Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing mobile robot learning rates through sharing of experiences

Describes a learning algorithm for autonomous mobile robots based on sets of fuzzy automata. The task the robots have to learn is how to avoid obstacles reactively. The approach presented is one in which two or four robots learn simultaneously, with the experiences of each robot being passed onto the others. It is shown that this sharing of experiences results in faster and more robust learning than is the case without sharing.
Fast influence-based coarsening for large networks

Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications. In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm COARSENET for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of COARSENET, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.
Universal Inhibitor Petri Net

The universal inhibitor Petri net was constructed that executes an arbitrary given inhibitor Petri net. The inhibitor Petri net graph, its marking and the transitions firing sequence were encoded as 10 scalar nonnegative integer numbers and represented by corresponding places of universal net. The algorithm of inhibitor net executing that uses scalar variables only was constructed on its state equation and encoded by universal inhibitor Petri net. Subnets which implement arithmetic, comparison and copying operations were employed.
Help Me Help You: Shared Reflection for Personal Data

Dramatic advances in sensor and computing miniaturization for personal data collection are making Personal Informatics (PI) tools a reality. Yet, advances in data collection have not been matched with similar advances in tools to promote, support, and facilitate reflection on this data. This gap leaves people with large swaths of data, but very little understanding of how to make sense of the data or to derive actionable insights. In this work, we explore a process called shared reflection, where individuals are paired with other data collectors, and asked (through prompts) to reflect on one another? s data. Based on a six-week study where 15 participants collected different kinds of personal data and engaged in a shared reflection process, we show that participants gained transformative insights from others' reflections on their data. While this was promising, we discuss practical challenges in deploying this idea into real world personal informatics tools. In particular, while shared reflection can be appropriated to effectively bootstrap reflection on one's data, this needs to be balanced against privacy and control concerns.
A meta-analysis of the training effectiveness of virtual reality surgical simulators

The increasing use of virtual reality (VR) simulators in surgical training makes it imperative that definitive studies be performed to assess their training effectiveness. Indeed, in this paper we report the meta-analysis of the efficacy of virtual reality simulators in: 1) the transference of skills from the simulator training environment to the operating room, and 2) their ability to discriminate between the experience levels of their users. The task completion time and the error score were the two study outcomes collated and analyzed in this meta-analysis. Sixteen studies were identified from a computer-based literature search (1996-2004). The meta-analysis of the random effects model (because of the heterogeneity of the data) revealed that training on virtual reality simulators did lessen the time taken to complete a given surgical task as well as clearly differentiate between the experienced and the novice trainees. Meta-analytic studies such as the one reported here would be very helpful in the planning and setting up of surgical training programs and for the establishment of reference `learning curves' for a specific simulator and surgical task. If any such programs already exist, they can then indicate the improvements to be made in the simulator used, such as providing for more variety in their case scenarios based on the state and/or rate of learning of the trainee
Coronary Hyperemic Dose Responses of Intracoronary Sodium Nitroprusside

Background—Sodium nitroprusside is one of several agents considered effective for treating the no-reflow phenomenon during acute coronary interventions. However, the coronary hyperemic dose responses and systemic hemodynamic effects of intracoronary nitroprusside have yet to be determined in humans. The purpose of this study was to compare the hyperemic and hemodynamic responses of intracoronary nitroprusside to intracoronary adenosine in patients during cardiac catheterization with angiographically normal anterior descending arteries. Methods and Results—In 21 patients, coronary blood flow velocity (0.014-inch Doppler flow wire), heart rate, and blood pressure were measured in unobstructed left anterior descending coronary arteries at rest, after intracoronary adenosine (30- to 50-&mgr;g boluses), and after 3 serial doses (0.3-, 0.6-, and 0.9-&mgr;g/kg boluses) of intracoronary nitroprusside. Coronary reserve was calculated as hyperemia/basal coronary flow velocity. In an additional 9 patients with intermediate stenoses (53±7%), 14 fractional flow reserve (FFR) measurements (using 0.014-inch pressure wire) were performed with both intracoronary adenosine and nitroprusside (0.6 &mgr;g/kg). Intracoronary nitroprusside produced equivalent coronary hyperemia with a longer duration (≈25%) compared with intracoronary adenosine. Intracoronary nitroprusside (0.9 &mgr;g/kg) decreased systolic blood pressure by <20%, with minimal change in heart rate, whereas intracoronary adenosine had no effect on these parameters. FFR measurements with intracoronary nitroprusside were identical to those obtained with intracoronary adenosine (r =0.97). Conclusions—Compared with adenosine, intracoronary nitroprusside produces an equivalent but more prolonged coronary hyperemic response in normal coronary arteries. Intracoronary nitroprusside, in doses commonly used for the treatment of the no-reflow phenomenon, can produce sustained coronary hyperemia without detrimental systemic hemodynamics. On the basis of FFR measurements compared with adenosine, sodium nitroprusside also appears to be a suitable hyperemic stimulus for coronary physiological measurements.
Quantifying Constructional Productivity with Unseen Slot Members

This paper is concerned with the possibility of quantifying and comparing the productivity of similar yet distinct syntactic constructions, predicting the likelihood of encountering unseen lexemes in their unfilled slots. Two examples are explored: variants of comparative correlative constructions (CCs, e.g. the faster the better), which are potentially very productive but in practice lexically restricted; and ambiguously attached prepositional phrases with the preposition with, which can host both large and restricted inventories of arguments under different conditions. It will be shown that different slots in different constructions are not equally likely to be occupied productively by unseen lexemes, and suggested that in some cases this can help disambiguate the underlying syntactic and semantic structure.
Iterative symbol decoding of convolutionally-encoded variable-length codes

In this paper, we present a symbol-level iterative source-channel decoding algorithm for variable-length codes (VLCs). First a soft-input source decoder for VLC-encoded data is derived by modification of the BCJR forward-backward recursion and adaptation to the nonstationary VLC trellis. Also proposed is a recursive implementation based on sectionalized code trellises for MAP symbol decoding of binary convolutional codes. This allows to use a merged trellis representation for a VLC and a convolutional channel code and exploit the residual source redundancies as a priori information on a code trellis. Simulation results indicate that the proposed iterative decoder allows to exchange between its constituent decoders the symbol-level extrinsic information and achieves high robustness against channel noises.
A time-evolution model for the privacy degree of information disseminated in online social networks

People tend to share private information with their friends on online social networks OSNs. The common position is that the shared information eventually reaches all users of the network since OSNs exhibit the small-world property. However, dissemination of private information in an OSN exhibits a set of factors that need to be accounted for in order to create more realistic models of the evolution of the privacy degree of information disseminated in an OSN. Among these factors are relationship strength between communicating users, influence of neighbours i.e., friends, users' adoption of new information, change of information, and dynamics of the structure of OSNs. This paper proposes a time series model for measuring the privacy of information disseminated in an OSN using the factors listed above. It shows through simulating the dissemination of private information in an OSN that the privacy of information does not vanish, but in most cases declines to a saturation level related to the information dissemination factors. The results also show how likely a user can get the information when the factors are accounted for.
Simultaneous Determination of Four Tanshinones by UPLC-TQ/MS and Their Pharmacokinetic Application after Administration of Single Ethanol Extract of Danshen Combined with Water Extract in Normal and Adenine-Induced Chronic Renal Failure Rats

Salvia miltiorrhiza, one of the major traditional Chinese medicines, is commonly used and the main active ingredients—tanshinones—possess the ability to improve renal function. In this paper, the UPLC-TQ/MS method of simultaneously determining four tanshinones—tanshinone IIA, dihydrotanshinone I, tanshinone I, and cryptotanshinone—was established and applied to assess the pharmacokinetics in normal and chronic renal failure (CRF) rat plasma. The pharmacokinetics of tanshinones in rats were studied after separately intragastric administration of Salvia miltiorrhiza ethanol extract (SMEE) (0.65 g/kg), SMEE (0.65 g/kg) combined with Salvia miltiorrhiza water extract (SMWE) (1.55 g/kg). The results showed Cmax and AUC0–t of tanshinone IIA, tanshinone I, cryptotanshinone reduced by 50%~80% and CLz/F increased by 2~4 times (p < 0.05) in model group after administrated with SMEE. Nevertheless, after intragastric administration of a combination of SMWE and SMEE, the Cmax and AUC0–t of four tanshinones were upregulated and CLz/F was downregulated, which undulated similarity from the model group to the normal group with compatibility of SMEE and SMWE. These results hinted that SMWE could improve the bioavailability of tanshinones in CRF rats, which provides scientific information for further exploration the mechanism of the combination of SMWE and SMEE and offers a reference for clinical administration of Salvia miltiorrhiza.
Economic Development through Business Profiling: A Text Analysis Based Approach

The tremendous improvements in the field of web technologies have contributed to the accumulation of large amounts of text data, particularly in the form of websites. Among others, most businesses, smaller or bigger, present themselves to the world through their websites. Economic development analysts could make use of the information available on business websites to identify ways in which businesses in a region can be grouped together into clusters, and possibly partnership for mutual benefit, and thus for economics gains of that particular region. Automated clustering of businesses is especially useful, as the existing NAICS codes-based clustering is not very accurate, according to domain experts, and does not scale up well. Text analysis is a blooming field whose goal is to automatically extract useful information from natural language text. In this work, we perform a preliminary text analysis of business websites to build business profiles and to organize businesses into clusters. Our approach is based on representing businesses as a mixture of ``topics" using a technique called Latent Dirichlet Allocation (LDA). Given the business profiles represented as the topic distributions obtained with LDA, we construct preliminary clusters of businesses. Manual analysis of the results shows that the proposed approach has the potential for giving interesting and useful clusters, which have the potential to replace the existing NAICS codes-based clusters. We identify further challenges associated with the existing business data, and several ideas for future work.
Biologically inspired intrinsically motivated learning for service robots based on novelty detection and habituation

The effective operation of service robots relies on developmental programs that allow the robot to expand its knowledge about its dynamic operating environment. Motivation theories from neuroscience and neuropsychology study the underlying mechanisms that drive the engagement of biological creatures to certain activities, such as learning. This research uses a physical Willow Garage PR2 robot, which is equipped with a cumulative learning mechanism driven by the intrinsic motivation of novelty detection based on computational models of biological habituation. It cumulatively learns the 360° appearance of novel real-world objects by picking them up. This paper discusses the theoretical motivations and background information on intrinsic motivations as novelty detection. The results and conclusions from the experimental study are presented.
A FRAMEWORK FOR MODELING OCCUPANCY SCHEDULES AND LOCAL TRIPS BASED ON HUMAN ACTIVITY PATTERNS IN THE CITY

This paper presents a methodology for the generation of building occupancy schedules and trips in urban areas based on regional activity-based travel surveys. Using eigendecomposition-based Principal Component Analysis (PCA) and the k-means clustering algorithm, the method identifies activities as well as city resident types. The outputs are typical occupancy schedules for building performance simulation, and activity profiles for mobility modeling in urban-scale simulation. As a proof of concept the framework is applied to the 2010/2011 Massachusetts Department of Transportation (MassDOT) travel survey. Four clusters of occupants were identified, and an example building occupancy schedule and complementary activity distributions were determined. A discussion about how such activity profiles can be used to generate a set of user behavior profiles for neighborhood and city models is presented.
A middleware design for large-scale clusters offering multiple services

We present a decentralized design that dynamically allocates resources to multiple services inside a global server cluster. The design supports QoS objectives (maximum response time and maximum loss rate) for each service. A system administrator can modify policies that assign relative importance to services and, in this way, control the resource allocation process. Distinctive features of our design are the use of an epidemic protocol to disseminate state and control information, as well as the decentralized evaluation of utility functions to control resource partitioning among services. Simulation results show that the system operates both effectively and efficiently; it meets the QoS objectives and dynamically adapts to load changes and to failures. In case of overload, the service quality degrades gracefully, controlled by the cluster policies.
Surrogate utility estimation by long-term partners and unfamiliar dyads

To what extent are people able to make predictions about other people's preferences and values? We report two experiments that present a novel method assessing some of the basic processes in surrogate decision-making, namely surrogate-utility estimation. In each experiment participants formed dyads who were asked to assign utilities to health related items and commodity items, and to predict their partner's utility judgments for the same items. In experiment one we showed that older adults in long-term relationships were able to accurately predict their partner's wishes. In experiment two we showed that younger adults who were relatively unfamiliar with one another were also able to predict other people's wishes. Crucially we demonstrated that these judgments were accurate even after partialling out each participant's own preferences indicating that in order to make surrogate utility estimations people engage in perspective-taking rather than simple anchoring and adjustment, suggesting that utility estimation is not the cause of inaccuracy in surrogate decision-making. The data and implications are discussed with respect to theories of surrogate decision-making.
A CASE tool for modelling and automatically generating web service-enabled applications

This paper presents a CASE tool for the high-level specification of web applications integrated with web services. The CASE tool is based on WebML, a conceptual modelling language used for the specification of the front end of web applications. WebML, extended with new primitives for modelling web services, allows one to specify complex interactions between a web application, driven by the user, and remote processes, represented by services. The CASE tool also allows the specification and deployment of application functionalities as web services and automatically generates the running code, including web pages, web services calls and the exposed web services. In this paper, we describe the primitives for web services modelling, the architecture of the CASE tool for designing the applications, and the runtime architecture of the generated applications. Finally, we give an overview of some industrial applications developed with the described approach.
A hybrid intelligent algorithm for short-term energy price forecasting in the Ontario market

Price forecasting is a crucial information for market participants in an electricity market. However, the electricity price is a complex signal due to its nonlinearity, stochasticity, and time dependent behavior. This paper presents a novel hybrid intelligent algorithm that uses the combination of a data filtering technique based on the wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network. The innovative contribution of this paper is an application of the FF algorithm to optimize FA network that utilizes the historical ill-behaved energy price time-series through the WT. Good forecast performance and adaptability of the proposed hybrid WT+FF+FA m o d e l to changes in the data is illustrated using the Ontario market power system data. The test results demonstrates that the proposed hybrid technique is able to improve the day-ahead price forecasting performance significantly when compared with other conventional soft computing models available in literature.
The uterine tubal fluid: secretion, composition and biological effects

Gamete transport, sperm capacitation, fertilization, and early embryo development are all physiological events that occur in a very synchronized manner within the uterine tubal lumen. The tubal fluid that bathes the male and female gametes allows these events to occur in vivo much more successfully than in vitro. Collection of tubal fluid from domestic females has been performed by different methods. The amount of fluid secreted by the uterine tube increases during estrus and decreases during diestrus and pregnancy. The ampulla produces approximately two thirds of the total daily secretion, while the isthmus supplies the rest. Steroid hormones qualitatively and quantitatively modify the tubal fluid, through both a direct effect on epithelial cells and indirectly through their action on the vascular bed. Estrogen predominantly stimulates while progesterone inhibits tubal fluid secretion. The concentration of nutrients in tubal fluid is generally below plasma concentrations which suggests an overall transport of nutrients across the tube, mainly by diffusion. High potassium levels in the tubal fluid seem to be constant across species and is an important factor to consider when designing fertilization culture medium. The main energy substrates in tubal fluid are glucose and pyruvate derived from blood, although some sucrose and lactate are also present. In the rabbit tube, 25% of lactate is filtered from the blood and 75% is produced from vascular glucose by the tubal epithelial cells. Twenty-five free amino acids were present in the tubal fluid of cows, ewes, pigs, rabbits, and mice. Glycine was found in the highest concentrations in all the species studied, but levels of free amino acids varied across species and also during the estrous cycle. Protein concentration in the fluid is approximately 10-15% of that in serum. Albumin and immunoglobulin G, derived from the blood stream, are the most common proteins representing about 95% of the total protein of the tubal content. Tubal-specific glycoproteins produced by the epithelial cells have been identified and characterized in several species. Prostaglandins, steroid hormones, and growth factors have also been found in the tubal fluid and likely play important roles in tubal events. Continuous changes that occur in the secretory patterns throughout the estrous cycle and among the different regions of the tube indicate the existence of systemic and local controlling mechanisms of tubal fluid production.
The Secure Shell (ssh) Transport Layer Protocol

The Secure Shell (SSH) is a protocol for secure remote login and other secure network services over an insecure network. This document describes the SSH transport layer protocol, which typically runs on top of TCP/IP. The protocol can be used as a basis for a number of secure network services. It provides strong encryption, server authentication, and integrity protection. It may also provide compression.