Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Switching and Networking

Next-generation SONET/SDH provides carriers with new capabilities for building advanced Ethernet data services. Along these lines, numerous Ethernet-over-SONET studies have been done, with most focusing on point-to-point private line type services. This paper introduces the more challenging problem of provisioning multi-point-to-multi-point Ethernet LAN services over inverse-multiplexing networks and proposes novel overlay strategies based upon star, bus, and minimum spanning tree topologies. Multi-tiered protection schemes are also developed to help set up these respective overlay designs. Detailed simulation results are then presented along with directions for future work. © 2010 Elsevier B.V. All rights reserved.
Connection-based communication in dynamic networks

We analyze and improve the fault tolerance of practical, efficient end to end communication schemes. We concentrate on connection-based source routing schemes, used in most existing wide-area networks, e.g. in SNA/APPN. These schemes are composed of three components: a topology update protocol, a route selection algorithm and a connection protocol. The topology update protocol maintains an approximate ion of the network topology at every processor. The route selection algorithm in the source processor uses the topology approximation to select the ‘best’ route to the destination. The connection protocol sends messages along this route. We make the following contributions: Explicitly expressing the high efficiency possible with connection-based schemes. This efficiency is well appreciated in practice, but has not been analyzed formally so far. Roughly speaking, both communication and time complexities are in the order of the shortest route from source to destination which is up for ‘long enough’. ●IBM T.J. Watson, Yorktown Heights, NY 10598. Email: amir@ watson . ibm.com Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. PoDC ‘92-81921B.C. a 1992 ACM 0-89781 . 496.1 /92/0008 /0013 . $1 . SO ●
Title Agent-based coordination for the sensor web

This paper addresses the problem of coordination within the Sensor Web, where the Sensor Web is defined as an amorphous network of spatially distributed nodes that sense various phenomena in the environment, that are battery powered, and that communicate and coordinate wirelessly. The approach described advocates the use of a multi-agent system, and specifically the use of multi-agent distributed constraint optimisation algorithms. Developing software for low powered sensing devices introduces several problems to be addressed; the most obvious being the limited computational resources available. In this paper we discuss an implementation of ADOPT, a pre-existing algorithm for distributed constraint optimisation, and describe how it has been integrated with a reflective agent platform developed for resource constrained devices, namely Agent Factory Micro Edition (AFME). The usefulness of this work is illustrated through the canonical multi-agent coordination problem, namely graph colouring.
A time-frequency division multiplexing communications system with hexagonal lattice structure

In this paper, we propose a time-frequency division multiplexing (TFDM) communications system for multiple users which uses prolate spheroidal wave functions (PSWFs) as the transmission basis. A hexagonal time-frequency lattice provides optimal packing of the data in a fixed time-bandwidth support. We show that when using chirp modulation created by fractional Fourier transform (FrFT) the resulting system is robust to noise. The chirps having an instantaneous frequency with the angle of π/3 with respect to the time sets a hexagonal lattice structure. The reconstruction of the data can be done efficiently by windowing and filtering. We illustrate the performance of the proposed TFDM system in the presence of additive white Gaussian noise by means of simulations.
Clustering low quality Farsi sub-words for word recognition

OCR of low resolution documents is not so common, because it has a lot of problems. However, today there are several archives of digital documents which are scanned at low resolution, to consume less storage. These documents which usually have a resolution of 100 to 150 dpi, require to be converted to searchable documents. In this paper presents a new method for clustering of low quality printed Persian sub-words. This is necessary to reduce the number of classes of sub-words in order to improve the overall recognition rate. Two popular clustering methods, hierarchical and k-means implemented and compared. Local binary patterns (LBP) and zoning algorithms used for feature extraction. Both features are fast and represent the global shape information very well. Moreover, we used different distance measures to find the similarity of feature vectors. We applied our algorithms on a dataset of 10,700 images of distinct Persian sub-words with 96 dpi resolution. Experimental results show that the hierarchical clustering with the correlation distance measure has the best performance over other clustering methods and distance measures.
Statistical Parsing for CCG with Simple Generative Models

This paper presents a statistical parser for a wide-coverage Combinatory Categorial Grammar (CCG) derived from the Penn Treebank. The Treebank is translated to a corpus of canonical CCG derivations. We de ne a generative statistical model over CCG derivations and train it on the transformed Treebank. This model is evaluated using Parseval measures and the accuracy of recovery of word-word dependencies. The impact of lexical coverage on parsing accuracy is also investigated.
ANFIS: General description for modeling dynamic objects

This work advocates the use of neuro-fuzzy based approach for the task of modeling nonlinear dynamic objects using Adaptive (Hybrid) learning mechanism in unknown environment. It aims to give to new researchers description on ANFIS capability to model nonlinear plants; i.e., combining (NN) and (FL) in what is referred in literature as "neuro-fuzzy". This combination seems natural because the two approaches generally attack the design of "intelligent" systems from different angles. NNs provide algorithms for learning, classification, and optimization, whereas FL deals with issues such as reasoning on a higher (semantic or linguistic) level. Consequently, the two technologies complement each other. By integrating neural networks with fuzzy logic, it is possible to bring the low-level of computational power and learning of NNs into FL systems.
Epileptogenic focus detection in intracranial EEG based on delay permutation entropy

Epileptogenic localization is a critical factor for successful epilepsy surgery. Determining the epileptogenic hippocampus with single channel intracranial electroencephalography (iEEG) recording is beneficial to decrease the risk of infection compared with that based on multi-channel iEEGs. Delay permutation entropy (DPE) methodology is presented in this study to measure iEEG with different delay lag based on focal epileptogenic zone. A total of 1600 20-s epileptic iEEG are evaluated and are used as features to classify epileptogenic and non-epileptogenic zone. Experimental results show that the DPE index of epileptogenic iEEG is significant lower than that of non-epileptogenic hemisphere when delay lag ranges from 5 to 30 (p=0.01). In addition, the accuracy of identifying epileptogenic region with the DPE index is increased when the delay lag between 5 and 25, compared to the performance of the PE index.
Giardia lamblia Transcriptome Analysis Using TSS-Seq and RNA-Seq

Giardia lamblia is a protozoan parasite that is found worldwide and has both medical and veterinary importance. We applied the transcription start sequence (TSS-seq) and RNA sequence (RNA-seq) techniques to study the transcriptome of the assemblage A WB strain trophozoite. We identified 8000 transcription regions (TR) with significant transcription. Of these regions, 1881 TRs were more than 500 nucleotides upstream of an annotated ORF. Combining both techniques helped us to identify 24 ORFs that should be re-annotated and 60 new ORFs. From the 8000 TRs, we were able to identify an AT-rich consensus that includes the transcription initiation site. It is possible that transcription that was previously thought to be bidirectional is actually unidirectional.
Formal Comparison Of Separation Kernel Models: GWV Separation Versus Rushby's Non Interference

Department of Mathematics and Computer Science Master in Computer Science and Engineering. Formal Comparison Of Separation Kernel Models: GWV Separation Versus Rushby’s Non Interference by Adrian Garcia Ramirez In this document we investigate how formal verification methods can be used to compare and classify separation kernel architectures. The separation kernels are an integral part of the Multiple Independent Levels of Security (MILS) design pattern. Consisting of relatively small components that run directly over the hardware and whose main task is to create separation or isolation between the different processes running within the host device. The two main formal models of separation kernels are the one proposed by Greves, Wilding and Vanfleet (GWV) and the one proposed by John Rushby. In the first part of this dissertation we formalize the original version of the GWV and Rushby models in the Isabelle/HOL theorem prover. Moreover, we present that to our knowledge is the first formal comparison of the aforementioned models. In the second part, we propose a a mapping between the elements conforming both models, and later we use this mapping to compare the security definitions presented in them. Finally In the last part we present two examples that illustrate the main differences between the Rushby Non Interference and GWV separation models.
Hastings ratio of the LOCAL proposal used in Bayesian phylogenetics.

This is an electronic version of an article published in Systematic Biology ["Holder, Mark T. , Paul O. Lewis, David L. Swofford, and Bret Larget. Hastings ratio of the local proposal used in Bayesian phylogenetics. Systematic Biology, 54:961{965, 2005. ] Systematic Biology is available online at informaworld http://dx.doi. org/10.1080/10635150500354670
Role of the H-2 complex in induction of T helper cells in vivo. I. Antigen-specific selection of donor T cells to sheep erythrocytes in irradiated mice dependent upon sharing of H-2 determinants between donor and host

When purified CBA lymph node T cells were mixed with sheep erythrocytes (SRC) and filtered from blood to lymph through irradiated syngeneic mice for 1-2 days, the donor cells lost their capacity to stimulate anti-SRC responses by CBA B cells; the response to a third-party antigen (horse erythrocytes) was unaffected and active suppression was not involved. This process of specific negative selection to SRC also occurred when semiallogeneic mice were used as filtration hosts. By contrast, when allogeneic hosts were used the helper function of the donor cells was not reduced; this applied to both primed and unprimed T cells. Studied with congeneic resistant strains indicated that negative selection to SRC occurred only when the donor and host shared H-2 determinants. Studies with T cells depleted of alloreactive lymphocytes showed that negative selection to SRC in irradiated F1 hybrid mice was followed by a stage of positive selection where the donor cells gave greatly increased responses to the injected antigen. Positive selection did not occur in H-2-different mice, however, and the helper function of the donor cells remained unchanged. By these parameters it was concluded that homozygous T helper cells have no detectable capacity to recognize antigen in an H-2-different environment.
'Studentsification': recognising the diversity of student populations and student accommodation pathways

This thesis advances understandings of the diversity of student populations, student accommodation pathways, and connections to processes of studentification. The massification of HE, coupled with widening participation and internationalisation agendas, has led to changes in the social composition of the student population. Alongside this transformation, student accommodation preferences are changing, and student accommodation is being supplied which contrasts with traditional notions of shared student housing. From this starting point, this thesis progresses existing knowledges of student geographies in several ways. [Continues. ]
The neuroarchitecture of the circadian clock in the brain of Drosophila melanogaster

Neuroethologists try to assign behavioral functions to certain brain centers, if possible down to individual neurons and to the expression of specific genes. This approach has been successfully applied for the control of circadian rhythmic behavior in the fruit fly Drosophila melanogaster. Several so‐called “clock genes” are expressed in specific neurons in the lateral and dorsal brain where they generate cell‐autonomous molecular circadian oscillations. These clusters are connected with each other and contribute differentially to the control of behavioral rhythmicity. This report reviews the latest work on characterizing individual circadian pacemaker neurons in the fruit fly's brain that control activity and pupal eclosion, leading to the questions by which neuronal pathways they are synchronized to the external light‐dark cycle, and how they impose periodicity on behavior. Microsc. Res. Tech. 62:94–102, 2003. © 2003 Wiley‐Liss, Inc.
Fuzzy K-mean clustering in MapReduce on cloud based hadoop

Clustering is regarded as one of the significant task in data mining which deals with primarily grouping of similar data. To cluster large data is a point of concern. Hadoop is a software framework which deals with distributed processing of huge amount of data across clusters of commodity computers using MapReduce programming model. MapReduce allows a kind of parallelization for solving a problem involving large data sets using computing clusters and is also an attractive mean for data clustering involving large datasets. Mahout, a scalable machine learning library is an approach to Fuzzy K-mean clustering which runs on a Hadoop. This paper focuses on studying the performance of different datasets using Fuzzy K-mean clustering in MapReduce on Hadoop. Experimental results depict the execution time of the approach on a multi-node Hadoop cluster which is build using Amazon Elastic Cloud Computing(Amazon EC2).
Cryptanalysis on PHOTON hash function using cube attack

The lightweight hash function PHOTON has shown excellent hardware compactness which almost reaches the theoretical optimum and brings benefits for on-tag applications. Unlike cryptanalysis which tries to find collisions or build distinguishers on PHOTON, we apply cube attack for secret recovery of PHOTON. We focus on 1 to 3 round of PHOTON-128/16/16 in our experiments. Using cube attack, we show at least half of the message bits can be recovered. When more powerful computing capability is used, it is possible to recover more bits of the message in the same number of PHOTON rounds.
Microbial aetiology of acute diarrhoea in children under five years of age in Khartoum, Sudan

Diarrhoea is one of leading causes of morbidity and mortality worldwide. Recent estimations suggested the number of deaths is close to 2.5 million. This study examined the causative agents of diarrhoea in children under 5 years of age in suburban areas of Khartoum, Sudan. A total of 437 stool samples obtained from children with diarrhoea were examined by culture and PCR for bacteria, by microscopy and PCR for parasites and by immunoassay for detection of rotavirus A. Of the 437 samples analysed, 211 (48 %) tested positive for diarrhoeagenic Escherichia coli, 96 (22 %) for rotavirus A, 36 (8 %) for Shigella spp. , 17 (4 %) for Salmonella spp. , 8 (2 %) for Campylobacter spp. , 47 (11 %) for Giardia intestinalis and 22 (5 %) for Entamoeba histolytica. All isolates of E. coli (211, 100 %) and Salmonella (17, 100 %), and 30 (83 %) isolates of Shigella were sensitive to chloramphenicol; 17 (100 %) isolates of Salmonella, 200 (94 %) isolates of E. coli and (78 %) 28 isolates of Shigella spp. were sensitive to gentamicin. In contrast, resistance to ampicillin was demonstrated in 100 (47 %) isolates of E. coli and 16 (44 %) isolates of Shigella spp. In conclusion, E. coli proved to be the main cause of diarrhoea in young children in this study, followed by rotavirus A and protozoa. Determination of diarrhoea aetiology and antibiotic susceptibility patterns of diarrhoeal pathogens and improved hygiene are important for clinical management and controlled strategic planning to reduce the burden of infection.
MASS CUSTOMIZATION AND BEYOND IN SOFTWARE ENGINEERING : Developing a framework for mass customized adaptive software

History of industrial production has gone through different phases, starting from craftsmanship, evolving to mass production and process improvement and lately to mass customization. This new way to organize production has challenged all industries producing products and services. In this research we are concentrating to software industry, which is a young and fast moving industry. We describe the evolution of software engineering and compare it to the evolution of industrial production, concentrating on mass customization possibilities in software industry. We also shortly discuss adaptive applications and their role in mass customization. Finally, we suggest a tentative framework for creating mass customized adaptive software.
Uncertainty, Credit Spreads, and Investment Dynamics

In the standard bond-pricing framework (e.g., Merton [1974]), the return function of holders of risky corporate debt is a concave function of the firm’s stochastic return, implying that a mean-preserving spread is associated with an increase in the bond risk premium. This feature of the standard debt contract has two important implications for the relationship between uncertainty and investment. First, to the extent that firms face significant frictions in credit markets, the rise in the bond risk premium implies an increase in the cost of capital and hence a reduction in investment; in such environment, uncertainty can have a significant effect on investment dynamics absent any investment irreversibility or managerial risk aversion. Second, if credit market frictions are an important mechanism through which uncertainty affects investment, then the inclusion of the bond risk premium in an empirical investment specification should attenuate the direct impact of uncertainty on investment. Using both the aggregate time-series and firm-level data, we test these two hypotheses and find strong support for the view that the relationship between uncertainty and investment is influenced importantly by the presence of credit market frictions. We then develop a tractable general equilibrium model in which firms issue risky bonds and equity in imperfect capital markets to finance investment projects. We calibrate the uncertainty process using firm-level estimates of shocks to the firms’ profits and show that the model successfully explains the cross-sectional and time-series properties of bond risk premiums and their comovement with uncertainty and aggregate investment.
Enantioselective Protein-Sterol Interactions Mediate Regulation of Both Prokaryotic and Eukaryotic Inward Rectifier K+ Channels by Cholesterol

Cholesterol is the major sterol component of all mammalian cell plasma membranes and plays a critical role in cell function and growth. Previous studies have shown that cholesterol inhibits inward rectifier K+ (Kir) channels, but have not distinguished whether this is due directly to protein-sterol interactions or indirectly to changes in the physical properties of the lipid bilayer. Using purified bacterial and eukaryotic Kir channels reconstituted into liposomes of controlled lipid composition, we demonstrate by 86Rb+ influx assays that bacterial Kir channels (KirBac1.1 and KirBac3.1) and human Kir2.1 are all inhibited by cholesterol, most likely by locking the channels into prolonged closed states, whereas the enantiomer, ent-cholesterol, does not inhibit these channels. These data indicate that cholesterol regulates Kir channels through direct protein-sterol interactions likely taking advantage of an evolutionarily conserved binding pocket.
Communication between deaf and hearing children using statistical machine translation

Communication with hearing people society is an important problem for deaf people. Because they are not learned the valid rules of spoken language that hearing people use them. We therefor prepare an efficient corpus and apply it to Moses Machine Translation to simplify these communications. We choose communications between children because they use e-communications more than adults. All of the systems that automatically process sign language corpus rely on appropriate data. So our corpus with a limited set of words and with specific subject is the first Persian corpus containing Persian language, PL, and Persian sign language, PSL, based on the domain of children conversations. At the first step raw data are pre-processed which provides necessary information for translation. These data are statistic information extracted of sentences. After getting important data from initial sentences, the corpus is applied for training of Moses machine translation. Beside on the main goal of this system, we can educate deaf people the valid Persian grammar that is a problem for deaf people in school and society. In this paper we compare our results with the results taken from Moses decoder in other spoken languages that indicate our purpose is applicable in real world.
Immunological, Viral, Environmental, and Individual Factors Modulating Lung Immune Response to Respiratory Syncytial Virus

Respiratory syncytial virus is a worldwide pathogen agent responsible for frequent respiratory tract infections that may become severe and potentially lethal in high risk infants and adults. Several studies have been performed to investigate the immune response that determines the clinical course of the infection. In the present paper, we review the literature on viral, environmental, and host factors influencing virus response; the mechanisms of the immune response; and the action of nonimmunological factors. These mechanisms have often been studied in animal models and in the present review we also summarize the main findings obtained from animal models as well as the limits of each of these models. Understanding the lung response involved in the pathogenesis of these respiratory infections could be useful in improving the preventive strategies against respiratory syncytial virus.
Research on Spectral Clustering algorithms and prospects

Along with the expansion and in-depth of the application domain of cluster analysis, one kind of new cluster algorithm called Spectral Clustering algorithm has been aroused great concern by scholars, Spectral Clustering algorithm is newly developing technique in the field of machine learning in recent years. Unlike the traditional clustering algorithms, this can solve the clustering of non-convex sphere of sample spaces and has globally optimal solution. This paper introduces the principle, the induction summary to the current research situation of Spectral Clustering algorithm as well as in various application domains. Firstly, the analysis and induction of some Spectral Clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, some typical Spectral Clustering algorithms have been selected to analyze and compare. Finally, it points out the key problems and future directions.