Miracle-dz/UniFAR-FTUs huggingface dataset:
Profile diversity in search and recommendation

We investigate profile diversity, a novel idea in searching scientic documents. Combining keyword relevance with popularity in a scoring function has been the subject of different forms of social relevance [2, 6, 9]. Content diversity has been thoroughly studied in search and advertising [4, 11], database queries [16, 5, 8], and recommendations [17, 10, 18]. We believe our work is the first to investigate profile diversity to address the problem of returning highly popular but too-focused documents. We show how to adapt Fagin's threshold-based algorithms to return the most relevant and most popular documents that satisfy content and profile diversities and run preliminary experiments on two benchmarks to validate our scoring function.
Possible cosmological implications of the quark-hadron phase transition

We study the quark-hadron phase transition within an effective model of QCD, and find that in a reasonable range of the main parameters of the model, bodies with quark content between 10-2 and 10 solar masses can have been formed in the early universe. In addition, we show that a significant amount of entropy is released during the transition. This may imply the existence of a higher baryon number density than is usually expected at temperatures above the QCD scale. The cosmological QCD transition may then provide a natural way of decreasing the high baryon asymmetry created by an Affleck-Dine-like mechanism down to the value required by primordial nucleosynthesis.
Does the think-aloud protocol reflect thinking? Exploring functional neuroimaging differences with thinking (answering multiple choice questions) versus thinking aloud

Background: Whether the think-aloud protocol is a valid measure of thinking remains uncertain. Therefore, we used functional magnetic resonance imaging (fMRI) to investigate potential functional neuroanatomic differences between thinking (answering multiple-choice questions in real time) versus thinking aloud (on review of items). Methods: Board-certified internal medicine physicians underwent formal think-aloud training. Next, they answered validated multiple-choice questions in an fMRI scanner while both answering (thinking) and thinking aloud about the questions, and we compared fMRI images obtained during both periods. Results: Seventeen physicians (15 men and 2 women) participated in the study. Mean physician age was 39.5 + 7 (range: 32–51 years). The mean number of correct responses was 18.5/32 questions (range: 15–25). Statistically significant differences were found between answering (thinking) and thinking aloud in the following regions: motor cortex, bilateral prefrontal cortex, bilateral cerebellum, and the basal ganglia (p < 0.01). Discussion: We identified significant differences between answering and thinking aloud within the motor cortex, prefrontal cortex, cerebellum, and basal ganglia. These differences were by degree (more focal activation in these areas with thinking aloud as opposed to answering). Prefrontal cortex and cerebellum activity was attributable to working memory. Basal ganglia activity was attributed to the reward of answering a question. The identified neuroimaging differences between answering and thinking aloud were expected based on existing theory and research in other fields. These findings add evidence to the notion that the think-aloud protocol is a reasonable measure of thinking.
FABRICATION AND CHARACTERIZATION OF CONDUCTIVE MELT ELECTROSPUN FIBERS

Conductive polymer nanocomposites are a type of particle reinforced plastic composite where the doping material is electrically conductive. The diverse properties of an engineered composite material allow for the material properties to be fine-tuned for the specific application. This research focuses on using carbon allotropes, such as two-dimensional graphene and onedimensional carbon nanotubes, to achieve direct current electrical conductivity through a polymer fiber. Melt electrospinning is the process used for creating the micrometer scale fibers by melting thermoplastic materials. High electrostatic fields apply a force to the polymer melt and a single fiber is drawn out. The resistivity of the bulk composite and composite fibers were characterized by four-point probe and van der Pauw resistivity measurements. Other material characterization methods such as X-ray diffraction and scanning electron microscopy were used to determine particle size and distribution in the polymer matrix. Several different polymers were used as the matrix material. Originally, the majority of the research focused on relatively low molecular weight varieties of polypropylene. Later, additional polymer samples of recycled polypropylene, polystyrene, and polyethylene terephthalate were supplied in collaboration with the Army Research Laboratory. Premade polypropylene and carbon nanotube composite material were supplied by Sandia National Laboratories and Virginia Tech. The graphene composites utilized polypropylene and polystyrene as the matrix material, and were made at Montana Tech. Recycled polyethylene terephthalate was used to create filament for rapid prototype machines.
Linear optical quantum computing with photonic qubits

Linear optics with photon counting is a prominent candidate for practical quantum computing. The protocol by Knill, Laflamme, and Milburn [2001, Nature (London) 409, 46] explicitly demonstrates that efficient scalable quantum computing with single photons, linear optical elements, and projective measurements is possible. Subsequently, several improvements on this protocol have started to bridge the gap between theoretical scalability and practical implementation. The original theory and its improvements are reviewed, and a few examples of experimental two-qubit gates are given. The use of realistic components, the errors they induce in the computation, and how these errors can be corrected is discussed.
Vocal Features for Glottal Pathology Detection using BPNN

Development of low cost, non-invasive applications is one of the most challenging tasks in the field of biomedical signal processing. Present work focuses on detection of glottal pathology with the knowledge of prominent speech processing and machine learning techniques. This paper addresses the discriminative characteristics of speech signal like, pitch, jitter, linear prediction residual and cepstral source excitation to aid such an identification system. Back-propagation Neural Network model is developed for various feature combinations to classify the glottal pathologic voice from normal voice. Accuracy of the developed system is evaluated considering different feature sets. Work also concludes the efficiency of such acoustic features for various combinations using objective measures like confusion matrix, true positive rate i.e. sensitivity, specificity i.e. true negative rate and accuracy. The results show promising development in identification of pathological individual from normal person using voice samples.
Unsupervised Generation of Parallel Treebanks through Sub-Tree Alignment

Unsupervised Generation of Parallel Treebanks through Sub-Tree Alignment The need for syntactically annotated data for use in natural language processing has increased dramatically in recent years. This is true especially for parallel treebanks, of which very few exist. The ones that exist are mainly hand-crafted and too small for reliable use in data-oriented applications. In this paper we introduce an open-source system for fast and robust automatic generation of parallel tree-banks. We expect the opening of the presented platform to the scientific community to help boost research in the field of data-oriented machine translation and lead to advancements in other fields where parallel treebanks can be employed.
Comparison of Advance Tree Data Structures

Btree and Rtree are two basic index structures; many different variants of them are proposed after them. Different variants are used in specific application for the performance optimization. In this paper different variants of Btree and Rtree are discussed and compared. Index structures are different in terms of structure, query support, data type support and application. Index structures are discussed first. Btree and its variants are discussed and them Rtree and its variants are discussed. Some structures example is also shown for the more clear idea. Then comparison is made between all structure with respect to complexity, query type support, data type support and application.
On TCP performance enhancing proxies in a wireless environment

TCP performance enhancement in wireless access networks is an important ongoing area of research. It is known that the hostile nature of the wireless channel and the mobile nature of wireless users interact adversely with standard TCP congestion control mechanisms [1], causing a drastic reduction in throughput. This article surveys a selection of different approaches to managing TCP performance over wireless links, and presents the results of simulation and field trial results of a novel TCP performance enhancing proxy over diverse cellular radio access technologies based on the GSM, cdma2000, and UMTS standards. The proposed TRL TCP performance enhancing proxy has the advantages of being completely transparent to both TCP endpoints and tunable to different access technologies, and frequently achieves the maximum throughput available from any of the studied radio access technologies.
[Effect of corticotherapy on interleukin-8 and -12 and nitric oxide production during Behçet and idiopathic uveitis].

OBJECTIVES To investigate the effect of corticotherapy on IL-8, IL-12, and nitric oxide (NO) production during idiopathic and Behçet active uveitis. METHODS Peripheral venous blood was drawn from 70 patients with active uveitis before and during corticotherapy (32 with Behçet uveitis and 38 with idiopathic uveitis) and from 30 controls. Plasma was collected and peripheral blood mononuclear cells were separated immediately and cultured with or without Concanavaline A. IL-8 and IL-12 levels in plasma and culture supernatants were measured by specific enzyme-linked immunosorbent assay (ELISA). Nitric oxide levels were evaluated using a modified Griess method. RESULTS Before therapy, the two groups of patients showed highly significant elevation of IL-8, IL-12, and NO levels compared to control subjects. During therapy, IL-8 and nitric oxide levels were significantly lower in active idiopathic and Behçet active uveitis both in vivo and in vitro. This effect was correlated with therapy duration. In contrast, while significant reduction of IL-12 levels was observed both in vivo and in vitro in idiopathic active uveitis during therapy, this effect was observed in vitro in Behçet active uveitis but not in vivo. CONCLUSION Our results suggest that IL-8, IL-12, and NO are involved in the physiopathological mechanisms of idiopathic and Behçet uveitis. These three molecules showed different degrees of sensitivity to the inhibitory effect of corticoids, reflecting their different regulation by corticotherapy during active phases of the two diseases. According to our study, IL-8 can serve as a marker of inflammatory responses, while IL-12 should be used as a marker of the specific immune responses during active uveitis.
Memory-error tolerance of scalable and highly parallel architecture for restricted Boltzmann machines in Deep Belief Network

A key aspect of constructing highly scalable Deep-learning microelectronic systems is to implement fault tolerance in the learning sequence. Error-injection analyses for memory is performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). It is confirmed that the RBMs in Deep Belief Networks (DBNs) provides remarkable robustness against memory errors. Fine-tuning has significant effects on recovery of accuracy for static errors injected to the structural data of RBMs during and after learning, which are either at cell-level or block level. The memory-error tolerance is observable using our hardware networks with fine-graded memory distribution.
Mobility Pattern Aware Mobile Ad hoc Networks and It's Applications in m-Governance

Ad hoc networks are emerging as the next generation of networks. An ad hoc network is an autonomous system of mobile stations connected by wireless links, union of these mobile stations form a communication network in the form of an arbitrary communication graph. Mobile stations in mobile ad hoc network are not just end systems, each node must be able to function as a router as well to relay the packets generated by other nodes. In ad hoc networks, a node moves to different locations over time, thus creating a different network topology. Routing in ad hoc network has been an active area of research. Significant work has been done on routing in ad hoc network. But with increasing acceptance of mobile ad hoc networks, the demand now is being placed on performance of these types of networks due to nondeterministic nature of these networks. Non deterministic nature is inherent property of mobile ad hoc network, but there are cases where the mobile node movements are predictable to some extent. It is the mobility of nodes that renders the network topology in a state of flux, resulting in degradation in performance of network. Issues of mobility can be resolved by defining mobility into various patterns. This paper tries to characterize the mobility of the nodes into some pre-identified mobility patterns where movements can be approximated to simple patterns and this information may be used to bring in predictability to motion. Most of the prior research on ad hoc networks assumed random mobility model but real world is complex and vastly diverse that makes the existing models inappropriate of representing the real world context properly. This paper proposes a general mobility model capable of considering more realistic scenarios by exploiting the concept of fixed hotspot inclusion. The mGovernance is at its nascent stage. Because MANETs are infrastructure less, they can be easily adapted to provide low cost m-Governance services. This paper describes how these mobility patterns can be used in m-Governance and how adoption of these patterns affects performance of network.
Intrusion Detection based on Incremental Combining Classifiers

Intrusion detection (ID) is the task of analysis the event occurring on a network system in order to detect abnormal activity. Intrusion Detection System has increased due to its more constructive working than traditional security mechanisms. As the network data is dynamic in nature, it leads to the problem of incremental learning of dynamic data. Now, combining classifiers is a new method for the improving classifiers robustness and accuracy. Most of ensemble methods operates in batch mode. For this purpose, proposed system incremental combining classifiers that combines three classifiers that operates incrementally on dynamic data, Naive Bayes, K-star, Non Nested Generalised Exemplars classifiers based on voting approach. In incremental learning process, numbers of hypotheses are generated during classification; an ensemble decision method is required to aggregate all the votes from multiple hypotheses for the final decision process which produces better accuracy in most of the cases in experiments. General Terms Security, Algorithms
The efficacy of N-acetylcysteine in the treatment of methamphetamine dependence: a double-blind controlled, crossover study.

OBJECTIVE Preclinical studies and early pilot clinical investigations have suggested that N-acetylcysteine (NAC) may be useful in treatment of methamphetamine (METH) dependence. The present study evaluated whether NAC would suppress craving to the METH. METHODS In a double-blind, controlled crossover clinical trial, 32 METH-dependent volunteers were chosen to receive either NAC (1200 mg/day) or placebo, randomly. They were intervened in two four-week sessions. During first session they received either 1200 mg/day of NAC (group A) or placebo (group B). After three days of washout period, next session started with the crossover intervention of the previous regimen. During these eight weeks, all participants received standardized, and Matrix Model of treatment. Craving was assessed using the Cocaine Craving Questionnaire-Brief (CCQ-Brief). The data were analyzed using SPSS version 20.0 (SPSS Inc. Chicago, Illinois, USA). RESULTS In 23 subjects who completed the study, the mean score of CCQ-Brief reduced in four consecutive weeks with NAC treatment. The mean (SD) scores of carving in group A and B were 3.38 (1.16) and 5.96 (1.03), at the end of first session; and 4.57 (1.88) and 3.2 (0.86), at the end of the second session, respectively. Our findings indicate that the main effect was significant for NAC (P < 0.001). Across placebo and NAC conditions, only mild side effects were noted, and the number of subjects who reported side effects did not differ. CONCLUSION The NAC showed good efficacy in suppressing METH craving, and may be a useful pharmacological treatment for METH dependency.
Burkholderia, a Genus Rich in Plant-Associated Nitrogen Fixers with Wide Environmental and Geographic Distribution

ABSTRACT The genus Burkholderia comprises 19 species, includingBurkholderia vietnamiensis which is the only known N2-fixing species of this bacterial genus. The first isolates of B. vietnamiensis were recovered from the rhizosphere of rice plants grown in a phytotron, but its existence in natural environments and its geographic distribution were not reported. In the present study, most N2-fixing isolates recovered from the environment of field-grown maize and coffee plants cultivated in widely separated regions of Mexico were phenotypically identified asB. cepacia using the API 20NE system. Nevertheless, a number of these isolates recovered from inside of maize roots, as well as from the rhizosphere and rhizoplane of maize and coffee plants, showed similar or identical features to those of B. vietnamiensis TVV75T. These features include nitrogenase activity with 10 different carbon sources, identical or very similar nifHDK hybridization patterns, very similar protein electrophoregrams, identical amplified 16S rDNA restriction (ARDRA) profiles, and levels of DNA-DNA reassociation higher than 70% with total DNA from strain TVV75T. Although the ability to fix N2 is not reported to be a common feature among the known species of the genus Burkholderia, the results obtained show that many diazotrophic Burkholderia isolates analyzed showed phenotypic and genotypic features different from those of the known N2-fixing species B. vietnamiensisas well as from those of B. kururiensis, a bacterium identified in the present study as a diazotrophic species. DNA-DNA reassociation assays confirmed the existence of N2-fixing Burkholderia species different fromB. vietnamiensis. In addition, this study shows the wide geographic distribution and substantial capability of N2-fixing Burkholderia spp. for colonizing diverse host plants in distantly separated environments.
Text String Extraction in Delaunay Triangulation

This paper establishes a Delaunay triangulation model for detecting text strings in an image. In this method, text regions are represented based on Delaunay triangles that can be extracted easily. A searching mechanism based on the left and right arms of a discrete symmetric axis is developed to locate text in an image. The discrete symmetric axis consists of a group of off-curve-edge adjacent Delaunay triangles that are identifiable. Our experiment shows that the approach can detect multi-orientated texts and is efficient and effective.
Cross-domain question classification in community question answering via kernel mapping

An increasingly popular method for retrieving information is via the community question answering (CQA) systems such as Yahoo! Answers and Baidu Knows. In CQA, question classification plays an important role to find the answers. However, the labeled training examples for statistical question classifier are fairly expensive to obtain, as they require the experienced human efforts. Meanwhile, unlabeled data are readily available. This paper employs the method of domain adaptation via kernel mapping to solve this problem. In detail, the kernel approach is utilized to map the target-domain data and the source-domain data into a common space, where the question classifiers are trained under the closer conditional probabilities. The kernel mapping function is constructed by domain knowledge. Therefore, domain knowledge could be transferred from the labeled examples in the source domain to the unlabeled ones in the targeted domain. The statistical training model can be improved by using a large number of unlabeled data. Meanwhile, the Hadoop Platform is used to construct the mapping mechanism to reduce the time complexity. Map/Reduce enable kernel mapping for domain adaptation in parallel in the Hadoop Platform. Experimental results show that the accuracy of question classification could be improved by the method of kernel mapping. Furthermore, the parallel method in the Hadoop Platform could effective schedule the computing resources to reduce the running time.
Characterization and Management of Popular Content in KAD

The endeavor of this work is to study the impact of content popularity in a large-scale Peer-to-Peer network, namely KAD. Based on an extensive measurement campaign, we pinpoint several deficiencies of KAD in handling popular content and provide a series of improvements to address such shortcomings. Our work reveals that keywords, which are associated with content, may become popular for two distinct reasons. First, we show that some keywords are intrinsically popular because they are common to many disparate contents: in such case we ameliorate KAD by introducing a simple mechanism that identifies stopwords. Then, we focus on keyword popularity that directly relates to popular content. We design and evaluate an adaptive load balancing mechanism that is backward compatible with the original implementation of KAD. Our scheme features the following properties: 1) it drives the process that selects the location of peers responsible to store references to objects, based on object popularity; 2) it solves problems related to saturated peers that would otherwise inflict a significant drop in the diversity of references to objects, and 3) if coupled with a load-aware content search procedure, it allows for a more fair and efficient usage of peer resources.