Miracle-dz/UniFAR-FTUs huggingface dataset:
Gas Exchanges and Dehydration in Different Intensities of Conditioning in Tifton 85 Bermudagrass: Nutritional Value during Hay Storage

The present study aimed at evaluating the intensity of Tifton 85 conditioning using a mower conditioner with free-swinging flail fingers and storage times on dehydration curve, fungi presence, nutritional value and in vitro digestibility of Tifton 85 bermudagrass hay dry matter (DM). The dehydration curve was determined in the whole plant for ten times until the baling. The zero time corresponded to the plant before cutting, which occurred at 11:00 and the other collections were carried out at 8:00, 10:00, 14:00, and 16:00. The experimental design was randomised blocks with two intensities of conditioning (high and low) and ten sampling times, with five replications. The high and low intensities related to adjusting the deflector plate of the free iron fingers (8 and 18 cm). In order to determine gas exchanges during Tifton 85 bermudagrass dehydration, there were evaluations of mature leaves, which were placed in the upper middle third of each branch before the cutting, at every hour for 4 hours. A portable gas analyser was used by an infrared IRGA (6400xt). The analysed variables were photosynthesis (A), stomatal conductance (gs), internal CO2 concentration (Ci), transpiration (T), water use efficiency (WUE), and intrinsic water use efficiency (WUEi). In the second part of this study, the nutritional value of Tifton 85 hay was evaluated, so randomised blocks were designed in a split plot through time, with two treatments placed in the following plots: high and low intensity of cutting and five different time points as subplots: cutting (additional treatment), baling and after 30, 60, and 90 days of storage. Subsequently, fungi that were in green plants as well as hay were determined and samples were collected from the grass at the cutting period, during baling, and after 30, 60, and 90 days of storage. It was observed that Tifton 85 bermudagrass dehydration occurred within 49 hours, so this was considered the best time for drying hay. Gas exchanges were more intense before cutting, although after cutting they decreased until ceasing within 4 hours. The lowest values of acid detergent insoluble nitrogen were obtained with low conditioning intensity after 30 days of storage, 64.8 g/kg DM. The in vitro dry matter of Tifton 85 bermudagrass did not differ among the storage times or the conditioning intensities. There was no fungi present in the samples collected during the storage period up to 90 days after dehydration, with less than 30 colony forming units found on plate counting. The use of mower conditioners in different intensities of injury did not speed up the dehydration time of Tifton 85.
In vivo induction of lymphokine-activated killer cells by interleukin-2 splenic artery perfusion in advanced malignancy.

In an effort to stimulate in vivo LAK cell activity at relatively nontoxic doses, 20 patients with advanced metastatic malignancy (13 renal cell carcinoma, 6 melanoma, 1 lymphoma) were treated with recombinant human interleukin-2 (IL-2) by continuous 5-day splenic artery perfusion using the femoral approach. Two treatment cycles were administered 3 weeks apart; IL-2 doses ranged from 1.5-4 x 10(4) Cetus units/kg/day. Peripheral blood lymphocyte cytotoxicity in a 4-h 51Cr release assay was measured using as tumor cell targets K562 for natural killer (NK) activity, Daudi for LAK, and Daudi plus in vitro IL-2 for inducible LAK (I-LAK). For the 20 patients, an increase in mean peak percent cytotoxicity from pretreatment levels was seen for NK (36% to 53%), LAK (8% to 37%) and I-LAK (20% to 53%) activity, all significant at P = 0.001. On day 43, 16 days after completing the second cycle of treatment, NK activity remained elevated at 47% and I-LAK at 40% (P = 0.008 and 0.01, respectively). Lymphocyte phenotype analysis by flow cytometry demonstrated increases from pretreatment levels in Leu 11+ (13 to 23%), Leu 19+ (10 to 21%), Leu 11+ 19+ (7 to 17%), IL-2r+ (4 to 17%), and HLA-DR+ (12 to 25%) subsets, all significant at P less than or equal to 0.01. Dose effect was studied at 3 dose levels: 1.5, 3, and 4 x 10(4) Cetus units/kg/day. At the higher doses mean peak NK (57%) and I-LAK (57%) activity were greater than at the low dose (42 and 31%, respectively), both significant at P less than 0.05. A trend to positive dose effect was seen in LAK activity (P = 0.08). Splenic artery perfusion with IL-2 can result in significant in vivo peripheral LAK cell generation as well as enhancement of I-LAK and NK activity that persists at least 16 days after the cessation of treatment. Such sustained activity would not be expected with conventional high dose i.v. therapy.
Topological versus Non--Topological Theories and $p-q$ Duality in $c \le 1$ 2d Gravity Models

We discuss the non--perturbative formulation for $c \leq 1$ string theory. The field theory like formulation of topological and non--topological models is presented. The integral representation for arbitrary $(p,q)$ solutions is derived which explicitly obeys $p-q$ duality of these theories. The exact solutions to string equation and various examples are also discussed.
AUTOMATIC SEGMENTATION OF SEPARATELY PRONOUNCED SINHALA WORDS INTO SYLLABLES

Aligned corpora are widely used in various speech applications like automatic speech recognition, speech synthesis, as well as prosodic and phonetic research. The segmentation into syllables can be done manually or automatically. But it consumes significantly more time for a fully manual phonetic segmentation and practically it is a complicated task because in many cases it requires a large aligned speech corpus. If the manual syllabification is done by a group of individuals then the consistency is decreased because the analysis variations of the individuals. Consequently, there is a dire need for automatic syllabification and it is important because Sinhala language is syllable centric in nature. A method for syllabification of acoustic signals of separately pronounced Sinhala words has been given. Detecting the syllable boundaries was achieved by two main phases and those phases have been described with examples.
Licensed under Creative Commons Attribution Cc by Behavior Analysis of Internet Traffic Using Graph and Similarity Matrix

As system activity of utilizations, for example, video spilling and distributed applications proceeds to grow it is difficult task to understand behavior patterns of end host and other network application. Keeping in mind the end goal to comprehend conduct closeness of Internet end has in the same system prefixes bipartite diagrams are utilized to model system activity, and after that one-mode projection diagrams are built for catching social-conduct closeness of end has. At that point by applying a basic and effective algorithm and similarity matrices and cluster coefficient of one mode projection graph, inherent clustered groups of internet application are discovered. Through a vector diagram including coefficient bunching that catch social practices of end hosts, the clusters are found that not just display comparable social conduct of end hosts, additionally have comparable attributes in the accumulated activity.
A New Technique of Data Integrity for Analysis of the Cloud Computing Security

Cloud computing is a latest and fast growing technology that offers an innovative, efficient and scalable business model for organizations to adopt various information technology resources i.e. software, hardware, network, storage, bandwidth etc. Cloud Computing is a jargon term without a commonly accepted non-ambiguous scientific or technical definition. At the foundation of cloud computing is the broader concept of converged infrastructure and shared services. It has the capability to incorporate multiple internal and external cloud services together to provide high interoperability there can be multiple accounts associated with a single or multiple service provider (SPs). So, Security in terms of integrity is most important aspects in cloud computing environment. In this paper, a detailed analysis of the cloud security problem is presented. Also the different problem in a cloud computing system and their effect upon the different cloud users are analyzed. It is providing a comparably scalable, position independent. Low cost platform for client's data. Since cloud computing environment is constructed based on open Architecture and interface. Based on this analysis various computing system and their effect upon the system, upon organizations and also upon different cloud users are analyzed. It is providing a comparably scalable, position-independent, low cost platform for client's data. Since cloud computing environment is constructed based on open architecture and interface. Based on this analysis various researches have also presented a view of measures that can be taken to deal with the cloud security problem and prevention that must be taken into account by any organization and cloud users seeking investment in cloud computing.
Tagging tags

Social image sharing websites like Flickr have successfully motivated users around the world to annotate images with tags, which greatly facilitate search and organization of social image content. However, these manually-input tags are far from a comprehensive description of the image content, which limits effectiveness of the tags in content-based image search. In this paper, we propose an automatic scheme called tagging tags to supplement semantic image descriptions by associating a group of property tags with each existing tag. For example, an initial tag "tiger" will be further tagged with "white", "stripes" and "bottom-right" along three tag properties: color, texture and location, respectively. In the proposed scheme, a lazy learning approach is first applied to estimate the corresponding image regions of each initial tag, and then a set of property tags, which involve six exemplary property aspects including location, color, texture, shape, size and dominance, are derived for each tag according to the content of the regions and the entire image. These tag properties enable much more precise image search especially when certain tag properties are included in the query. The results of the empirical evaluation show that tag properties remarkably boost the performance of social image retrieval.
The Robust Likelihood Model of State Measurement and Its Applications in Articulated Object Tracking

The establishment of the likelihood model of state observation with a strong robustness is one of the core issues in the study of moving hand tracking. This paper is dedicated to building a robust likelihood model of state observation, and do some study by using the method of gaining feature points from frame images of human hand. Firstly, based on physiological models and camera projection principle, we propose a basic idea that use gesture polygon to describe the image contour of hand gesture. Secondly, Lindeberg method is improved by designing the two types of response function to get the different types of feature points on multiscale space basen on the local area of vertex in the polygon, and a novel structural response mode is presented. Then we fuse the features in different scales by using Hausdorff distance and Hausdorff matrix, and present the likelihood model of state observation. Finally, the model is used for 3D motion tracking of human hand. Our theoretical analysis and experimental results show that the approach put forward in this paper has the advantages of a low time complexity and strong robustness, compared with the Lindeberg method.
A Novel Information Theoretic Framework for Finding Semantic Similarity in WordNet

Information content (IC) based measures for finding semantic similarity is gaining preferences day by day. Semantics of concepts can be highly characterized by information theory. The conventional way for calculating IC is based on the probability of appearance of concepts in corpora. Due to data sparseness and corpora dependency issues of those conventional approaches, a new corpora independent intrinsic IC calculation measure has evolved. In this paper, we mainly focus on such intrinsic IC model and several topological aspects of the underlying ontology. Accuracy of intrinsic IC calculation and semantic similarity measure rely on these aspects deeply. Based on these analysis we propose an information theoretic framework which comprises an intrinsic IC calculator and a semantic similarity model. Our approach is compared with state of the art semantic similarity measures based on corpora dependent IC calculation as well as intrinsic IC based methods using several benchmark data set. We also compare our model with the related Edge based, Feature based and Distributional approaches. Experimental results show that our intrinsic IC model gives high correlation value when applied to different semantic similarity models. Our proposed semantic similarity model also achieves significant results when embedded with some state of the art IC models including ours.
An efficient hardware architecture for deep packet inspection in hybrid intrusion detection systems

Intrusion Detection Systems are known as important security components to establish a protection mechanism for computer and network related resources. By increasing speed of computer networks, and also increasing number of incidents and complexity of attacks; IDSs need to intelligently process the inputs with high performance and precision. A key idea could be an implementation of hardware modules for some components of IDS. In this paper, an efficient hardware architecture is proposed for Network based Intrusion Detection Systems which able to detect known attacks and anomaly behavior over application protocols. Minimum time complexity, low storage cost and improved accuracy and correctness are some key features of the proposed IDS.
Identification and Evaluation of Sharing Memory Covert Timing Channel in Xen Virtual Machines

Virtualization technology is the basis of cloud computing, and the most important property of virtualization is isolation. Isolation guarantees security between virtual machines. However, covert channel breaks the isolation and leaks sensitive message covertly. In this paper, we formally model the isolation into noninterference, and define that all the transmission channels violating noninterference are covert channels. With this definition, we present an identification method based on information flow. This method first compiles the source code into a more structured equivalent code with LLVM. And then a search algorithm is proposed to obtain the shared resources and the operational processes in the equivalent code. A new covert channel termed sharing memory covert timing channel (SMCTC) is identified from Xen source code. We construct channel scenario for SMCTC, and evaluate its threat with the metrics of channel capacity and transmission accuracy. The results show that SMCTC is much more threatened than CPU load based and cache based covert channels etc.
On Combining Decisions from Multiple Expert Imitators for Performance

One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.
Turbulence Modelling of Deep Dynamic Stall at Low Reynolds Number

In the recent decades, due to the increased awareness of the environmental issues associated with the fossil fuel based power generation industry, wind industry is drawing more and more attention. Dynamic stall has been a critical phenomenon which has an important effect on the operation of both Horizontal Axis Wind Turbines (HAWT) and Vertical Axis Wind Turbines (VAWT). The aim of the present paper is to assess the ability of the Unsteady Reynolds-Averaged Navier-Stokes (URANS) method with two advanced turbulence models ( RNG k model and Transition SST model) (3) and the Detached Eddy Simulation (DES), in capturing the dynamic stall at low Reynolds number flows (Reynolds number based on the chord length of the airfoil Rec is of the order of 10 5 ), and to provide a detailed two-dimensional analysis to gain a better understanding of the flow phenomenon.
Coordinated Team Play in the Four-Legged RoboCup League

For a decade now, the RoboCup competition promotes research in robotics through soccer games between autonomous robot teams. The ability to coordinate the players within such a team of robots is the key to the success of the team. Team coordination in a human soccer game is achieved through various team formations, tactics, and strategies. Unfortunately, research in the four-legged RoboCup league has focused mostly on single player skills, demonstrating only limited results in coordinated team play. In our work, we adapt and transfer formations, tactics, and strategies used by human soccer teams, such as the popular 4-4-2 scheme, to our four-legged RoboCup team Kouretes. We define roles for each player in all the cases we consider and we implement these roles using Petri Net Plans (PNP). The assignment of appropriate roles to players is performed dynamically during the game depending on the current game state using a simple communication scheme and a finite state machine. Our approach is implemented and tested on our four-legged RoboCup team. The proposed coordination scheme can be generalized and used in various robot team applications beyond robotic soccer, such as planetary exploration and search-and-rescue missions.
A Comprehensive Analysis of CBCDACP in Wireless Sensor Networks

As wireless sensor networks are equipped with sensor nodes which have a limited energy and sensing capabilities, a good routing protocol must be designed to make the network energy efficient. In [1] we proposed a centralized routing protocol called Central Base Station Controlled Density Aware Clustering Protocol (CBCDACP) where the base station centrally performs the cluster formation task. In CBCDACP, an optimum set of cluster heads is selected by using a new cluster head selection algorithm focusing on both the density of the sensor nodes and the minimum distances among the cluster head and its neighbor nodes. It calculates the optimum set of cluster head by first selecting the candidate node, then calculating the minimum distance and also calculates the density and finally executing the main BS setup algorithm. In this paper, a comprehensive analysis of CBCDACP algorithm is stated varying the simulation parameters to demonstrate the robustness of this algorithm and explaining the algorithm blocks of CBCDACP. The simulation performance of CBCDACP is then compared with some clustering-based schemes such as Static-Clustering, Low Energy Adaptive Clustering Hierarchy (LEACH), Centralized LEACH (LEACH-C). Simulation results show that CBCDACP can improve system life time and energy efficiency in terms of different simulation performance metrics over its comparatives . As wireless sensor networks are equipped with sensor nodes which have a limited energy and sensing capabilities, a good routing protocol must be designed to make the network energy efficient. In [1] we proposed a centralized routing protocol called Central Base Station Controlled Density Aware Clustering Protocol (CBCDACP) where the base station centrally performs the cluster formation task. In CBCDACP, an optimum set of cluster heads is selected by using a new cluster head selection algorithm focusing on both the density of the sensor nodes and the minimum distances among the cluster head and its neighbor nodes. It calculates the optimum set of cluster head by first selecting the candidate node, then calculating the minimum distance and also calculates the density and finally executing the main BS setup algorithm. In this paper, a comprehensive analysis of CBCDACP algorithm is stated varying the simulation parameters to demonstrate the robustness of this algorithm and explaining the algorithm blocks of CBCDACP. The simulation performance of CBCDACP is then compared with some clustering-based schemes such as Static-Clustering, Low Energy Adaptive Clustering Hierarchy (LEACH), Centralized LEACH (LEACH-C). Simulation results show that CBCDACP can improve system life time and energy efficiency in terms of different simulation performance metrics over its comparatives .
Gramian-Preserving Frequency Transformation for Linear Discrete-Time State-Space Systems

This paper proposes the Gramian-preserving frequency transformation for linear discrete-time state-space systems. In this frequency transformation, we replace each delay element of a discrete-time system with an allpass system that has a balanced realization. This approach can generate transformed systems that have the same controllability/observability Gramians as those of the original system. From this result, we show that the Gramian-preserving frequency transformation gives us transformed systems with different magnitude characteristics, but with the same structural property with respect to the Gramians as that of the original system. This paper also presents a simple method for realization of the Gramian-preserving frequency transformation. This method makes use of the cascaded normalized lattice structure of allpass systems.
A Fair Active Queue Management Algorithm Based on Hierarchical Rate Code

In view of the disadvantages of core-stateless fair queuing fair active queue management algorithm based on hierarchical rate coding is proposed. In this method, the hierarchical rate coding in the compressed shared space of rate is pointed out to ensure more accurate rate max-min fairness. Effective punishment upon misbehaving flow with a full stateless method is imposed to improve the fairness of the algorithm. The queue arrival rate is maintained through reasonable hierarchical packet dropping and the buffer have a better utilization because of management of the index queue. Experiment results show that the algorithm keeps high efficiency and good fairness, and has good performances.