Miracle-dz/UniFAR-FTUs huggingface dataset:
J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort.
Belief Propagation, Bethe Approximation and Polynomials

Factor graphs are important models for succinctly representing probability distributions in machine learning, coding theory, and statistical physics. Several computational problems, such as computing marginals and partition functions, arise naturally when working with factor graphs. Belief propagation is a widely deployed iterative method for solving these problems. However, despite its significant empirical success, several questions regarding the correctness and efficiency of belief propagation remain open. The Bethe approximation is an optimization-based method for approximating the partition functions. While it is known that the stationary points of the Bethe approximation coincide with the fixed points of belief propagation, in general, the relation between the Bethe approximation and the partition function is not well understood. It has been observed that for a few classes of factor graphs, the Bethe approximation gives a lower bound to the partition function, which distinguishes them from the general case, where neither a lower bound nor an upper bound holds universally. This has been rigorously proved for permanents and for attractive graphical models. Here, we consider bipartite factor graphs over binary alphabet and show that if the local constraints satisfy a certain analytic property, the Bethe approximation is a lower bound to the partition function, generalizing an analogous inequality between the permanent and the Bethe permanent of a matrix with non-negative entries. We arrive at this result by viewing the factor graphs through the lens of polynomials, which allows us to reformulate the Bethe approximation as an optimization problem involving polynomials. The sufficient condition for our lower bound property to hold is inspired by the recent developments in the theory of real stable polynomials. We believe that this way of viewing factor graphs and its connection to real stability might lead to a better understanding of belief propagation and factor graphs in general.
Efficient Online Resource Allocation in Heterogeneous Clusters with Machine Variability

Approximation jobs that allow partial execution of their many tasks to achieve valuable results have played an important role in today’s large-scale data analytics [1], [2]. This fact can be utilized to maximize the system utility of a big data computing cluster by choosing proper tasks in scheduling for each approximation job. A fundamental challenge herein, however, is that the machine service capacity may fluctuate substantially during a job’s lifetime, which makes it difficult to assign valuable tasks to well-performed machines. In addition, the cluster scheduler needs to make online scheduling decisions without knowing future job arrivals according to machine availabilities. In this paper, we tackle this online resource allocation problem for approximation jobs in parallel computing clusters. In particular, we model a cluster with heterogeneous machines as a multi-armed bandit where each machine is treated as an arm. By making estimations on machine service rates while balancing the exploration-exploitation trade-off, we design an efficient online resource allocation algorithm from a bandit perspective. The proposed algorithm extends existing online convex optimization techniques and yields a sublinear regret bound. Moreover, we also examine the performance of the proposed algorithm via extensive trace-driven simulations and demonstrate that it outperforms the baselines substantially.
Application of Optimization Algorithms in Perforating Procedures of Oil Wells

The perforating process is a significant step in the construction phase of oil wells. The procedure consists in using shaped explosive charges to open a tunnel through the casing and the rock, allowing the fluid to flow from the reservoir into the well, thus enabling the hydrocarbons production. Several factors influence in the perforating process, interfering in the productivity, such as density of shots, penetration depth, tunnel diameter, among others. Thus, optimization algorithms are useful in the design of perforating processes, intending at an optimum configuration for the perforating tunnel and consequently achieving greater productivity. Therefore, the objective of this paper is to present optimization studies to evaluate the efficiency of different algorithms and compare the optimal settings presented for each case. For this purpose, three synthesis and optimization models were used: Genetic Algorithm (GA), Artificial Immune System (AIS/CLONALG) and Particle Swarm Optimization (PSO). All of them presented satisfactory results for the problem; however the PSO showed better results in terms of number of evaluation and best fitness.
Genetic Stock Composition of Subyearling Chinook Salmon in Seasonal Floodplain Wetlands of the Lower Willamette River, Oregon

Abstract We used genetic identification methods to examine the stock composition of subyearling Chinook salmon Oncorhynchus tshawytscha in floodplain wetland and main-stem habitats of the lower Willamette River, Oregon. Using a microsatellite DNA baseline of 13 standardized loci and 30 Columbia River basin populations, we analyzed 280 subyearlings collected in winter and spring 2005-2006 from wetland and main-stem river sites. Genetic stock identification analysis indicated that spring Chinook salmon originating from the Willamette River made up a substantial proportion of the samples and contributed 16-71% to sample mixtures representing the wetland habitat sites. Fall Chinook salmon from lower Columbia River sources were also present and contributed 58% of winter samples. Spring Chinook salmon from lower Columbia River populations were present in both wetland (17%) and river (16%) samples in spring 2005, and subyearlings from summer-fall-run populations in the middle and upper Columbia River contributed.
Bayesian inference for quantiles of the log‐normal distribution

The log‐normal distribution is very popular for modeling positive right‐skewed data and represents a common distributional assumption in many environmental applications. Here we consider the estimation of quantiles of this distribution from a Bayesian perspective. We show that the prior on the variance of the log of the variable is relevant for the properties of the posterior distribution of quantiles. Popular choices for this prior, such as the inverse gamma, lead to posteriors without finite moments. We propose the generalized inverse Gaussian and show that a restriction on the choice of one of its parameters guarantees the existence of posterior moments up to a prespecified order. In small samples, a careful choice of the prior parameters leads to point and interval estimators of the quantiles with good frequentist properties, outperforming those currently suggested by the frequentist literature. Finally, two real examples from environmental monitoring and occupational health frameworks highlight the improvements of our methodology, especially in a small sample situation.
Mechanisms of Action of a Web-Based Intervention With Health Professional Support to Increase Adherence to Nebulizer Treatments in Adults With Cystic Fibrosis: Qualitative Interview Study

Background Adherence to nebulizer treatments in adults with cystic fibrosis (CF) is often low. A new complex intervention to help adults with CF increase their adherence to nebulizer treatments was tested in a pilot randomized controlled trial (RCT) in 2 UK CF centers. Patients used a nebulizer with electronic monitoring capabilities that transferred data automatically to a digital platform (CFHealthHub) to monitor adherence over time and to a tailored website to display graphs of adherence data and educational and problem-solving information about adherence. A trained interventionist helped patients identify ways to increase their adherence. Objective This study aims to explore the mechanisms of action underpinning the intervention. Methods A qualitative interview study was conducted concurrently with a pilot RCT. In total, 25 semistructured interviews were conducted with 3 interventionists at 2 time points, 14 patients in the intervention arm of the trial, and 5 members of the multidisciplinary teams offering wider care to patients. A framework approach was used for the analysis. Results The intervention was informed by a theoretical framework of behavior change. There was evidence of the expected behavior change mechanisms of action. There was also evidence of additional mechanisms of action associated with effective telehealth interventions for self-management support: relationships, visibility, and fit. Patients described how building a relationship with the interventionist through face-to-face visits with someone who cared about them and their progress helped them to consider ways of increasing adherence to medication. Rather than seeing the visibility of adherence data to clinicians as problematic, patients found this motivating, particularly if they received praise about progress made. The intervention was tailored to individuals, but there were challenges in how the intervention fitted into some patients’ busy lives when delivered through a desktop computer. Conclusions The mechanisms of action associated with effective telehealth interventions for self-management operated within this new intervention. The intervention was modified to strengthen mechanisms of action based on these findings, for example, delivery through an app accessed via mobile phones and then tested in an RCT in 19 UK CF centers. Trial Registration International Standard Randomized Controlled Trial Number 13076797; http://www.isrctn. com/ISRCTN13076797
Mobile information literacy: a preliminary outline of information behaviour in a mobile environment

The purpose of this paper is to examine the idea that information searching and use may vary with the widespread use of internet capable mobile devices. It highlights areas of existing literature that report variation in information searching and use on mobile devices, in addition to reporting on the results of a small set of semi-structured interviews. This was a small qualitative study, interviewing five experienced and confident users of mobile devices, which will not show the full range of mobile behaviour in seeking for and using information on the move. It gives a starting point to consider how information behaviour may change between fixed and mobile environments.
Risk of invasive prostate cancer and prostate cancer death in relatives of patients with prostatic borderline or in situ neoplasia: A nationwide cohort study

The question of whether having a family history of prostatic borderline or in situ neoplasia (PBISN) is associated with an increased risk of invasive prostate cancer (PCa) or death from PCa remains unanswered. The objective of the current study was to provide an evidence‐based risk estimation for the relatives of patients with PBISN.
The Value of Structured and Unstructured Content Analytics of Employees’ Opinion Mining

Employees and their knowledge are critical for a company’s success and were extensively covered by literature. Free form of knowledge, as opinions or feedback is analyzed in some companies in order to understand potential areas of improvement or satisfaction. This research focuses on analyzing free format opinions collected with a survey instrument from 586 employees of a bank. Various techniques as text analytics, word embedding, supervised and unsupervised learning were explored, extracting key concepts or entities. Attribution and relational similarity was explored using techniques as supervised text classification or unsupervised, by word vector representation and embedding using word2vdec. The aim was to explain overall satisfaction combining structured data (collected with closed questions survey, using 7 points Likert scale) enhanced with text classification of opinions, related to best and worst applications, along with explanations. The theoretical model used for ontology and taxonomy of text classification was based on Technology Acceptance Model, mapped into the main perceptual constructs, Perceived Ease of Use and Perceived Utility. The scope of research was the banks’ enterprise IT environment, not focused on a specific application. Various quantitative models, including linear models, decision trees and neural nets, were evaluated to capture potential causality of overall employee IT satisfaction level. The results suggest strong influence of perceptual constructs towards satisfaction, within all methods, while unstructured textual data provide additional insights on employees’ perception from concept associations.
Screening of latex producing plants for their milk clotting activity.

th Main, 18 th Cross, Jayanagar 3 ABSTRACT Latex of latex producing plants from Euphorbiaceae, Asclepiadaccae, and Apocyanaceae families were screened for their milk clotting efficiency. Latex enzymes were partially purified by salt or solvent precipitation. Partially purifiedproteases of Calotropis gigantea was found to possess high milk clotting activity (479±1 U/ml) and caseinolytic activity.However, partially purified Allamandacatharticalatex proteases showed relatively low caseinolytic activity and therefore a high milk clotting index (53.585 ± 0.105). Jatrophacurcus showed no milk clotting activity even after 24 hours of incubation of enzyme with the substrate.High ratio of milk clotting tocaseinolytic activityexhibited byA.catharticalatex proteasesindicates the suitability of this plantas apotential vegetable milk coagulantsource.
Innovations Around Mobile Applications: Scope for Indian Developers

This paper explores innovations around mobile applications, globally, and brings out the scope for monetization of these trends, by Indian Application Developers. The paper is based on qualitative research and utilizes Content Analysis Technique for tabulating and reporting the data. A thorough literature review has been conducted to understand the innovations around mobile applications globally and to identify the top 5 categories of mobile applications. These categories serve as variables for understanding the scope for Indian mobile application developers. Data on Indian Mobile application market has been sourced from mass media: Top 3 English newspapers (Indian) and 6 online sources. Recommendations for Indian developers have been generated on the basis on data which was reported under 5 identified categories of mobile applications. Scope for Indian Mobile application developers is in (but not limited to) the following areas; Developing cloud enabled applications, developing customised Indian applications, working on innovations based on global acceptability through multi-language apps. , synchronising data with backend systems and addressing security threats, reducing costs through prudent locations and newer API’s (Application Programming Interface), beta testing applications and appropriate keyword tagging for marketing and making applications oriented towards customer delight so that in the process of diffusion of the innovation, the application becomes an integral part of the customer’s life. Methodical content analysis brings out the scope for Indian application developers and offers them valuable insights on monetization tactics. Since the findings are based on published data from Indian Mass Media, it makes the paper very contemporary and the recommendations very implementable for the industry. Academicians working in this area too, will also find an avenue for further exploration. This research work is distinctive because of the lack of scholarly studies in this area, especially in the Indian context. Data from mass media and use of content analysis – a widely used qualitative analytic method for a scholarly study, increases its value by making the work recent, contemporary and worthy of further scholarly explorations.
Configuration and Management Tool for laboratory training

Electronic Laboratories have been growing during the last decade, but till now, configuring a complex automated system shared between several institutes and used for several disciplines, is a process restricted to qualified staff. Moreover, authoring and managing lab resources (programs, learning scenarios, documentation for both instructors and trainees) is a complex task as soon as the number of instructors, disciplines, different levels. grows up. This paper introduces a software tool aiming at helping in the lab resource management and session configuration.
Morphological changes of Rosa×hybrida by a chimeric repressor of Arabidopsis TCP3

Chimeric repressor gene-silencing technology is a useful tool for changing morphology of ornamental plants. It has previously been demonstrated that the chimeric repressor TCP3SRDX, which consists of Arabidopsis TCP3 and an ERF-associated amphiphilic repression motif repression domain, perturbs the marginal morphology of Arabidopsis leaves and flowers. To obtain new rose cultivars that have ornamental values, we attempted to alter the morphology of Rosa×hybrida cv. Lavande with TCP3SRDX. The TCP3SRDX transgenic rose plants showed interesting phenotypes: the number of leaflets and the size of leaf teeth increased, the petals were wavy, and the sepals were compound-leafy. We succeeded in altering rose morphology using Arabidopsis TCP3 without the sequence information of a TCP3 homologue in the target plant species.
Hybrid ARQ scheme using in-band full-duplex LoRa = Sistema ARQ Híbrido utilizando full-duplex dentro de banda en redes LoRa

Market studies have projected that there will be around 20-30 billions of objects connected to the Internet in 2020 (McKinsey[1]). LPWAN networks offer a solution for low-power and long-range requirements. This solution is attracting a lot of users and the prospective state that there will be around 3.1 million SIGFOX, LoRa and NB-IoT devices in 2024 (Analysis Mason[2]). One of the main attractions of this technology is that they operate in unlicensed sub-GHz frequency bands. However, in the case of LoRa, the networks might get saturated if the growth projections are fulfilled, because of the constraints that ISM bands impose. This problem is worse if ARQ schemes are required. There as on for these problems is that LoRa WAN standard and ISM band constraints limit the down link traffic and make it dependant on the uplink traffic. This Project presents a new scheme that would reduce the impact of the uplink traffic on the downlink one: using in-band full duplex communications. We build an ARQ LoRa full-duplex system that proves the concept of using full-duplex with LoRa. The system is based on an end-node that sends longer frames than usual that are composed of a normal LoRa frame and redundancy bits, that are sent while listening for an Acknowledgment (ACK) that comes from the gateway. Then, as soon as the end-node detects the ACK, it stops the transmission. This Project uses PHY-layer ACK messages and a modern cannel coding scheme: polar codes. As the PHY-layer ACK message, a series of LoRa downchirps was selected, so that another LoRa channel is not occupied but the signal is still orthogonal to the LoRa data frames. The results show that we can have an ARQ system that would potentially increase the capacity of the system and reduce the time for transmission (if we consider the time for retransmissions in a normal ARQ scheme).
Species richness of the genera Trachelomonas and Strombomonas (pigmented Euglenophyceae) in a subtropical urban lake in the Porto Alegre Botanical Garden, RS, Brazil

The objective of this study was to evaluate the taxonomic composition and richness of the genera Trachelomonas and Strombomonas (pigmented Euglenophyceae) in a subtropical urban lake (30o03'05"S; 51o10'34"W) within the Botanical Garden, in the city of Porto Alegre, located in the state of Rio Grande do Sul, Brazil, in order to contribute to the floristic knowledge of Euglenophyta in artificial urban lakes. From July 2007 to June 2008, we collected samples monthly from two sampling stations, using a 25-µm mesh plankton net. The samples were preserved in 4% formaldehyde. We evaluated the following environmental variables related to the water in the lake: ammonia content, silica content, organic matter content, dissolved oxygen concentration, temperature, pH, depth, transparency, electrical conductivity and rainfall. To study the species composition in relation to environmental factors, we used cluster analysis, principal components analysis, canonical correspondence analysis and indicator species analysis. At the specific and infraspecific level, 22 taxa were identified, Trachelomonas elliptica (Playf.) Defl. and T. gracillima Bal. & Dast. representing new records for the state of Rio Grande do Sul. The cluster analysis indicated seasonal variation, species richness being highest in the spring of 2007. Seven indicator species were characterized as acid-tolerant.
CO2 fluxes and drivers as affected by soil type, tillage and fertilization

Abstract Importance of agricultural practices for greenhouse gases mitigation is examined worldwide. However, there is no consensus on CO2 emissions as affected by soil management practices. Deeper understanding of soil CO2 fluxes and drivers under different management practices are needed. The investigation of net CO2 exchange rate as dependent variable and drivers (soil water and temperature, air temperature) as affected by soil type (loam and sandy loam), tillage (conservation and no-tillage) and fertilization are presented. Soil management practices and weather conditions affected the CO2 flux through effects on soil water and temperature regime. Mean net CO2 exchange rate on sandy loam was 8% higher than on loam. No-tillage, as a moisture-conserving tool, could be an appropriate tool for CO2 emissions mitigation in any weather conditions on sandy loam; however, the advantage of no-tillage on loam was negligible. Mineral NPK fertilizers promoted significantly higher net CO2 exchange rate in both soils, but suppressed it by 15% on sandy loam during a normal year. Effect of soil water content on net CO2 exchange rate was direct in all tillage and fertilization treatments in both loam and sandy loam, whereas this effect was positive only in dry and normal weather conditions. In wet weather conditions, the direct effect of soil water content on net CO2 exchange rate was negative. Soil and air temperature acted indirectly on net CO2 exchange rate. The increase in temperature markedly suppressed the positive direct impact of soil water content on net CO2 exchange rate in dry weather conditions, but did not reduce the direct effect of soil water content in normal weather conditions. In a wet year the negative indirect effect of increased temperature enhanced the negative direct impact of soil water surplus on net CO2 exchange rate.