Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Impairment WDM Optical Performance Monitoring for Burst Switched Networks

We present an analytical investigation of a performance monitoring scheme capable of simultaneously monitoring Chromatic Dispersion, Polarization Mode Dispersion and Optical Signal to Noise Ratio at 40 Gbit/s on timescales that are appropriate to optical burst switched networks. This analysis supports previously published experimental results which showed that this technique is scalable to WDM network monitoring.
Workflow Optimization for a Graphic Artist working on large Texture Sets using Virtual Texturing

In this paper we present an approach to optimize the workflow for a graphic artist currently working on high resolution photographs of architectural and archaeological monuments. These photographs are used as texture maps to color the meshes, which are calculated from laser-scanned point clouds corresponding to exactly those monuments. Because a particular region belonging to a monument is covered from several photographs with different colorization, further manual processing of the photos is required. Therefore we developed an application, which generates masks to emphasize regions of the photographs that are used in the final model, to ease the work of the graphic artist. For fast rendering of the model, we took Virtual Texturing into account, and developed an application for fast generation of the Virtual Texture Atlas and the Tile Store. A fast and efficient update of the Tile Store, if a photograph is edited after the final generation of the Virtual Texture Atlas and the Tile Store, the application also provides. The used algorithms are stated as well as the speed-up compared to an existing implementation.
Mining Infrequent Causal Associations in Electronic Health Databases

Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The exclusive causal-leverage was employed to rank the potential causal associations between each of the two selected drugs (i.e., enalapril and pravastatin) and 3,954 recorded symptoms, each of which corresponds to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by our physicians on the project team. The results showed that the number of symptoms considered as real ADRs for enalapril and pravastatin was 8 and 7 out of 10, respectively.
Taxi Queue, Passenger Queue or No Queue? - A Queue Detection and Analysis System using Taxi State Transition

Taxi waiting queues or passenger waiting queues usually reflect the imbalance between taxi supply and demand, which consequently decrease a city’s trac system productivity and commuters’ satisfaction. In this paper, we present a queue detection and analysis system to conduct analytics on both taxi and passenger queues. The system utilizes the event-driven taxi traces and the taxi state transition knowledge to detect queue locations at a coordinate level and subsequently identify 4 di↵erent types of queue context (e.g., only passengers queuing or only taxis queuing). More specifically, it adopts the novel and easy-to-implement algorithms to selectively extract taxi pickup events and their critical features. The extracted taxi pickup locations are then used to detect queue locations, and the extracted critical features are used to infer queue context. The extensive empirical evaluations, which run on daily 12.4 million taxi trace records from nearly 15000 taxis in Singapore, demonstrate the high accuracy and stability of the queue analytics results. Finally, we discuss the real world deployment issues and the gained insights from the queue analysis results.
A simple convergence comparison of Gallager codes under two message-passing schedules

The convergence rate of iterative decoding of Gallager codes on the additive white Gaussian noise (AWGN) channel using the sum-product algorithm (SPA) under the flooding schedule (FS) is compared with that under the turbo-decoding schedule (TDS). Analyses using extrinsic information transfer (EXIT) charts show that TDS exhibits a much faster convergence behavior than FS.
Emotional Agents for Interactive Environments

Interactive environments need believable characters. Their personality and emotions should affect the behavior in order to increase the emotional immersion of the player. In our new model virtual characters (or agents) are equipped with an individual personality and dynamic emotions. Both models interact within the selection of appropriate actions, and are thus central for the exhibited behavior of autonomous agents. The proposed model is application-independent and allows to render a rich set of behaviors for virtual characters in learning environments like Squeak eToys or even distributed 3D spaces like Open Croquet
Code transformation and instruction set extension

The demand for flexible embedded solutions and short time-to-market has led to the development of extensible processors that allow for customization through user-defined instruction set extensions (ISEs). These are usually identified from plain C sources. In this article, we propose a combined exploration of code transformations and ISE identification. The resulting performance of such a combination has been measured on two benchmark suites. Our results demonstrate that combined code transformations and ISEs can yield average performance improvements of 49%. This outperforms ISEs when applied in isolation, and in extreme cases yields a speed-up of 2.85.
Biologically motivated push recovery strategies for a 3D bipedal robot walking in complex environments

Though balancing is a fundamental part of human walking, it has been a challenging topic for bipedal robot. Compared to the versatile strategies of handling disturbances of human, current bipedal robots possess limited skills of managing external disturbances. Among them the capabilities of push recovery and maintaining balance are obviously of prior importance for a bipedal robot to walk in an unknown environment. Existing solutions, such as, capture point, walking phase modification, foot placement estimator and etc, are mostly based on simplifying a bipedal robot as a linear inverted pendulum, which result in omitting the effects of knee and ankle joints, natural dynamics, ground reaction forces and interaction between joints in sagittal and frontal planes in a 3D bipedal robot. To overcome those drawbacks, this paper presents a work that it classifies push types, presents a push detector, and proposes control strategies for managing continuous strong pushes occurring during walking. Based on biological and biomechanical research, more efficient strategies, e. g. , hip and ankle strategy, a bent-knee strategy, can be transferred to control methods for bipeds. Through generating required torques and/or regulating joint positions in order to stabilize bipedal robot, a push recovery controller is proposed in this paper. Simulation results demonstrate that a 3D physically simulated anthropomorphic biped with 21 degrees of freedom can succeed in recovering from a strong push of up to 200 N during walking in different walking scenarios.
Using logistic regression to estimate the number of faulty software modules

Background. The evaluation of the accuracy of an estimation model for software fault-proneness is carried out by using the model with data collected on a set of software modules and classifying the modules in the set as either estimated faulty or estimated non-faulty. This classification usually involves setting a fault-proneness threshold: software modules whose fault-proneness is above that threshold are classified as estimated faulty and the others as estimated non-faulty. The selection of the threshold value is to some extent subjective and arbitrary, and different threshold values may lead to very different results in terms of classification accuracy. Objective. With our proposal, the accuracy of a fault-proneness model can be evaluated without fixing a threshold. Method. We first derive a property of Binary Logistic Regression fault-proneness estimation models. We show that the number of actually faulty software modules in the training set used to build a model is equal to the number of modules estimated faulty in that set, i.e., estimation is perfect on the training set. Then, we use the model on a different set, the test set, and estimate the number of faulty modules. We also estimate the number of faulty modules in the test set by using a more conventional approach with five different fault-proneness thresholds, and we finally compare the estimates with the estimates obtained via our approach. We carried out the empirical validation on a data set from NASA hosted on the PROMISE repository, by using a technique similar to the one used in K-fold cross validation. Results. In the empirical validation we carried out, the approach we propose is able to estimate the number of faulty modules in the test sets better than the threshold-based ones, in a statistically significant way. Conclusions. Our approach seems to have the potential to be practically used to accurately estimate the number of faulty modules without having to set specific fault-proneness thresholds.
K-Cluster Algorithm for Automatic Discovery of Subgoals in Reinforcement Learning

Options have proven to be useful to accelerate agent's learning in many reinforcement learning tasks, determining useful subgoals is a key step for agent to create options. A K-cluster algorithm for automatic discovery of subgoals is presented in this paper. This algorithm can extract subgoals from the trajectories collected online in clustering way. The experiments show that the K-cluster algorithm can find subgoals more efficiently than the diverse density algorithm and that the reinforcement learning with this algorithm outperforms the one with the diverse density algorithm and flat Q-learning
Probabilistic Field Mapping for Product Search

This paper describes our participation in the product search task of the CLEF 2015 LL4IR Lab. Working within a generative language modeling framework, we represent products as semi-structured documents. Our focus is on establishing a probabilistic mapping from query terms to document fields. We present and experimentally compare three alternatives. Our results show that term-specific mapping is beneficial. We also find evidence suggesting that estimating field mapping priors based on historical clicks outperforms the setting where the priors are uniformly distributed.
Psychiatric Aspects of Organ Transplantation

Surgical transplantation of human organs from deceased as well as living donors to sick and dying patients began after the Second World War. Over the past 50 years the transplantation of human organs, tissues and cells has become a worldwide practice which has extended, and greatly enhanced the quality of hundreds of thousands of lives. The field of transplantation medicine provides an important chance for liaison between psychiatric professionals and other transplant physicians and surgeons. The discrepancy between the ever-increasing demand for organs but the decreasing supply makes it important to evaluate and prioritize individuals who are in dire need of the organ. However, this also gives rise to certain ethical questions. The following paper discusses various psychiatric aspects of organ transplantation in general.
On Multigrid Convergence for Quadratic Finite Elements

Quadratic and higher order finite elements are interesting candidates for the numerical solution of (elliptic) partial differential equations (PDEs) due to their improved approximation properties in comparison to linear approaches. While the systems of equations that arise from the discretisation of the underlying PDEs are often solved by iterative schemes like preconditioned Krylow-space methods, multigrid solvers are still rarely used due the higher effort that is associated with the realization of appropriate smoothing and intergrid transfer operators. However, numerical tests indicate that quadratic FEM can provide even better convergence rates than linear finite elements: If m denotes the number of smoothing steps, the convergence rates behave asymptotically like O( 1 m 2 ) in contrast to O( 1 m ) for linear FEM. We prove this new convergence result for quadratic conforming finite elements in a multigrid solver.
Image Edge Detection by Using Rule Based Fuzzy Classifier

─ Fuzzy logic is a key concept of artificial intelligence helps to implement the rule based algorithms that helps to find and highlight the properties of an image by checking the relative pixel values. Image processing is any form of information processing for which both the input and output are images where the images are treated as two-dimensional arrays (i.e. matrices), in which each element of the matrix corresponds to a single pixel in the displayed image. Edge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. In this research work Fuzzy image processing is used for edge detection, which is a collection of fuzzy logic and image processing that understand, represent and process the images as fuzzy sets. The proposed approach is begins by padding the input image and exhaustive scanning of image is done by using the sliding window of 5x5 mask, then pixel values of window are subjected to fuzzy rules designed for horizontal, vertical and diagonal direction, for comparison of pixel values with the adjacent pixels to check, whether the pixel belongs to edge region or non-edge region, an intermediate image is generated as output of fuzzy logic system. First derivative is calculated to fetch the edges from the intermediate image. At the last thinning algorithm is applied to get thin and refined edges. Keywords─ Image processing, Fuzzy logic, Fuzzy image processing, MATLAB, Edge detection, padding, fuzzy rules, fuzzification, de-fuzzification
Visual transcripts

Blackboard-style lecture videos are popular, but learning using existing video player interfaces can be challenging. Viewers cannot consume the lecture material at their own pace, and the content is also difficult to search or skim. For these reasons, some people prefer lecture notes to videos. To address these limitations, we present Visual Transcripts, a readable representation of lecture videos that combines visual information with transcript text. To generate a Visual Transcript, we first segment the visual content of a lecture into discrete visual entities that correspond to equations, figures, or lines of text. Then, we analyze the temporal correspondence between the transcript and visuals to determine how sentences relate to visual entities. Finally, we arrange the text and visuals in a linear layout based on these relationships. We compare our result with a standard video player, and a state-of-the-art interface designed specifically for blackboard-style lecture videos. User evaluation suggests that users prefer our interface for learning and that our interface is effective in helping them browse or search through lecture videos.
Optimistický postoj k životu jako kognitivní styl

V praci se v teoreticke casti zaměřuji na vymezeni kognitivnich stylů, se zvlastnim důrazem na tzv. heuristickou/algoritmickou orientaci, dale na vědecke pojeti optimismu, kde popisuji koncept tzv. dispozicniho optimismu a optimistickeho vysvětlovaciho stylu. Poukazuji take na propojenost optimismu/pesimismu, kognitivniho stylu a způsobu zvladani narocných životnich situaci. Zabývam se i dalsimi aspekty optimismu a pesimismu. Soucasti prace je i strucne vymezeni cirkadiannich rytmů a tzv. diurnalni preference. V empiricke casti prace jsem se zaměřil na zjistěni urovně optimismu/pesimismu, kognitivniho stylu heuristicka/algoritmicka orientace a diurnalni preference u vybraneho výzkumneho souboru, kterým byla skupina 119 vysokoskolských studentů. Na zakladě provedeneho výzkumu bylo zjistěno, že dva testy použivane k urceni miry optimismu (LOT-R a AASQ) v nasich podminkach nekoreluji, tzn. že neměři stejne charakteristiky. Dale se podařilo zjistit, že existuje rozdil mezi optimisty a pesimisty (respondenti rozděleni do skupin na zakladě dispozicniho optimismu, podle výsledků testu LOT-R) v kognitivnim stylu heuristicka/algoritmicka orientace; konkretně že optimiste použivaji vice heuristik při řeseni různých narocných situaci, kdežto pesimiste maji spise tendenci použivat algoritmů. Při rozděleni respondentů na optimisty a pesimisty na zakladě atribuci přicin nepřiznivých udalosti (podle výsledků testu AASQ) nebyl zjistěn rozdil v kognitivnim stylu. Rovněž v otazce vztahu kognitivniho stylu a diurnalni preference se nepodařilo zjistit, že by mezi nimi existoval statisticky významný vztah.
4-2-1997 Spatial Learning for Robot Locialization

Although evolutionary algorithms have been employed to automatically synthesize control and behavior programs for robots and even design the physical structures of the robots, it is impossible for evolution to anticipate the detailed structure of speci c environments that the robot might have to deal with. Robots must thus possess mechanisms to learn and adapt to the environments they encounter. One such mechanism that is of importance to mobile robots is that of spatial learning, i.e., the ability to learn the spatial locations of objects and places in the environment, which would allow them to successfully explore and navigate in a-priori unknown environments. This paper proposes a computational model for the acquisition and use of spatial information that is inspired by the role of the hippocampal formation in animal spatial learning and navigation.
Study on Worm Poisoning Technology

In this paper, concepts of worm poisoning and PoisonWorm are presented and the feasibility of worm poisoning is testified. A propagation model named SIRP Model and PoisonWorm's side-effect on network traffic are given and compared with the classical epidemic Kermack-Mckendrick model. The feasibility and necessity of PoisonWorm and its application are highlighted in an active defense system against Internet worms. In addition, the technology of P2P-based unknown worm detection and signature verification are introduced briefly
Probabilistic Models for the (sub)Tree(s) of Life

The goal of these lectures is to review some mathematical aspects of random tree models used in evolutionary biology to model gene trees or species trees. We start with stochastic models of tree shapes (finite trees without edge lengths), culminating in the $\beta$-family of Aldous' branching models. We next introduce real trees (trees as metric spaces) and show how to study them through their contour, provided they are properly measured and ordered. We then focus on the reduced tree, or coalescent tree, which is the tree spanned by individuals/species alive at the same fixed time. We show how reduced trees, like any compact ultrametric space, can be represented in a simple way via the so-called comb metric. Beautiful examples of random combs include the Kingman coalescent and coalescent point processes. We end up displaying some recent biological applications of coalescent point processes to the inference of species diversification, to conservation biology and to epidemiology.
A Satis£cing Approach to Assigning Vehicles to Targets

Determining which of N -agents are assigned to which of M -tasks is order M in complexity. By requiring individual agents to identify the set of individually satis£cing actions, this complexity remains exponential but can be signi£cantly reduced. We present an algorithm for identifying individually satis£cing choices in a problem where agents must reach a target while avoiding threats. We demonstrate that the algorithm provides some computational savings and produces reasonable assignments.
A Quantitative Analysis of Baitarani Drainage Basin ( Odisha ) Using Geographical Information System

Basin morphometry is a means of numerically analysing or mathematically quantifying different aspects of a drainage basin. In the present study, morphometric analysis of the Baitarani basin has been carried out using earth observation data and geographical information system (GIS) techniques. The morphometric parameters considered for analyses include the linear, areal and relief aspects of the basin. The Baitarani basin covers an area of 10,982 sq km. It is a 6 order drainage basin with mainly dendritic drainage pattern. The mean bifurcation ratio is 4.58 indicating the basin is largely controlled by structure. The basin has medium drainage density of 0.64 per km and is elongated in shape. The length of overland flow values of the basin is 3.13, indicating high relief. The study has strengthened in understanding the hydrological, geological and geomorphological characteristics of the Baitarani basin.
Production of interferon α by human immunodeficiency virus type 1 in human plasmacytoid dendritic cells is dependent on induction of autophagy.

BACKGROUND The mechanisms responsible for interferon α (IFN-α) production by plasmacytoid dendritic cells (pDCs) during human immunodeficiency virus type 1 (HIV-1) infection are unknown. This research examined the roles of Toll-like receptor 7 (TLR7) and autophagy in IFN-α production by pDCs during HIV-1 infection. METHODS pDCs from human peripheral blood mononuclear cells were incubated with infectious or aldrithiol 2 (AT-2)-inactivated HIV-1 or with uridine-rich single-stranded RNA40 (ssRNA40) from the HIV-1 long terminal repeat. IFN-α was quantified by enzyme-linked immunosorbant assay. Autophagic proteins were detected by Western blot, and autophagosomes were identified using immunofluorescent and confocal microscopy. To inhibit autophagy, pDCs were treated with the phosphoinositide-3 kinase inhibitor 3-methyladenine (3-MA) or were transfected with autophagy-related protein 7 or TLR7 small interfering RNA (siRNA). RESULTS Increased levels of IFN-α were present in culture supernatants following 16-hour incubation of pDCs with infectious or AT-2-inactivated HIV-1. Treatment of pDCs with ssRNA40 but not ssRNA41 resulted in high levels of IFN-α. pDCs exposed to HIV-1 gp120, rapamycin, or 3-MA alone failed to induce IFN-α. Pretreatment of pDCs with 3-MA significantly reduced the induction of IFN-α by ssRNA40. Similarly, knock down of autophagy-related protein 7 and TLR7 by use of siRNA significantly reduced the induction of IFN-α by ssRNA40 or HIV-1. CONCLUSIONS These findings demonstrate that IFN-α production by pDCs exposed to infectious or noninfectious HIV-1 and ssRNA40 occurs through induction of autophagy following TLR7 signaling.