Miracle-dz/UniFAR-FTUs huggingface dataset:
Using extra-topical user preferences to improve web -based metasearch

When searching the web, a user strives to find useful documents. Web search engines have been shown to have relatively low coverage, as well as other problems, that limit their ability to return useful documents. One solution is use of a metasearch engine: a tool that sends user queries to multiple search engines and combines the results to increase coverage. Unfortunately, a metasearch engine also has problems, mostly due to the lack of direct control of the underlying search engines, that limit its ability to locate and identify useful results. To improve the ability of users to find useful results when searching the web, we present the architecture of a preference-based metasearch engine Inquirus 2, which utilizes explicit user preferences in the form of a category, such as “personal homepages” or “research papers”. Inquirus 2 demonstrates five architectural improvements that enhance the ability to locate and identify useful documents, and to increase performance. The architectural improvements are: an incremental user interface, need-based source selection, source and category-specific query modification, selective downloading of results, and need-based scoring. A user study was performed to measure the effectiveness of the architectural components and to improve our understanding of user judgments of usefulness as related to topical relevance and document category. This user study demonstrated a bounding relation between the levels of user judgments of topical relevance and usefulness, as well as confirming the effectiveness of our architecture. We also present the Query Modification Learning Procedure (QMLP), a procedure that automatically learns category-specific and source-specific query modifications, along with experimental results confirming the effectiveness of the procedure.
Linguistic Steganography Detection Based on Perplexity

Linguistic steganography is a technique for hiding information into text carriers. Most of the previous work on linguistic steganography was focused on steganography and there were few researches on steganalysis. Research on attacking methods against linguistic steganography plays an important role in information security (IS) area. In this paper, a linguistic steganography detection algorithm based on perplexity is presented. An experiment to detect text segments generated by linguistic steganography system NICETEXT is carried out. The result of our experiment is pretty promising. The experimental accuracy of our method on classification of generated text and normal text exceeds 99% when text size is larger than 400 bytes. Even for sentences, the experimental accuracy exceeds 85%.
Apprentissage de la structure des processus de décision markoviens factorisés pour l'apprentissage par renforcement

Des algorithmes de planification recents issus de la theorie de la decision sont capables de trouver des politiques optimales ou quasi-optimales sur des problemes de grande taille en utilisant le formalisme des processus de decision markoviens factorises (FMDPs). Cependant, ces algorithmes ont besoin d’une connaissance a priori de la structure des problemes qu’ils resolvent. Dans cette contribution, nous proposons SDYNA , un cadre general pour traiter des problemes d’apprentissage par renforcement (A/R) de grande taille par essais et erreur et sans connaissance a priori de leur structure. SDYNA integre des algorithmes incrementaux de planification tires des FMDPs avec des techniques d’apprentissage supervise qui construisent une representation structuree du probleme. Nous decrivons en particulier SPITI , une instance de SDYNA , qui utilise un algorithme d’induction incrementale d’arbres de decision pour apprendre la structure d’un probleme et une version incrementale de l’algorithme Structured Value Iteration pour effectuer la planification. Nous montrons que SPITI construit une representation factorisee d’un probleme d’A/R et ameliore la politique plus rapidement qu’un algorithme tabulaire en exploitant la propriete de generalisation des algorithmes d’induction d’arbres de decision.
Enhancement of microtubules in EM tomography

The interpretation of the EM tomography of microtubules is challenging due to the low SNR and low contrast of the volume data. Therefore, image enhancement is crucial for the subsequent segmentation and structural analysis of microtubules. In this paper, we propose a model based 3D image enhancement approach by combining transform domain technique and spatial domain techniques in three consecutive steps. The enhancement starts with an anisotropic invariant wavelet transform to effectively enhance the elongated features, followed by a 3D shape filter via eigen analysis to capture the local geometric properties of the tubular structure. The enhancement ends with a coherence enhancing diffusion to complete the interruptions along the microtubules. The contribution of this work is that we have tailored and improved each of the above techniques to exploit the unique geometric and photometric properties of microtubules. Experimental results indicate that our proposed approach has excellent performance in noise removal and enhancement of the tomography volume.
Microbes, cables, and an electrical touch.

In nature, highly efficient and diverse consortia of microbes cycle carbon and other elements while generating energy for growth. Driving these reactions are organisms with the ability to extract electrons from the chemical substrates and transfer them to insoluble and soluble electron acceptors. One bacterial group in particular, Geobacter spp. , can couple their respiratory metabolism to the reduction of insoluble minerals, such as iron and manganese oxides, and soluble toxic metals such as uranium. Key to these activities is the ability of the cells to transfer respiratory electrons extracellularly using an electroactive cell envelope containing abundant metalloproteins, including c-cytochromes, and conductive protein appendages or pili (known as nanowires). Thus, in addition to been ecological drivers of the cycling of carbon and metals in nature, these organisms show promise for the bioremediation of environments impacted with toxic metals. The electrical activity of Geobacter can also be mimicked in electrochemical reactors equipped with an electrode poised at a metabolically oxidizing potential, so that the electrode functions as an unlimited sink of electrons to drive the oxidation of electron donors and support cell growth. Electrochemical reactors are promising for the treatments of agricultural, industrial, and human wastes, and the electroactivity of these microbes can be used to develop materials and devices for bioenergy and bioremediation applications.
Big Social Data and Political Sentiment: The Tweet Stream during the UK General Election 2015 Campaign

The General Election for the 56th United Kingdom Parliament was held on 7 May 2015. Tweets related to UK politics, not only those with the specific hashtag "#GE2015", have been collected in the period between March 1 and May 31, 2015. The resulting dataset contains over 28 million tweets for a total of 118 GB in uncompressed format or 15 GB in compressed format. This study describes the method that was used to collect the tweets and presents some analysis, including a political sentiment index, and outlines interesting research directions on Big Social Data based on Twitter microblogging.
Trajectory estimation of a moving target from UWB ranging measurements

In this master thesis we reconstruct position and movement of a blind target node in a realistic environment based on ranging measurements from a set of fixed anchor nodes. We propose a method for trajectory estimation of a moving node based on minimizing residual sum defined as the difference between a reported and the actual distance from the anchor nodes. We devised extensive and complex outdoor as well as indoor experiments with exploratory data analysis and interpretation of the results. Our findings show that the method achieves sub one metre accuracy in an outdoor environment and a slight improvement over existing point based localization system in an indoor environment.
An Exploratory Study of Variations in Exposure to Environmental Tobacco Smoke in the United States

There is considerable interest in assessing exposure to environmental tobacco smoke (ETS) and in understanding the factors that affect exposure at various venues. The impact of these complex factors can be researched only if monitoring studies are carefully designed. Prior work by Jenkins et al. gathered personal monitor and diary data from 1,564 nonsmokers in 16 metropolitan areas of the United States and compared workplace exposures to ETS with exposures away from work. In this study, these data were probed further to examine (1) the correspondence between work and away‐from‐work exposure concentrations of ETS; (2) the variability in exposure concentration levels across cities; and (3) the association of ETS exposure concentrations with select socioeconomic, occupation, and lifestyle variables. The results indicate (1) at the population level, there was a positive association between ETS concentrations at the work and away‐from‐work environments; (2) exposure concentration levels across the 16 cities under consideration were highly variable; and (3) exposure concentration levels were significantly associated with occupation, education, household income, age, and dietary factors. Workplace smoking restrictions were associated with low ETS concentration levels at work as well as away from work. Generally, the same cities that exhibited either lower or higher away‐from‐work exposure concentration levels also showed lower or higher work exposure concentration levels. The observations suggest that similar avoidance characteristics as well as socioeconomic and other lifestyle factors that affect exposure to ETS may have been in operation in both away‐from‐work and work settings.
Circularly polarized double- and plate-loop antennas with a single feed

A double-loop (DL) antenna has been investigated to radiate a tilted beam of circular polarization [1]. A tilted beam is formed by superimposing the fields from the two loops. Note that the inner and outer loops have one- and two-wavelength circumferences, and radiate axial and conical beams, respectively.
Improving the Efficiency of Water Management System in Biomass Power Plant Using Cyber-Physical Cloud Computing

: Optimized water management strategies are among the most crucial concerns in a biomass power plant. Especially during the times of water scarcity, an improper water supply planning and operation result in a detrimental power generation or shut down processes. This paper aims to propose the cyber-physical cloud computing (CPCC), a mechanism of analysis and control physical process using cloud-based framework, in order to improve the efficiency of water management system in a biomass power plant. In this study, a 9.9 MW biomass power plant is implemented to investigate the performance of CPCC. The architecture of the proposed system consists of three physical tiers. The first tier is a tier of physical devices included with pressure, flow, pH sensors, water pumps and valves that are responsible for detecting the physical data and interacting with the water production process. The second tier is an edge computing device which functions as a controller, embedded server, data storage, gateway and switch to manage all operational tasks in an intranet area. The third tier is a cloud computing system which enables big data applications such as online monitoring and visualization of process operation, adaptive filtration fouling control, consuming water and total water cost analysis. The results validate the effectiveness of the proposed system as the ability of an adaptive fouling control system to adjust backwash scheduling and chemical dosing so that achieving the target of purified water quality even during the fluctuation of raw water qualities. Subsequently, the water treatment system can achieve the capability for optimal total water cost operations under a target permeate flow rate and the percent of the recovery. Significantly, the data analytics in the CPCC reflect current operational requirements of water under local climate conditions, likewise contribute to the practical solution for sustainable water resource planning in the energy production.
Data collection, communications and processing in the Sumatran GPS array (SuGAr)

The Sumatran GPS array spans approximately 1300 km along the West coast of Sumatra, Indonesia. Almost 40 monitoring stations periodically collect high accuracy continuous GPS readings, and convey these to a central server for post-processing in Singapore. Solutions from the array data are used to provide precise measurements of the deformation of the earth's surface over large distances: in effect allowing the tracking of tectonic movements, and aiding in the understanding of seismic events in this highly active area of the earth's crust. Invaluable SuGAr-provided scientific data has been published over recent years, however this paper will discuss the communications and computer engineering architecture and evolution of this large sensing network, rather than the tectonic meaning of the data
Exploring video streams using slit-tear visualizations

Video slicing---a variant of slit scanning in photography---extracts a scan line from a video frame and successively adds that line to a composite image over time. The composite image becomes a time line, where its visual patterns reflect changes in a particular area of the video stream. We extend this idea of video slicing by allowing users to draw marks anywhere on the source video to capture areas of interest. These marks, which we call slit-tears, are used in place of a scan line, and the resulting composite timeline image provides a much richer visualization of the video data. Depending on how tears are placed, they can accentuate motion, small changes, directional movement, and relational patterns.
Behavior Retrieval for Robot Control in a Unified CBR Hybrid Planner

This research examines a hybrid planner for a real-world mobile robot delivering messages in an office environment. The overall project, RUPART, uses "unified CBR" to combine behavior-based control with high-level case-based planning, using a single similarity metric to retrieve both behavior cases and plan cases. This paper focuses on case-based reasoning for behavior cases. Each behavior case describes a set of behaviors targeted for a particular environment. We explore features used to index behavior cases, as well as adaptation and learning methods. This project is incomplete at this time, and thorough evaluation, while underway, is not complete.
Group Enclosing Queries

Given a set of points P and a query set Q, a group enclosing query (Geq) fetches the point p* ∈ P such that the maximum distance of p* to all points in Q is minimized. This problem is equivalent to the Min-Max case (minimizing the maximum distance) of aggregate nearest neighbor queries for spatial databases. This work first designs a new exact solution by exploring new geometric insights, such as the minimum enclosing ball, the convex hull, and the furthest voronoi diagram of the query group. To further reduce the query cost, especially when the dimensionality increases, we turn to approximation algorithms. Our main approximation algorithm has a worst case √2-approximation ratio if one can find the exact nearest neighbor of a point. In practice, its approximation ratio never exceeds 1.05 for a large number of data sets up to six dimensions. We also discuss how to extend it to higher dimensions (up to 74 in our experiment) and show that it still maintains a very good approximation quality (still close to 1) and low query cost. In fixed dimensions, we extend the √2-approximation algorithm to get a (1 + ε)-approximate solution for the Geq problem. Both approximation algorithms have O(log N + M) query cost in any fixed dimension, where N and M are the sizes of the data set P and query group Q. Extensive experiments on both synthetic and real data sets, up to 10 million points and 74 dimensions, confirm the efficiency, effectiveness, and scalability of the proposed algorithms, especially their significant improvement over the state-of-the-art method.
Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources

In this paper we examine the issue of optimizing disk usage and of scheduling large-scale scientific workflows onto distributed resources where the workflows are data- intensive, requiring large amounts of data storage, and where the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer required and we schedule the workflows in a way that assures that the amount of data required and generated by the workflow fits onto the individual resources. For a workflow used by gravitational- wave physicists, we were able to improve the amount of storage required by the workflow by up to 57 %. We also designed an algorithm that can not only find feasible solutions for workflow task assignment to resources in disk- space constrained environments, but can also improve the overall workflow performance.
Branched-Chain Amino Acid Transport in Cytoplasmic Membranes of Leuconostoc mesenteroides subsp. dextranicum CNRZ 1273

Membrane vesicles of Leuconostoc mesenteroides subsp. dextranicum fused with proteoliposomes prepared from Escherichia coli phospholipids containing beef heart cytochrome c oxidase were used to study the transport of branched-chain amino acids in a strain isolated from a raw milk cheese. At a medium pH of 6.0, oxidation of an electron donor system comprising ascorbate, N,N,N′,N′-tetramethyl-p-phenylenediamine, and horse heart cytochrome c resulted in a membrane potential (Δψ) of −60 mV, a pH gradient of −36 mV, and an l-leucine accumulation of 76-fold (ΔμLeu/F = 108 mV). Leucine uptake in hybrid membranes in which a Δψ, ΔpH, sodium ion gradient, or a combination of these was imposed artificially revealed that both components of the proton motive force (Δp) could drive leucine uptake but that a chemical sodium gradient could not. Kinetic analysis of leucine (valine) transport indicated three secondary transport systems with Kt values of 1.7 (0.8) mM, 4.3 (5.9) μM, and 65 (29) nM, respectively. l-Leucine transport via the high-affinity leucine transport system (Kt = 4.3 μM) was competitively inhibited by l-valine and l-isoleucine (Ki and Kt values were similar), demonstrating that the transport system translocates branched-chain amino acids. Similar studies with these hybrid membranes indicated the presence of high-affinity secondary transport systems for 10 other amino acids.
Identity of Leu-19 (CD56) leukocyte differentiation antigen and neural cell adhesion molecule

Neural cell adhesion molecule (N-CAM) is a membrane glycoprotein expressed on neural and muscle tissues that is involved in homotypic adhesive interactions. We have demonstrated that N-CAM also is expressed on hematopoietic cells, and is recognized by the anti-Leu-19 mAb. Leu-19 is preferentially expressed on NK cells and T lymphocytes that mediate MHC-unrestricted cytotoxicity, but is also present on some myeloid leukemia cell lines. On NK cells, T cells, the KG1a.5 hematopoietic cell line, and a neuroblastoma cell line, Leu-19 is a approximately 140-kD polypeptide with N-linked carbohydrates and abundant sialic acid residues. Sequential immunoprecipitation and peptide mapping demonstrated that the Leu-19 and N-CAM molecules expressed on leukocyte and neuroblastoma cell lines are similar structures. These findings suggest that the Leu-19 antigen on leukocytes may be involved in cell adhesion, analogous to the function on N-CAM on neural cells.
AI analysis patterns as UML meta-model constructs

In this article, we investigate the use of the OO computational paradigm for the formulation of knowledge model patterns as OO analysis patterns. We seek to take advantage of research on design pattern specification, aimed at modelling patterns by means of structural and behavioural "meta-level" constraints, introducing appropriate modifications into the UML. We illustrate our argument with the formulation of an OO "assessment pattern" in analogy to the well known "assessment task template".
Fast perfect-information leader-election protocols with linear immunity

AbstractIn this paper we develop a leader election protocolP with the following features:1.The protocol rums in theperfect information model: Every step taken by a player is visible to all others.2. It haslinear immunity: IfP is run byn players and a coalition ofc1n players deviates from the protocol, attempting to have one of them elected, their probability of success is <1-c2, wherec1,c2>0 are absolute constants.3. It isfast: The running time ofP is polylogarithmic inn, the number of players. A previous protocol by Alon and Naor achieving linear immunity in the perfect information model has a linear time complexity. The main ingredient of our protocol is areduction subprotocol. This is a way forn players to elect a subset of themselves which has the following property. Assume that up toen of the players are bad and try to have as many of them elected to the subset. Then with high probability, the fraction of bad players among the elected ones will not exceede in a significant way. The existence of such a reduction protocol is first established by a probabilistic argument. Later an explicit construction is provided which is based on the spectral properties of Ramanujan graphs.
The Self-Paced Graz Brain-Computer Interface: Methods and Applications

We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.
Transport Layer Security (TLS) Renegotiation Indication Extension

SSL and TLS renegotiation are vulnerable to an attack in which the attacker forms a TLS connection with the target server, injects content of his choice, and then splices in a new TLS connection from a client. The server treats the client's initial TLS handshake as a renegotiation and thus believes that the initial data transmitted by the attacker is from the same entity as the subsequent client data. This draft defines a TLS extension to cryptographically tie renegotiations to the TLS connections they are being performed over, thus preventing this attack.