Miracle-dz/UniFAR-FTUs huggingface dataset:
Profile Extractor Test Extractor Develop Extractor Input Documents Label Text / Clues Task Analysis Rule Development Performance Tuning Delivery Export Extractor

Information extraction (IE) is becoming a critical building block in many enterprise applications. In order to satisfy the increasing text analytics demands of enterprise applications, it is crucial to enable developers with general computer science background to develop high quality IE extractors. In this demonstration, we present WizIE, an IE development environment intended to reduce the development life cycle and enable developers with little or no linguistic background to write high quality IE rules. WizIE provides an integrated wizard-like environment that guides IE developers step-by-step throughout the entire development process, based on best practices synthesized from the experience of expert developers. In addition, WizIE reduces the manual effort involved in performing key IE development tasks by offering automatic result explanation and rule discovery functionality. Preliminary results indicate that WizIE is a step forward towards enabling extractor development for novice IE developers.
Mountain Plains

Enterprise Resource Planning (ERP) has enabled organizations to connect vendors, the organization, and customers in an almost seamless manner with real-time accurate communication and information. In an increasingly competitive environment, businesses are looking for ways to become more competitive within their marketplace. Many are turning to ERP solutions to facilitate multiple competitive priorities to stand above competing businesses or to create a new competitive advantage. Critical success factors and change management are the primary vehicles for the achievement of success in the adoption of these systems. This research studies how change management becomes a significant link to successful ERP implementation.
Enhanced Discoverability of Content through Linked Data for Online Reviews using Classification and Ranking Techniques

Massive unstructured data are available and being posted in numerous blogs, forums, and online sites. This enormous amount of information on worldwide network platforms make them feasible and can be used as input source, in applications based on opinion mining and sentiment analysis. The aim of this paper is to analyze online reviews in unstructured form and discover content through linked data and deriving an opinion. Our proposed methodology comprises of phases such as Data pre-processing, content discovery and Opinion mining. Initially the unstructured data is extracted from the web document. This phase is used for formatting the data before sentiment analysis and mining. The second phase will be classified into two i.e., Feature extraction, content discovery and opinion extraction. The features like term frequency, Part of Speech are extracted from the words in the documents. After feature extraction, we extract useful information related to the item’s features and use it to rate them as positive, neutral, or negative. This final phase will be done by supervised learning algorithm decision tree classifier with the help of features extracted. In the final step ranking and classification will be done.
To appear in Theory of Computing Systems (Special Issue for SPAA’97) Can a Shared-Memory Model Serve as a Bridging Model for Parallel Computation?

There has been a great deal of interest recently in the development of general-purpose bridging models for parallel computation. Models such as the bsp and logp have been proposed as more realistic alternatives to the widely-used pram model. The bsp and logp models imply a rather di erent style for designing algorithms when compared to the pram model. Indeed, while many consider data parallelism as a convenient style, and the shared-memory abstraction as an easyto-use platform, the bandwidth limitations of current machines have diverted much attention to message-passing and distributed-memory models (such as the bsp and logp) that account more properly for these limitations. In this paper we consider the question of whether a shared-memory model can serve as an e ective bridging model for parallel computation. In particular, can a shared-memory model be as e ective as, say, the bsp? As a candidate for a bridging model, we introduce the Queuing Shared Memory (qsm) model, which accounts for limited communication bandwidth while still providing a simple shared-memory abstraction. We substantiate the ability of the qsm to serve as a bridging model by providing a simple work-preserving emulation of the qsm on both the bsp, and on a related model, the (d;x)-bsp. We present evidence that the features of the qsm are essential to its e ectiveness as a bridging model. In addition, we describe scenarios in which the high-level qsm more accurately models certain machines than the more detailed bsp and logp models. Finally, we present algorithmic results for the qsm, as well as general strategies for mapping algorithms designed for the bsp or pram models onto the qsm model. Our main conclusion is that shared-memory models can potentially serve as viable alternatives to existing message-passing, distributed-memory bridging models. A preliminary version of this paper appeared in Proc. 9th ACM Symp. on Parallel Algorithms and Architectures, pages 72{83, June 1997. Information Sciences Research Center, Bell Laboratories (Lucent Technologies), 600 Mountain Avenue, Murray Hill NJ 07974. email: gibbons@research.bell-labs.com Information Sciences Research Center, Bell Laboratories (Lucent Technologies), 600 Mountain Avenue, Murray Hill NJ 07974. Current address is Tel-Aviv University, Ramat Aviv, Tel-Aviv 69978 Israel. Email: matias@math.tau. ac.il. Dept. of Computer Sciences, University of Texas at Austin, Austin TX 78712. email: vlr@cs.utexas. edu. This work was supported in part by NSF grant CCR/GER-90-23059.
Predicting the chances of live birth for couples undergoing IVF-ICSI : a novel instrument to advise patients and physicians before treatment [ version 1 ; peer review : awaiting peer review ]

In developed countries, the prevalence of infertility ranges Background: from 3.5% to 16.7%. Therefore, the number of fertilization technique in vitro (IVF) and its subtype intracytoplasmic sperm injection (ICSI) treatments has been significantly increasing across Europe. Several factors affect the success rate of treatments, which can be used to calculate the in vitro probability of success for each couple. As these treatments are complicated and expensive with a variable probability of success, the most common question asked by IVF patients is ‘‘What are my chances of conceiving? ”. The main aim of this study is to develop a validated model that estimates the chance of a live birth before they start their IVF non-donor cycle. A logistic regression model was developed based on the Methods: retrospective study of 737 IVF cycles. Each couple was characterized by 14 variables (woman’s and man’s age, duration of infertility, cause of infertility, woman’s and man’s body mass index (BMI), anti-Müllerian hormone (AMH), antral follicle count (AFC), woman’s and man’s ethnicity, woman’s and man’s smoking status and woman’s and man’s previous live children) and described with the outcome of the treatment "Live birth" or "No live birth". The model results showed that from the 14 variables acquired Results: before starting the IVF procedures, only male factor, man’s BMI, man's mixed ethnicity and level of AMH were statistically significant. The interactions between infertility duration and woman’s age, infertility duration and man’s BMI, AFC and AMH, AFC and woman’s age, AFC and woman’s BMI and AFC and disovulation were also statistically significant. The area under the receiver operating characteristic (AUROC) curve test for the discriminatory ability of the final prediction model is 0.700 (95% confidence interval (CI) 0.660–0.741). This model might result in a new validated decision support Conclusions: system to help physicians to manage couples’ expectations. 1 1 2
Independent Component Analysis

Abstract: Given a set of M signal mixtures (x 1 ;x 2 ;:::;x M ) (e.g. microphone out- puts), each of which is a dierent mixture of a set of M statistically independent source signals (s 1 ;s 2 ;:::;s M ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically independent and that they have non-Gaussian dis- tributions. Dierent physical processes usually generate statistically independent and non-Gaussian signals, so that, in the process of extracting such signals from a set of signal mixtures, ICA eectively recovers the underlying physical causes for a given set of measured signal mixtures.
Effect of Bridge Pier Geometry on Local Scouring

: Local scour around bridge pier is the main reason for failure of a hydraulic structure like as bridge piers, abutment etc. Local scour around a bridge pier is largely depending on the shape of the bridge pier and how the design is fashionable from the view of construction. Local scour is a complex phenomenon which depends on the discharge, depth of flow, geometry of the pier and type of sediment particle. In this study, geometry i.e. shape of the pier is the main concern. Research is carried out by considering three different geometry pier of rectangular, circular and oblong shape to conclude the optimal shape of the pier at a different flow velocity of 0.146m/s, 0.231 m/s & 0.323 m/s on 15m hydraulic flume under clear water condition using natural sand as the bed material in the university laboratory. Results show an idea that the scouring at the upstream face is directly proportional to the exposed upstream nose area of the pier. It is also shows that the scouring is highest in all cases for the rectangular pier and minimum for the oblong pier.
A feasibility study of the predictive value of peEr's observations to estimate human states

This paper examines the predictive value of peer’s observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor’s data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant’s smartphone, and peer-derived features (pDFs) from the peer’s assessments. We evaluated the pDFs on a binary classiﬁcation task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classiﬁcation accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classiﬁcation accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The ﬁndings are encouraging about the incorporation of peer’s observations in machine learning with potential beneﬁts in the ﬁelds of personal sensing and pervasive computing, especially for mental health and well-being. Abstract This paper examines the predictive value of peer’s observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor’s data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant’s smartphone, and peer-derived features (pDFs) from the peer’s assessments. We evaluated the pDFs on a binary classiﬁcation task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classiﬁcation accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classiﬁcation accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The ﬁndings are encouraging about the incorporation of peer’s observations in machine learning with potential beneﬁts in the ﬁelds of personal sensing and pervasive computing, especially for mental health and well-being.
GENERALIZATIONS WITH PROBABILITY DISTRIBUTIONS FOR DATA ANONYMIZATION

Anonymization based privacy protection ensures that data cannot be traced to an individual. Many anonymify algorithms proposed so far made use of d~fferent value generalization techniques to satisfy d~jferent privacy constraints. This paper presents pdf-generalization merhod that empowers data value generalizations with probability distribution functions enabling the publisher to have better control over the trade off between privacy and utilization. We evaluate the pdf approach for k-anonymity and 6-presence privacy models and show how to use pdf generalizations to utilize datasets even further without violating privacy constraints. Paper also shows theoretically and experimentally that information gained from pdfs increases the utilization of the anonymized data w.r. t. real world applications such as class$cation and association rule
SHARE @ Children's Mercy SHARE @ Children's Mercy

burden. Evidence of improvement in both adherence and patients ’ impairment due to asthma were seen only with interactive interventions involving two-way responsive patient communication. Digital interventions were generally positively perceived by patients and physicians. Implementation was considered feasible, with certain preferences for design and features important to drive use. CONCLUSIONS: Digital health interventions show substantial promise for asthma disease monitoring and personalization of treatment. To be successful, future interventions will need to include both inhaler device and software elements, combining accurate measurement of clinical parameters with careful consideration of ease of use, personalization, and patient engagement aspects. (cid:1) 2021 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). (J Allergy Clin Immunol Pract 2021;9:2377-98)
Neighborhood watch : early detection and rapid response to biological invasion along US trade pathways

The growing pace of international commerce has created an increased risk of invasion by alien species, which often take root and thrive in their new environment, often at great costs for the local biota and economy. The risk of introduction of harmful organisms into the United States is increasing exponentially, with costs to the American economy measured in hundreds of billions of dollars. This report offers recommendations to improve biosecurity measures at US ports, as well as a possible funding mechanism based upon the polluter-pays principle.
To Fit or not to Fit: Unraveling Parameter Degeneracy in GRB Data Analysis

Gamma-ray burst (GRB) afterglow light curves and spectra provide information about the density of the environment, the energy of the explosion, the properties of the particle acceleration process, and the structure of the decelerating jet. Due to the large number of parameters involved, the model can present a certain degree of parameter degeneracy. In this paper, we present synthetic photometric observations of GRB afterglows and model them using the Markov Chain Monte Carlo (MCMC) method. This method has emerged as the preferred approach for analysing and interpreting data in astronomy. We show that, depending on the choice of priors, the parameter degeneracy can go unnoticed by the MCMC method. Furthermore, we apply the MCMC method to analyse the GRB 170817A afterglow. We find that there is a complete degeneracy between the energy of the explosion, the density of the environment, and the microphysical parameters describing the particle acceleration process, which cannot be determined by the afterglow light curve alone. Our results emphasise the importance of gaining a deep understanding of the degeneracy properties which can be present in GRB afterglows models, as well as the limitations of the MCMC method.
THE DYNAMICS OF COLLECTIVE SORTING ROBOT . LIKE ANTS AND ANT . LIKB ROBOTS

A distribured sorting algorithm, inspired by how ant colonies sort their brood, is presenæd for use by robot æams. The robots move randomly, do not communicate, have no hierarchical organisation, have no global representation, can only perceive objects just in front of them, but can distinguish between objects of rrvo or more types with a certain degree of error. The probability that they pick up or put down an object is modulaæd as a function of how many of the same objeca they have met in the. rêcent, pasL This generates a positive feed-back that is sufficient to coordinate the roboe activity, resulting in their soning the objecs inm commolr clusters: While less eflicient than a hierarchically controll€d sorting, this decenralised organisation offers rhe advantages of simplicity, flexibility and robushess.
The Complexity of the Hajos Calculus

The Hajos calculus is a simple, nondeterministic procedure that generates the class of non-3-colorable graphs. Mansfield and Welsch posed the question of whether there exist graphs that require exponential-sized Hajos constructions. Unless ${\rm NP} \neq {\rm coNP}$, there must exist graphs that require exponential-sized constructions, but to date, little progress has been made on this question, despite considerable effort. In this paper, we prove that the Hajos calculus generates polynomial-sized constructions for all non-3-colorable graphs if and only if extended Frege systems are polynomially bounded. Extended Frege systems are a very powerful family of proof systems for proving tautologies, and proving superpolynomial lower bounds for these systems is a long-standing, important problem in logic and complexity theory. We also establish a relationship between a complete subsystem of the Hajos calculus and bounded-depth Frege systems; this enables us to prove exponential lower bounds on this subsystem of the Hajos calculus.
Integrating images from multiple microscopy screens reveals diverse patterns of protein 2 subcellular localization change 3 4

1 Department of Computer Science, University of Toronto 7 2 Terrence Donnelly Centre for Cellular & Biomolecular Research, University of 8 Toronto 9 3 Department of Cell & Systems Biology, University of Toronto 10 4 Department of Electrical and Computer Engineering, University of Toronto 11 5 Department of Molecular Genetics, University of Toronto 12 6 Center for Analysis of Genome Evolution and Function, University of Toronto 13
Assessment of Conservation Measures for Olive Ridley Sea Turtle (Lepidochelys Olivacea) Along Rushikulya Rookery, Ganjam Disrict, Odisha, India

Every year, between October and April, Gahirmatha beach in Odisha becomes the nesting ground for Olive Ridley Turtles. These beautiful creatures are an endangered species, and this area is the world’s largest known rookery of the marine animals. Over 600,000 turtles come here together for nesting, but this area has not always been the safe haven for hatching the situation exists today. According to the International Union for Conservation of Nature and Natural Resources (IUCN), Olive Ridley Turtles are classed as a “vulnerable” marine species [1]. They derive their name from their heart-shaped, olive green-coloured shells. This mass nesting, popularly known as ‘arribada’ (a Spanish term used for en masse laying eggs by turtle species) is a yearly phenomenon. More than 4, 00,000 turtles arrive for nesting each year, each laying around 100–150 eggs in pits dug out instantly just before laying. The eggs, after an incubation period of 45 – 60 days, are hatched out yielding 60,000,000 hatchlings, which find their way to seas again. Only one in a thousand hatchlings survives. They take 25–30 years to reach maturity, and on attaining maturity, which usually takes 25 – 30 years the females then return to the same beach where they were born to lay their own eggs. The turtles migrate hundreds of kilometres for mating and nesting in this yearly breeding season, and every effort are initiated to ensure the mission successful. The Odisha government imposed a 7-month ban on fishing from November 2017 to protect the turtles following as the Orissa Marine Fishing Regulation Act, 1982, and Wildlife Protection Act, 1972. Nets are ensconced along the length of the beach to prevent the hatchlings from accidentally heading to the land instead of the sea. The Rushikulya Sea Turtle Protection Committee and The Orissa Marine Resources Conservation Consortium work towards conservation of the Olive Ridley Turtles proactively through formation of monitoring units at nesting sites, conduct regular beach patrols and training of guards and volunteers. They also publish and distribute educational material, conduct field trips and promote exhibitions during beach festivals to sensitize the public on the issue through sculptures and sand art. Many villagers are volunteers who are the go-to persons for fisheries, coast guards, forest departments, biologists, researchers and tourists. and are involved in turtle protection works for decades. They know exactly when the turtles will arrive, the best places to view them, where not to tread to avoid trampling the eggs incubating under the sand and when to expect the hatchlings. May 23, World Turtle Day is celebrated in these villages as ‘Turtle ka Birthday,’ a reminder of the millions of hatchlings that have made it safely to the water from these shores.
Opportunistic Beamforming with Single Beamforming Matrix for Virtual Antenna Arrays

Opportunistic beamforming is a promising technique for downlink channels in cellular systems. When opportunistic beamforming is to support multiple users at a time, beams should be formed to avoid interference and this requires the feedback of channel state information (CSI) from users to a base station (BS). With limited feedback, to minimize the impact of interference, the best beamforming matrix can be selected from a set of multiple beamforming matrices. Thus, when a different set of users are to be supported by a scheduler, a new beamforming matrix has to be selected, which may require excessive complexity. To avoid this problem, in this paper, we propose an opportunistic beamforming approach with single beamforming matrix. In addition, we show that the proposed approach suits for a virtual antenna array where multiple BS units form a transmit antenna array.