Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Video Mining Based on SNGRLD-rLDA Model

In this paper we introduce a novel probabilistic topic model named rLDA-SNGRLD for motions or activities mining in complex scene. Based on the improvement of rLDA model, we developed SNGRLD algorithm which can inference in real-time with massive video stream data set and mine the video latent motion topics and motion regions online. Experiments prove that the application of this model for detecting and locating abnormal events in complex scene have a good real-time performance and effectiveness.
Using entropy of traffic features to identify bot infected hosts

Botnets are proliferating on the web and are increasingly being used by criminals for data theft, denial of service attacks, spamming and such other activities. Several bot detection approaches have been proposed which can be classified as either host-based or network-based. A hybrid approach which mitigates the disadvantages of the previous two approaches is proposed here. The proposed method aims to identify bots on a single host by looking at the network traffic generated by the host. The detection method is designed for HTTP traffic. A characterization of normal HTTP traffic as well as bot traffic is initially done using features extracted from network packets. A Neural Network Classifier is trained using these traffic features and later used to classify unlabeled traffic as benign or malicious. A normal traffic profile is first used to filter out packets to commonly accessed destinations thereby reducing the workload on the classifier. Stealthy bots which communicate at large time intervals of up to 32 hours are also detected. 120 bots samples were used to evaluate the system. The experimental results demonstrate a high detection rate of 97.4% and a very low false positive rate of 2.5%. The performance of the system is compared with many recent bot detection methods.
An Applicable Method for Evaluation of Web-Learning Platforms Content Quality and Usage

The quality and the quantity of the e-Learning educational content is reflected by its usage. In this paper, old and new performance metrics and a simple performance prediction algorithm, for the measurement of both educational content as exposed by the educators and the usage of such educational material by the learners, are presented. Metrics and algorithms were tested in an experimental system implementation and the scenario examined took place in a Hellenic university. In this experimental system, authors propose a three level course performance evaluation algorithm and confirm both the system's capability and the performance metrics/algorithms success on tracking and following up the e-Learning educational content and usage.
A Study on Dynamic and Static Clustering Based Routing Schemes for Wireless Sensor Networks

A Wireless sensor network is an infrastructure comprised of sensing (measuring), computing, and communication elements that gives an administrator the ability to instrument, observe, and react to events and phenomena in a specified environment. The administrator typically is a civil, governmental, commercial, or industrial entity. The environment can be the physical world, a biological system, or an information technology (IT) framework. Routing is very difficult in wireless sensor network due to a large number of sensor nodes. In wireless sensor network, Routing can be divided into Flat Routing, Hierarchical Routing, Location - aware Routing. Hierarchical Routing can be further divided into two parts: Dynamic and Static Hierarchical Routing or Clustering-based Routing. Dynamic Clustering based protocols are those in which the clusters are formed and diminished dynamically. Static Clustering based Routing Protocols are those in which clusters once formed remain same throughout the network lifetime. In our paper, we discuss all Dynamic and Static Clustering based Routing Protocols and its pros and cons.
Short-Term Intraocular Pressure Elevations after Combined Phacoemulsification and Implantation of Two Trabecular Micro-Bypass Stents: Prednisolone versus Loteprednol

Objective. To compare the effects of prednisolone and of loteprednol after combined phacoemulsification and trabecular micro-bypass stent implantation (phaco-iStent). Methods. Patients who underwent phaco-iStent between April 2013 and November 2014 were identified by retrospective chart review. Postoperatively, they received either prednisolone (n = 38) or loteprednol (n = 58). Baseline data was compared. Primary outcomes including intraocular pressure (IOP) and number of glaucoma medications (NGM) were analyzed at preoperative visit, postoperative day 1, weeks 1-2, weeks 3-4, and months 2-3. Results. Both groups had similar preoperative parameters (p > 0.05). The mean IOP spike occurred at postoperative weeks 1-2 with an increase of 2.21 ± 7.30 mmHg in the loteprednol group and 2.54 ± 9.28 mmHg in the prednisolone group. It decreased by weeks 3-4 in both groups and continued to improve at months 2-3. NGM showed significant reduction (p < 0.0001) after the surgery and remained stable in both groups. No significant group effect or time-group interaction in IOP and NGM evolution was detected (p > 0.05). The proportions of patients needing paracentesis were similar between the two groups. Conclusion. Similar early IOP elevations after combined phaco-iStent occurred with both prednisolone and loteprednol. Facilitated glucocorticoid infusion, altered aqueous humor outflow, and local inflammation may be contributing factors.
Global Trends and Factors Associated with the Illegal Killing of Elephants: A Hierarchical Bayesian Analysis of Carcass Encounter Data

Elephant poaching and the ivory trade remain high on the agenda at meetings of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Well-informed debates require robust estimates of trends, the spatial distribution of poaching, and drivers of poaching. We present an analysis of trends and drivers of an indicator of elephant poaching of all elephant species. The site-based monitoring system known as Monitoring the Illegal Killing of Elephants (MIKE), set up by the 10th Conference of the Parties of CITES in 1997, produces carcass encounter data reported mainly by anti-poaching patrols. Data analyzed were site by year totals of 6,337 carcasses from 66 sites in Africa and Asia from 2002–2009. Analysis of these observational data is a serious challenge to traditional statistical methods because of the opportunistic and non-random nature of patrols, and the heterogeneity across sites. Adopting a Bayesian hierarchical modeling approach, we used the proportion of carcasses that were illegally killed (PIKE) as a poaching index, to estimate the trend and the effects of site- and country-level factors associated with poaching. Important drivers of illegal killing that emerged at country level were poor governance and low levels of human development, and at site level, forest cover and area of the site in regions where human population density is low. After a drop from 2002, PIKE remained fairly constant from 2003 until 2006, after which it increased until 2008. The results for 2009 indicate a decline. Sites with PIKE ranging from the lowest to the highest were identified. The results of the analysis provide a sound information base for scientific evidence-based decision making in the CITES process.
Analysis and control of polytopic uncertain/nonlinear systems in the presence of bounded disturbance inputs

For a general input-output system subjected to disturbance inputs whose magnitudes are uniformly bounded, we introduce the notion of L/sub /spl infin// stability with level of performance /spl gamma/. We present a sufficient condition which guarantees L/sub /spl infin// stability of this system. Furthermore, for a specific class of uncertain/nonlinear systems, characterized by a polytopic condition, this condition results in a bunch of matrix inequalities. An iterative algorithm based on LMIs (linear matrix inequalities) is proposed for minimizing the level of performance. We also investigate the use of linear state feedback controllers to minimize the level of performance and illustrate our results with a nontrivial example.
Optimization of grid-tied distributed microgrid system with EV charging facility for the stadiums of Bangladesh

Photovoltaic system and wind power are the bright future of hybrid distributed renewable power generation for dreaming a decontaminated green world. Modern world's enhanced commercial activities and rapid industrialization are increasing the peak of electricity demand in a significant way. Integration of battery operated vehicles and upgraded automation technologies to conventional power systems are also consuming large amount of power from grids. On the verge of producing clean energy, the civilized world has all the reasons to generate power from hybrid power systems. Coping with the technological advancement and obsolescence, Bangladesh is also progressing to extract power from existing renewable energy resources and their integration to the national grid. To assist country's distributed generation plan, installation of photovoltaic-wind power based hybrid systems with charging facility for Electric vehicles in major stadiums in Bangladesh is proposed in this paper. Cost analysis is executed in HOMER. Proposals validity and usefulness both are also explicitly discussed.
PD01-06 PROSPECTIVE CLINICAL STUDY ON SUPERPULSE THULIUM FIBER LASER: INITIAL ANALYSIS OF OPTIMAL LASER SETTINGS

INTRODUCTION AND OBJECTIVES: Recently, SuperPulse Thulium (Tm) fiber laser (NPO IRE-Polus) with wavelength of 1.94 µm was introduced as a promising lithotripsy tool. In preclinical study, we have found its efficacy to be higher than the leading Ho:YAG laser for fragmentation (x1.7 times) and dusting (x2.3 to 4 times) for equal energy and repetition rate settings. In this work, we report results of the on-going clinical study aimed at comprehensive evaluation of safety and efficacy of the new laser. METHODS: The SuperPulse Tm fiber laser device has been cleared for clinical use by the Ministry of Health of Russian Federation and the protocol has been approved by the institutional Ethical Review committee. We prospectively analyzed 268 patients (173 kidney, 80 ureteral, 15 bladder stones) who underwent SuperPulse Tm fiber laser lithotripsy. Stone size, its density, operative and laser on time (LOT) were measured. Based on surgeon's feedback, retropulsion and intraoperative visibility were assessed. RESULTS: For kidney stones, the average size was 11.4 mm (7–25 mm), for ureteral stones 7.6 mm (3–18 mm), for bladder stones 22.2 mm (9–36 mm). The average stone density for kidney stones was 987 HU (330–1960 HU), for ureteral stones 995 HU (460–1700 HU), for bladder stones 980 HU (860–1050 HU). The average time to complete renal stone fragmentation (to dust or small fragments) was 27.2 min, for ureteral stones 17.1 min, for bladder stones 19 min. LOT was 24.3; 12.7 and 14.5 min for kidney, ureter and bladder stones, respectively. The retropulsion was insignificant in all cases with energy level less than 0.5 J. Visibility was estimated as optimal by surgeons in most cases. Fragmentation mode with energy level 2 to 5J and repetition rate 5-10 Hz was most suitable for bladder stone disintegration. Overall, the settings of 0.1-0.2J/15-30W; 0.2-0.5J/10-15W and 2-5J/30-50W were identified as optimal for kidney (dusting), ureter (dusting and fragmentation), and bladder (fragmentation) stones, respectively. CONCLUSIONS: Based on the analysis of data on 268 subjects available today, the SuperPulse Tm fiber laser is safe and efficacious for all types of urinary stones in all relevant anatomic locations. Source of Funding: None
A discrete event simulation model for reliability modeling of a chemical plant

This paper discusses a discrete event simulation model developed to identify and understand the impact of different failures on the overall production capabilities in a chemical plant. The model will be used to understand key equipment components that contribute towards maximum production loss and to analyze the impact of a change policy on production losses. A change policy can be classified in terms of new equipment installation or increasing the stock level for the failure prone components. In this paper, we present the approach used and some preliminary results obtained from available data.
Adaptive High-End Microprocessor for Power-Performance Efficiency

Microprocessor development costs are considerably high. To minimize these costs, manufacturers produce a single design that better satisfies, in average, a wide range of applications. Nevertheless, as applications have different characteristics and users have different demands, this single design is suboptimal in many situations. As a consequence there is a need to design microprocessors that are "flexible" and adapt to the different scenarios. This paper presents a simple multiple parameter selection algorithm to achieve high power-performance efficiency gains for each application. The proposed algorithm is tested using detailed simulation and a set of different applications from multimedia, scientific and database workloads. The experimental results show that when the selection algorithm is applied to a baseline configuration optimized for the corresponding workload, the efficiency gains are up to 30%. When the baseline used is optimized for a different workload then the gains may go up by an order of magnitude. The effectiveness of the simple selection algorithm with the proposed optimizations is impressive as the efficiency gains obtained represent 96% of the gains obtained with an ideal parameter selection algorithm
Designing Ranking Systems for Hotels on Travel Search Engines to Enhance User Experience

Information seeking in an online shopping environment is different from classical relevance-based information retrieval. In this paper, we focus on understanding how humans seek information and make economic decisions, when interacting with an array of choices in an online shopping environment. Our study is instantiated on a unique dataset of US hotel reservations from Travelocity.com. Current travel search engines display results with only rudimentary interfaces by using a single ranking criterion. This largely ignores consumers’ multi-dimensional preferences and is constrained by human cognitive limitations towards multiple and diverse online data sources. This paper proposes to improve the search interface using an inter-disciplinary approach. It combines text mining, image classification, social geo-tagging and field experiments with structural modeling techniques, and designs an interface whereby each hotel of interest is ranked according to its average consumer surplus on the travel search site. This system would display the hotels with the “best value for money” for a given consumer at the top of the computer screen during the travel search process in response to a query. The final outcome based on several user studies shows that our proposed ranking system outperforms the existing baselines. This suggests that our inter-disciplinary approach has the potential to enhance user search experience for information seeking in the context of economic decision-making.
CrowdGrader: Crowdsourcing the Evaluation of Homework Assignments

Crowdsourcing offers a practical method for ranking and scoring large amounts of items. To investigate the algorithms and incentives that can be used in crowdsourcing quality evaluations, we built CrowdGrader, a tool that lets students submit and collaboratively grade solutions to homework assignments. We present the algorithms and techniques used in CrowdGrader, and we describe our results and experience in using the tool for several computer-science assignments. CrowdGrader combines the student-provided grades into a consensus grade for each submission using a novel crowdsourcing algorithm that relies on a reputation system. The algorithm iterativerly refines inter-dependent estimates of the consensus grades, and of the grading accuracy of each student. On synthetic data, the algorithm performs better than alternatives not based on reputation. On our preliminary experimental data, the performance seems dependent on the nature of review errors, with errors that can be ascribed to the reviewer being more tractable than those arising from random external events. To provide an incentive for reviewers, the grade each student receives in an assignment is a combination of the consensus grade received by their submissions, and of a reviewing grade capturing their reviewing effort and accuracy. This incentive worked well in practice.
Linearization of boundary conditions in solving problems by the methods of computational aerodynamics

To simplify the calculations of flow about wing with different possible deflections of aerodynamic surfaces, the algorithm with linearization of a boundary condition of impermeability is offered and implemented. The solution of the Euler equations is obtained by time marching to a steady state. The computational grid for the aerodynamic configuration is made in the variant with surfaces without deflection but when setting a normal vector to the surface it is necessary to take into account not only the direction of the normal towards the local surface but also an angle of rotation (or deflection) of this part of the surface for the calculated variant.
Social capital in Brazilian wine industry networks

The aim of this study is to analyze the role of social capital in the conversion of collaboration in a productive force and, consequently, the improvement of competitiveness of two Brazilian wine industry networks: Aprovale and Aprobelo. In a previous study, we concluded that there were statistical significant differences between social capital and competitiveness dimensions. In other words, this means that both levels of social capital and competitiveness differ significantly from one network to another. In this paper, we sought to analyze the results found in the quantitative phase in a qualitativedescriptive way. The main results pointed out that, despite economic, human and intellectual difficulties, a network can raise significant levels of social capital, since the network has shared goals and values.
Cell Type-Specific Sexual Dimorphism in Rat Pituitary Gene Expression During Maturation 1

The most obvious functional differences between mammalian males and females are related to the control of reproductive physiology and include patterns of GnRH and gonadotropin release, the timing of puberty, sexual and social behavior, and the regulation of food intake and body weight. Using the rat as the best-studied mammalian model for maturation, we examined the expression of major anterior pituitary genes in five secretory cell types of developing males and females. Corticotrophs show comparable Pomc profiles in both sexes, with the highest expression occurring during the infantile period. Somatotrophs and lactotrophs also exhibit no difference in Gh1 and Prl profiles during embryonic to juvenile age but show the amplification of Prl expression in females and Gh1 expression in males during peripubertal and postpubertal ages. Gonadotrophs exhibit highly synchronized Lhb, Fshb, Cga, and Gnrhr expression in both sexes, but the peak of expression occurs during the infantile period in females and at the end of the juvenile period in males. Thyrotrophs also show different developmental Tshb profiles, which are synchronized with the expression of gonadotroph genes in males but not in females. These results indicate the lack of influence of sex on Pomc expression and the presence of two patterns of sexual dimorphism in the expression of other pituitary genes: a time shift in the peak expression during postnatal development, most likely reflecting the perinatal sexspecific brain differentiation, and modulation of the amplitude of expression during late development, which is secondary to the establishment of the hypothalamic-pituitary-gonadal and -thyroid axes. Cga, Fshb, Gh1, Gnrhr, Lhb, Pomc, Prl, Tshb
FAULT DETECTION AND ISOLATION : A HYBRID APPROACH

Modern computers and computing techniques allow for the rapid processing of data and permit the development of real-time fault detection and isolation (FDI) tools. For nuclear power plants, these tools are designed to aid the operator by first detecting and then isolating system faults. This paper introduces an original methodology for FDI merging analytical redundancy and neural network techniques, and it presents an overview of the computer-based development and testing methodology.
On Studying the Effectiveness of Extended Finite State Machine Based Test Selection Criteria

Many test selection criteria are considered for the derivation of functional tests for reactive systems and protocols modeled using formal techniques such as Extended Finite State Machines (EFSMs). In this paper, we consider three known EFSM specifications and analytically compare the effectiveness of many EFSM test suites. The assessment is conducted using EFSM mutants of these specifications, namely, EFSM mutants with single and double transfer faults. Test selection is based on deriving single transfer fault, double transfer faults, All-Transitions, Transition Tour, All-Predicates, and All-Uses of context variables test suites from the given EFSM specifications and their corresponding flow-graph representations. An order between these test suites (in terms of fault coverage) is established for each considered type of faults. Data flow scores of the best performing test suites are also determined. Further, an initial assessment of test suites randomly generated from the considered EFSM specifications is reported.
A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet nonsinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step, which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. This paper takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. The torque and the speed control scheme both integrate two neural blocks, one dedicated for optimal-current calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.
Hand segmentation for gesture recognition in EGO-vision

Portable devices for first-person camera views will play a central role in future interactive systems. One necessary step for feasible human-computer guided activities is gesture recognition, preceded by a reliable hand segmentation from egocentric vision. In this work we provide a novel hand segmentation algorithm based on Random Forest superpixel classification that integrates light, time and space consistency. We also propose a gesture recognition method based Exemplar SVMs since it requires a only small set of positive sampels, hence it is well suitable for the egocentric video applications. Furthermore, this method is enhanced by using segmented images instead of full frames during test phase. Experimental results show that our hand segmentation algorithm outperforms the state-of-the-art approaches and improves the gesture recognition accuracy on both the publicly available EDSH dataset and our dataset designed for cultural heritage applications.