Miracle-dz/UniFAR-FTUs huggingface dataset:
Prometheus: a methodology for developing intelligent agents

As agents gain acceptance as a technology there is a growing need for practical methods for developing agent applications. This paper presents the Prometheus methodology, which has been developed over several years in collaboration with Agent Oriented Software. The methodology has been taught at industry workshops and university courses. It has proven effective in assisting developers to design, document, and build agent systems. Prometheus differs from existing methodologies in that it is a detailed and complete (start to end) methodology for developing intelligent agents which has evolved out of industrial and pedagogical experience. This paper describes the process and the products of the methodology illustrated by a running example.
Transcatheter closure of atrial septal defect and interatrial communications with a new self expanding nitinol double disc device (Amplatzer septal occluder): multicentre UK experience

OBJECTIVE To review the safety and efficacy of the Amplatzer septal occluder for transcatheter closure of interatrial communications (atrial septal defects (ASD), fenestrated Fontan (FF), patent foramen ovale (PFO)). DESIGN Prospective study following a common protocol for patient selection and technique of deployment in all participating centres. SETTING Multicentre study representing total United Kingdom experience. PATIENTS First 100 consecutive patients in whom an Amplatzer septal occluder was used to close a clinically significant ASD or interatrial communication. INTERVENTIONS All procedures performed under general anaesthesia with transoesophageal echocardiographic guidance. Interatrial communications were assessed by transoesophageal echocardiography with reference to size, position in the interatrial septum, proximity to surrounding structures, and adequacy of septal rim. Stretched diameter of the interatrial communications was determined by balloon sizing. Device selection was based on and matched to the stretched diameter of the communication. MAIN OUTCOME MEASURES Success defined as deployment of device in a stable position to occlude the interatrial communication without inducing functional abnormality or anatomical obstruction. Occlusion status determined by transoesophageal echocardiography during procedure and by transthoracic echocardiography on follow up. Clinical status and occlusion rates assessed at 24 hours, one month, and three months. RESULTS 101 procedures were performed in 100 patients (86 ASD, 7 FF, 7 PFO), age 1.7 to 64.3 years (mean (SD), 13.3 (13.9)), weight 9.2 to 100.0 kg (mean 32.5 (23.5)). Procedure time ranged from 30 to 180 minutes (mean 92.4 (29.0)) and fluoroscopy time from 6.0 to 49.0 minutes (mean 16.1 (8.0)). There were seven failures, all occurring in patients with ASD, and one embolisation requiring surgical removal. Immediate total occlusion rate was 20.4%, rising to 84.9% after 24 hours. Total occlusion rates at the one and three month follow up were 92.5% and 98.9%, respectively. Complications were: transient ST elevation (1), transient atrioventricular block (1), presumed deep vein thrombosis (1), presumed transient ischaemic attack (1). CONCLUSIONS It appears feasible to close interatrial communications and atrial septal defects up to 26 mm stretched diameter safely with the Amplatzer septal occluder. Short term results confirm an early high occlusion rate with no major complications. Careful selection of cases based on the echocardiographic morphology of the ASD and accurate assessment of their stretched diameter is of utmost importance. Further experience with the larger devices and longer term results are required before a firm conclusion regarding its use can be made.
Aedes aegypti on Madeira Island (Portugal): genetic variation of a recently introduced dengue vector

The increasing population of Aedes aegypti mosquitoes on Madeira Island (Portugal) resulted in the first autochthonous dengue outbreak, which occurred in October 2012. Our study establishes the first genetic evaluation based on the mitochondrial DNA (mtDNA) genes [cytochrome oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4)] and knockdown resistance ( kdr ) mutations exploring the colonisation history and the genetic diversity of this insular vector population. We included mosquito populations from Brazil and Venezuela in the analysis as putative geographic sources. The Ae. aegypti population from Madeira showed extremely low mtDNA genetic variability, with a single haplotype for COI and ND4. We also detected the presence of two important kdr mutations and the quasi-fixation of one of these mutations (F1534C). These results are consistent with a unique recent founder event that occurred on the island of Ae. aegypti mosquitoes that carry kdr mutations associated with insecticide resistance. Finally, we also report the presence of the F1534C kdr mutation in the Brazil and Venezuela populations. To our knowledge, this is the first time this mutation has been found in South American Ae. aegypti mosquitoes. Given the present risk of Ae. aegypti re-invading continental Europe from Madeira and the recent dengue outbreaks on the island, this information is important to plan surveillance and control measures.
Towards MapReduce based Bayesian deep learning network for monitoring big data applications

One of the most commonly used ways to monitor execution of software applications is by analyzing logs. Logs are execution foot-print of software applications that are produced and stored for real-time or post-execution analysis of execution. With the software applications becoming large, complex, distributed, web-scale, also called as big data applications, logs produced by such software applications are also large-scale. That means, such logs are large in volume, velocity and variety. That makes it crucial to have such logs analyzed in an automated, scalable and effective manner to ensure high veracity and have analytics with high value. In this paper, we present our proposed solution of a formal model for organizing and structuring logs. We then present a Bayesian deep learning network based analysis approach that utilizes the formal model for logs to detect and predict any possible faults and consequences of such faults. Moreover, we also present our MapReduce based distributed, parallel, single-pass and incremental approach to build, train and execute the proposed Bayesian deep learning framework. This helps in effective processing of logs on cloud platforms and therefore efficient handling of logs that are produced at the scale of big data by big data applications.
Multi-Document Summarization by Graph Search and Matching

We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out.
Weakly Supervised Learning of Image Partitioning Using Decision Trees with Structured Split Criteria

We propose a scheme that allows to partition an image into a previously unknown number of segments, using only minimal supervision in terms of a few must-link and cannot-link annotations. We make no use of regional data terms, learning instead what constitutes a likely boundary between segments. Since boundaries are only implicitly specified through cannot-link constraints, this is a hard and nonconvex latent variable problem. We address this problem in a greedy fashion using a randomized decision tree on features associated with interpixel edges. We use a it structured purity criterion during tree construction and also show how a backtracking strategy can be used to prevent the greedy search from ending up in poor local optima. The proposed strategy is compared with prior art on natural images.
On convergence of approximate message passing

Approximate message passing is an iterative algorithm for compressed sensing and related applications. A solid theory about the performance and convergence of the algorithm exists for measurement matrices having iid entries of zero mean. However, several authors have observed that for more general matrices the algorithm often encounters convergence problems. In this paper we identify the reason of the non-convergence for measurement matrices with iid entries and non-zero mean in the context of Bayes optimal inference. Finally we demonstrate numerically that when the iterative update is changed from parallel to sequential the convergence is restored.
An Automatic Page Link Generation Method based on Users' Behavior

In this paper, we propose a novel method for generating personalized page links. The page links which are generated by our proposed method are useful if users look for Web pages related to the current Web pages. To generate these page links, we proposed the following two steps, such as (1) divide current Web pages into the significant minimal blocks, and (2) Web pages related to these blocks. We confined in our experimental evaluation that users can find Web pages related to the users' interests.
Detecting Human Activity Profiles with Dirichlet Enhanced Inhomogeneous Poisson Processes

This paper describes an activity pattern mining method via inhomogeneous Poisson point processes (IPPPs) from time-series of count data generated in behavior detection by pyroelectric sensors. IPPP reflects the idea that typical human activity is rhythmic and periodic. We also focus on the idea that activity patterns are affected by exogenous phenomena, such as the day of the week, and weather condition. Because single IPPP could not tackle this idea, Dirichlet process mixtures (DPM) are leveraged in order to discriminate and discover different activity patterns caused by such factors. The use of DPM leads us to discover the appropriate number of the typical daily patterns automatically. Experimental result using long-term count data shows that our model successfully and efficiently discovers typical daily patterns.
A Maximum A Posteriori Probability and Time-Varying Approach for Inferring Gene Regulatory Networks from Time Course Gene Microarray Data

Unlike most conventional techniques with static model assumption, this paper aims to estimate the time-varying model parameters and identify significant genes involved at different timepoints from time course gene microarray data. We first formulate the parameter identification problem as a new maximum a posteriori probability estimation problem so that prior information can be incorporated as regularization terms to reduce the large estimation variance of the high dimensional estimation problem. Under this framework, sparsity and temporal consistency of the model parameters are imposed using L1-regularization and novel continuity constraints, respectively. The resulting problem is solved using the L-BFGS method with the initial guess obtained from the partial least squares method. A novel forward validation measure is also proposed for the selection of regularization parameters, based on both forward and current prediction errors. The proposed method is evaluated using a synthetic benchmark testing data and a publicly available yeast Saccharomyces cerevisiae cell cycle microarray data. For the latter particularly, a number of significant genes identified at different timepoints are found to be biological significant according to previous findings in biological experiments. These suggest that the proposed approach may serve as a valuable tool for inferring time-varying gene regulatory networks in biological studies.
Neuroanatomical and Functional Correlates of Cognitive and Affective Empathy in Young Healthy Adults

Neural substrates of empathy are mainly investigated through task-related functional MRI. However, the functional neural mechanisms at rest underlying the empathic response have been poorly studied. We aimed to investigate neuroanatomical and functional substrates of cognitive and affective empathy. The self-reported empathy questionnaire Cognitive and Affective Empathy Test (TECA), T1 and T2∗-weighted 3-Tesla MRI were obtained from 22 healthy young females (mean age: 19.6 ± 2.4) and 20 males (mean age: 22.5 ± 4.4). Groups of low and high empathy were established for each scale. FreeSurfer v6.0 was used to estimate cortical thickness and to automatically segment the subcortical structures. FSL v5.0. 10 was used to compare resting-state connectivity differences between empathy groups in six defined regions: the orbitofrontal, cingulate, and insular cortices, and the amygdala, hippocampus, and thalamus using a non-parametric permutation approach. The high empathy group in the Perspective Taking subscale (cognitive empathy) had greater thickness in the left orbitofrontal and ventrolateral frontal cortices, bilateral anterior cingulate, superior frontal, and occipital regions. Within the affective empathy scales, subjects with high Empathic Distress had higher thalamic volumes than the low-empathy group. Regarding resting-state connectivity analyses, low-empathy individuals in the Empathic Happiness scale had increased connectivity between the orbitofrontal cortex and the anterior cingulate when compared with the high-empathy group. In conclusion, from a structural point of view, there is a clear dissociation between the brain correlates of affective and cognitive factors of empathy. Neocortical correlates were found for the cognitive empathy dimension, whereas affective empathy is related to lower volumes in subcortical structures. Functionally, affective empathy is linked to connectivity between the orbital and cingulate cortices.
Using Probabilistic I/O Automata to Analyze an Oblivious Transfer Protocol

We demonstrate how to carry out cryptographic security analysis of distributed protocols within the Probabilistic I/O Automata framework of Lynch, Segala, and Vaandrager. This framework provides tools for arguing rigorously about the concurrency and scheduling aspects of protocols, and about protocols presented at dierent levels of abstraction. Consequently, it can help in making cryptographic analysis more precise and less susceptible to errors. We concentrate on a relatively simple two-party Oblivious Transfer protocol, in the presence of a semi-honest adversary (essentially, an eavesdropper). For the underlying cryptographic notion of security, we use a version of Canetti’s Universally Composable security. In spite of the relative simplicity of the example, the exercise is quite nontrivial. It requires taking many fundamental issues into account, including nondeterministic behavior, scheduling, resource-bounded computation, and computational hardness assumptions for cryptographic primitives.
A deployable scheme of CMT-SCTP with off-the-shelf android smartphones

We present a deployable scheme that achieves seamless handover in the context of heterogeneous wireless access networks based on concurrent multipath transfer (CMT) using the Stream Control Transmission Protocol (SCTP) with off-the-shelf Android smartphones and protocol translation servers. Since the scheme is accomplished by neither embedding special modules nor relying on super-user (root) privileges on off-the-shelf Android smartphones, it has considerable potential for general acceptance. In our scheme, we modified the mechanism of the dynamic address reconfiguration (DAR) extension of SCTP to ensure seamless handover, especially for the scenario where the mobile users frequently join/leave heterogeneous Wi-Fi networks that provide Internet access via network address translation (NAT) devices. We confirmed the feasibility of the scheme through prototype implementation.
An incentive-compatible multi-armed bandit mechanism

This paper presents a truthful sponsored search auction based on an incentive-compatible multi-armed bandit mechanism. The mechanism described combines several desirable traits. The mechanism gives advertisers the incentive to report their true bid, learns the click-through rate for advertisements, allows for slots with different quality, and loses the minimum welfare during the sampling process. The underlying generalization of the multi-armed bandit mechanism addresses the interplay between exploration and exploitation in an online setting that is truthful in high probability while allowing for slots of different quality. As the mechanism progresses the algorithm more closely approximates the hidden variables (click-though rates) in order to allocate advertising slots to the best advertisements. The resulting mechanism obtains the optimal welfare apart from a tightly bounded loss of welfare caused by the bandit sampling process. Of independent interest, in the field of economics it has long been recognized that preference elicitation is difficult to achieve, mainly as people are unaware of how much happiness a particular good will bring to them. In this paper we alleviate this problem somewhat by introducing a valuation-discovery process to the mechanism which results in a preference-elicitation mechanism for advertisers and search engines.
A CAM with mixed serial-parallel comparison for use in low energy caches

A novel, low-energy content addressable memory (CAM) structure is presented which achieves an approximately four-fold improvement in energy per access, compared to a standard parallel CAM, when used as tag storage for caches. It exploits the address patterns commonly found in application programs, where testing the four least significant bits of the tag is sufficient to determine over 90% of the tag mismatches; the proposed CAM checks those bits first and evaluates the remainder of the tag only if they match. Although, the energy savings come at the cost of a 25% increase in search time, the proposed CAM organization also supports a parallel operating mode without a speed loss but with reduced energy savings.
Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unified similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.
Fabrication and in vitro release behavior of a novel antibacterial coating containing halogenated furanone-loaded poly(L-lactic acid) nanoparticles on microarc-oxidized titanium

Background Dental implants have become increasingly common for the management of missing teeth. However, peri-implant infection remains a problem, is usually difficult to treat, and may lead eventually to dental implant failure. The aim of this study was to fabricate a novel antibacterial coating containing a halogenated furanone compound, ie, (Z-)-4-bromo-5-(bromomethylene)-2(5H)-furanone (BBF)-loaded poly(L-lactic acid) (PLLA) nanoparticles on microarc-oxidized titanium and to evaluate its release behavior in vitro. Methods BBF-loaded PLLA nanoparticles were prepared using the emulsion solvent-evaporation method, and the antibacterial coating was fabricated by cross-linking BBF-loaded PLLA nanoparticles with gelatin on microarc-oxidized titanium. Results The BBF-loaded PLLA nanoparticles had a small particle size (408 ± 14 nm), a low polydispersity index (0.140 ± 0.008), a high encapsulation efficiency (72.44% ± 1.27%), and a fine spherical shape with a smooth surface. The morphology of the fabricated antibacterial coating showed that the BBF-loaded PLLA nanoparticles were well distributed in the pores of the microarc oxidation coating, and were cross-linked with each other and the wall pores by gelatin. The release study indicated that the antibacterial coating could achieve sustained release of BBF for 60 days, with a slight initial burst release during the first 4 hours. Conclusion The novel antibacterial coating fabricated in this study is a potentially promising method for prevention of early peri-implant infection.
RFSIM: A feature based image quality assessment metric using Riesz transforms

Image quality assessment (IQA) aims to provide computational models to measure the image quality in a perceptually consistent manner. In this paper, a novel feature based IQA model, namely Riesz-transform based Feature SIMilarity metric (RFSIM), is proposed based on the fact that the human vision system (HVS) perceives an image mainly according to its low-level features. The 1st-order and 2nd-order Riesz transform coefficients of the image are taken as image features, while a feature mask is defined as the edge locations of the image. The similarity index between the reference and distorted images is measured by comparing the two feature maps at key locations marked by the feature mask. Extensive experiments on the comprehensive TID2008 database indicate that the proposed RFSIM metric is more consistent with the subjective evaluation than all the other competing methods evaluated.
The 2015 influenza A (H1N1) pdm09 outbreak in India

The influenza A(H1N1) pdm09 outbreak in India caused nearly 30,000 laboratory confirmed cases with more than 1700 deaths by March 15, 20151. Most of these cases were reported from the States of Rajasthan, Gujarat, Delhi, Jammu and Kashmir, Maharashtra, Madhya Pradesh, Telangana, Karnataka and Tamil Nadu2. In response to the upsurge of cases, the Central and State health departments initiated a number of activities including strengthening the surveillance; provision of laboratory support in terms of testing, quality assurance, supply of transport medium and diagnostic reagents; dissemination of guidelines for case management of cases and vaccination, making oseltamivir available through the public health system and schedule X chemists and educating the public through regular information, education and communication (IEC) activities3. We present here certain facts about the 2015 influenza A(H1N1) pdm09 outbreak and some lessons learnt for future.
Adaptive quantization for transform-domain Wyner-Ziv residual coding

As prediction processes are not in Wyner-Ziv encoder but in decoder, the Wyner-Ziv coding cannot perform better than conventional video encoder. In order to improve its compression efficiency, Wyner-Ziv residual coding has been proposed which implements a prediction process with low complexity in the encoder. Although it has a good performance in pixel domain, it does not have any improvements in transform domain, since pre-defined quantization matrices are not compatible with the Wyner-Ziv residual coding. In this paper, we propose a new quantization method for transform domain Wyner-Ziv residual coding. Experimental result shows up to 36% gain in BDBR and 3.09dB gain in BDPSNR.