher data rate by this cooperation process compared with the case where there is no cooperation.