Miracle-dz/UniFAR-FTUs huggingface dataset:
A Store Recommendation Procedure in Ubiquitous Market for User Privacy

personal information and privacy since it is not aware of the customer, exactly who they are. The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance [Shahabi and Banaei-Kashani, 2003]. Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.
A Novel Factor H–Fc Chimeric Immunotherapeutic Molecule against Neisseria gonorrhoeae

Neisseria gonorrhoeae, the causative agent of the sexually transmitted infection gonorrhea, has developed resistance to almost every conventional antibiotic. There is an urgent need to develop novel therapies against gonorrhea. Many pathogens, including N. gonorrhoeae, bind the complement inhibitor factor H (FH) to evade complement-dependent killing. Sialylation of gonococcal lipooligosaccharide, as occurs in vivo, augments binding of human FH through its domains 18–20 (FH18–20). We explored the use of fusing FH18–20 with IgG Fc (FH18–20/Fc) to create a novel anti-infective immunotherapeutic. FH18–20 also binds to select host glycosaminoglycans to limit unwanted complement activation on host cells. To identify mutation(s) in FH18–20 that eliminated complement activation on host cells, yet maintained binding to N. gonorrhoeae, we created four mutations in domains 19 or 20 described in atypical hemolytic uremic syndrome that prevented binding of mutated fH to human erythrocytes. One of the mutant proteins (D to G at position 1119 in domain 19; FHD1119G/Fc) facilitated complement-dependent killing of gonococci similar to unmodified FH18–20/Fc but, unlike FH18–20/Fc, did not lyse human erythrocytes. FHD1119G/Fc bound to all (100%) of 15 sialylated clinical N. gonorrhoeae isolates tested (including three contemporary ceftriaxone-resistant strains), mediated complement-dependent killing of 10 of 15 (67%) strains, and enhanced C3 deposition (≥10-fold above baseline levels) on each of the five isolates not directly killed by complement. FHD1119G/Fc facilitated opsonophagocytic killing of a serum-resistant strain by human polymorphonuclear neutrophils. FHD1119G/Fc administered intravaginally significantly reduced the duration and burden of gonococcal infection in the mouse vaginal colonization model. FHD1119G/Fc represents a novel immunotherapeutic against multidrug-resistant N. gonorrhoeae.
A REVIEW OF INTERNET OF THINGS (IOT) SYSTEMS IN TERMS OF SECURITY

The Internet of Things (IoT) has become one of the most important topics in our life nowadays. It is emer - ging everywhere because of its ability to integrate various things without human intervention. In this study, we focus on IoT security and how to protect an IoT system. We first explain IoT architecture and how it works. As building a well-established and clear architecture for IoT is fraught with many security challen - ges that can stand in the way of implementing the IoT, this study then presents the greatest of these chal - lenges and explains them in detail. Subsequently, it will explain the necessary procedures for protecting the IoT, which are distributed in layers so that these procedures can ensure authentication and confidentiality. Finally, the design integrity security strategy for protecting IoT is also identified.
Modelled Solute Acquisition from Suspended Sediment in a Theoretical 4-Cell Subglacial Hydrological System

Twenty-five laboratory dissolution experiments have been conducted to quantify rates of solute acquisition, measured as Ca concentration against time, from glacigenic sediments suspended in cold, dilute waters. Suspended sediment character was constrained by field-calibrated ranges of both concentration in meltwater (g cm) and specific surface area by sediment mass (cm g). This constraint yielded, for the first time in a glacier hydrochemical study, dissolution rate data as a function of the specific sediment surface area by water volume (SSAv; cm cm). The resulting experimental data have been used to calibrate a kinetic dissolution model (Brown et al., in press), where the rate of solute acquisition is considered in terms of an initial concentration reflecting rapid ion-exchange reactions (C0), an ultimate steady-state concentration (Cs), and a rate parameter (k). The results, which allow the dissolution equation parameters C0, Cs, and k to be expressed in terms of SSAv, are used here to drive dynamic predictive model of within-channel solute acquisition in a theoretical, 4-cell subglacial channel. Using realistic values of SSAv and residence time, this allows the magnitude of WSA to be predicted at any distance from the head of the channelised drainage system to the glacier snout.
Accident Analysis and Prevention

This study reports the results of a simulator-based assessment of driver response to two different urban highway work zone configurations. One configuration represented an existing design which was contrasted with a second configuration that presented a reduced taper length prototype work zone design. Twenty-one drivers navigated the two different work zones in two different conditions, one with and one without a lead vehicle; in this case a bus. Measures of driver speed, braking, travel path, and collision frequency were recorded. Drivers navigated significantly closer to the boundary of the work area in the reduced taper length design. This proximity effect was moderated by the significant interaction between lead vehicle and taper length and such interactive effects were also observed for driver speed at the end of the work zone and the number of collisions observed within the work zone itself. These results suggest that reduced taper length poses an increase in risk to both drivers and work zone personnel, primarily when driver anticipation is reduced by foreshortened viewing distances. Increase in such risk is to a degree offset by the reduction of overall exposure to the work zone that a foreshortened taper creates. The benefits and limitations to a simulation-based approach to the assessment and prediction of driver behavior in different work zone configurations are also discussed. © 2009 Elsevier Ltd. All rights reserved.
INVARIANTS IN THE EVOLUTION AND TERRITORIAL IDENTITY OF TRADITIONAL HUERTA LANDSCAPE AT VEGA BAJA OF SEGURA RIVER: 1929-2010

The Huerta landscape at Vega Baja of the Segura River is a clearly delimited area, resulting from man-made secular organization and adaptation of territory, with formal coherence and evident spatial structure. Historically, the population has accommodated their needs to the natural features of the environment, developing from experience specific rules for the sharing of water or measurement of land, among others. This system has supported a balanced dynamic between extreme climate and environmental conditions and has provided the opportunity to expand agricultural production along the alluvial plain (Canales, 1995). For these reasons, it is considered a cultural landscape with an outstanding value thanks to its unique qualities, different from other similar areas. This area is a well-known Huerta landscape for experts from many different disciplines. Its territorial unit represents a clear extended system, from Murcia to Alicante, belonging to the same river basin system, from “la Contraparada” or Major Dam, in Murcia, to its mouth in Guardamar del Segura, in the south of Alicante Province. This study covers the last stretch of the river, some 35 kilometers known as “Vega Baja” (“Low fertile floodplain”) because this fertile strip of land is at the lowest part of the river’s course. Local people often struggle with extreme weather conditions resulting in prolonged droughts and flash floods Floods are paradoxically a destructive force but at the same time the provider of essential nutrients to fertilize fields (Andrés, 2011; Canales, 1993; Calvo García-Tornel, 1972). Therefore, from time immemorial, the decision-making process for territorial organization was set by seeking solutions to minimize the destructive force of floods and enable a balanced water distribution during drought periods. Due to its unpredictable and extreme nature, the Segura River gained the reputation for being the most monitored European river for many decades (Gómez y Grindlay, 2008). Boletín de la Asociación de Geógrafos Españoles N.o 73 2017, págs. 531-535
7-SMART HOME AUTOMATION USING INTERNET OF THINGS (IOT)

If one discusses about a smart system using Internet, this type of control system is named Internet of Things (IoT). Internet of Things (IoT) is a concept that includes everyday objects. IoT is able to connect a large number of devices around the world. Different types of sensors are used to control smart systems. The inputs of the sensors are evaluated by a computer and outputs of the system act as actions to do something which will be benefit of humans. For example, a security camera system can be used as a distance to monitor the home, and thanks to alerts created by sensors, moving objects around the home can be detected immediately. By creating an instant alarm via camera system, malicious aliens approaching home can be disturbed. Evidence can be generated by remote recording. Zooming in the camera allows a clear view of the moving subjects. You can define which areas in the view of camera need to detect moving objects. IoT technology is used in real time to collect and detect human daily life data from different places. The Internet of Things (IoT) is the future of technology that can make our lives more efficient. This article is about a low-cost intelligent home automation system, all appliances, and electronic devices can be easily controlled through the mobile application. An experimental set was established to see how some devices can be controlled using Internet. For practice set an Arduino Mega 2560 board, various sensors and modules were used. In this paper, the parts of the set are explained and the control system is analyzed.
Quantitative nmr imaging of kiwifruit (actinidia deliciosa) during growth and ripening

Quantitative 1H magnetic resonance (MR) imaging was used to determine relaxation changes (T1, T2-CPMG) at regular intervals during growth and ripening of kiwifruit (Actinidia deliciosa var deliciosa). Temporal trends and differences between flesh, locule and core tissue were found for both relaxation parameters. However, no consistent associations were found between non-destructive measurements and those for individual free sugars, soluble solids content (SSC) and macronutrients and micronutrients determined on dissected companion samples. Increases of 200% in total free sugar concentration in flesh and 68% in SSC accompanied starch hydrolysis after harvest. Despite the magnitude of these changes, relaxation times remained unaltered. These observations were repeated in a second investigation using A arguta fruit and T1, T2, T2-CPMG and self-diffusion image contrasts. Here, SSC increased 125% during a compressed 15-day ripening period, while MR parameters like self-diffusion declined only 7–14% from harvest values. T2-CPMG relaxation was also investigated in aqueous solutions containing individual organic acids, sugars or pectate and juice from ripening fruit (4·7–15·5% SSC). Analysis of solutions and juices showed relaxation is indeed sensitive to increases in sugar composition but relatively insensitive to changes in organic acids and soluble pectin at concentrations normally found in fruit. Results imply that relaxation parameters determined from MR images may not be appreciably influenced by processes that cause solution composition to vary dramatically, even though these changes are reflected in the relaxation properties of the juice itself. Possible reasons for this are discussed with regard to the impact of cell structure and magnetic field strength on relaxation processes. © 1998 Society of Chemical Industry.
Identifying Candidate Informative Genes for Biomarker Prediction of Liver Cancer

Biomarker determination is a very important issue in medical research. Knowing the biomarker, better prognosis and diagnosis could be reached. Experimental prediction of biomarkers is lengthy and very costly. Recently, many studies have been conducted in the area of bioinformatics for In-silico prediction of biomarkers. This is assumed to save a lot of money while enabling us to explore many genetic issues. In this paper we provide a method to extract the most likely genes that could be considered as informative genes in microarrays of Hepatocellular Carcinoma. The used dataset is composed of fifty samples (19 normal and 31 abnormal samples). We extract the informative genes as those having the highest entropy. Those genes are considered as candidate biomarkers. The genes that have the highest mutual information with the well-known tumor biomarker Alpha fetoprotein gene are considered as candidate biomarkers. The results are validated by examining the presence of the experimentally known biomarkers among the extracted ones to get a confidence level.
Nitrogen use efficiency: re-consideration of the bioengineering approach

There is considerable confusion about N use efficiency (NUE) in the plant literature. We would like to propose the simple and ubiquitous definitions described by Good et al. (2004) as a starting point for studies of NUE. Based on this terminology, there is evidence from breeding programs for variation in both uptake efficiency (UpE) and utilization effi- ciency (UtE). Molecular physiology studies typically address mechanisms for improving NUE, but often do not calculate NUE or even acquire appropriate data for calculating NUE. Herein, we report in detail on recent studies involving molecu- lar approaches for improving NUE, and calculate changes in NUE where possible. The evidence suggests that there is po- tential for improving usage index and UpE in dicots and UpE and UtE in monocots by overexpressing enzymes for N assimilation, specifically glutamine synthetase 1, glutamate synthase, and alanine aminotransferase. If decreased fertilizer- N input and improved NUE are truly goals of the plant biology community, researchers are encouraged to (i) consider the use of both wild type and azygous controls, (ii) compare general NUE (on the basis of grain or biomass yield per unit of applied N) of overexpression mutants and controls at both limiting and non-limiting N levels, (iii) select an appropriate type of specific NUE for assessing the physiological mechanisms involved (uptake versus internal utilization), and (iv) confirm promising results under field conditions.
Two strains of Pseudomonas fluorescens bacteria differentially affect survivorship of waxworm (Galleria mellonella) larvae exposed to an arthropod fungal pathogen, Beauveria bassiana

Abstract Two strains of Pseudomonas fluorescens were found contaminating a biopesticide used in a previous study against Varroa destructor infestations in honey bee hives. In that study, the biopesticide, a formulation of a fungal pathogen of arthropods, Beauveria bassiana, failed to have any negative impact on the mite infestation despite successful results in previous studies using uncontaminated batches of the same biopesticide. The objective of the present research was to determine whether the bacteria may have interfered with the infectivity and/or virulence of B. bassiana in a simplified system; positive results in that system would then provide a rationale for further work under more complex conditions. Galleria mellonella late instar larvae treated topically with both a bacterial suspension of 6.8 to 7.0×107 cfu/ml and a fungal suspension of 2.5×107 or 2.5×108 B. bassiana conidia/ml showed, in the case of one of the bacterial strains, significantly increased survivorship compared to larvae treated with just the B. bassiana suspension. When larvae were immersed in a bacterial suspension prior to application of B. bassiana suspension using a spray tower, a significant positive effect of the same P. fluorescens strain on larval survivorship was observed at 2.5×108 conidia/ml. Neither the bacterial suspensions alone nor blank control solutions had any effect on larval survivorship. These results show that an interaction between the bacteria and the pathogen may explain some of the results from the prior field trial.
Assessment of Machine Learning Classifiers for Malware Detection

In our daily life, cell phones (e.g., cell phones and tablets) have met an expanding business achievement and have turned into an essential component of the regular daily existence for billions of individuals all around the globe. Day by day the advancements in technology is growing like an infinity thing . And the advancements in technology made everyone to use the smart phones and tablets regardless their professions . Everyday a big range of apps coming in to existence which made our lives very comfortable. While installing these apps without knowing we are allowing some malware in to our mobile which may leads to leakage of once private information. So in this paper we are going to analyze some machine learning techniques which will help in malware classification by taking the dataset. In this paper we calculated accuracy rate of malware classifiers such as KNN, Random Forest, SVM, and Gaussian Etc. Where we will be rating all these machine learning techniques according to their rate of accuracy. According to the experiments what we conducted Random forest stood as the best malware classifiers among all the other classifiers. We accept our study will be a reference work for specialists and experts in this examination field.
Morphological and genetic variation in Aethalops alecto (Chiroptera, Pteropodidae) from Java, Bali and Lombok Is, Indonesia

Des analyses statistiques univariees et multivariees du crâne et de la morphologie externe d'Aethalops alecto, originaire des hautes montagnes de Java (10 specimens), Bali (7) et Lombok (20) revele peu ou pas de dimorphisme sexuel. Cependant, des mesures de la majorite des 35 crânes et de 19 caracteres externes montrent des differences significatives entre ces populations. Les analyses des caracteres du crâne et, a un moindre degre, des caracteres externes, montrent que la population de Java est separee dans un espace de fonction discriminante de celles de Bali et de Lombok (qui sont proches l'une de l'autre). Ces populations d'Aethalops alecto sont distinctes des Aethalops aequalis de Sabah, a Borneo
Perceptual Digital Gaps in Generations between Adoption and Usage of Different Modes of Payment in Digital Economy

The Mobile payment revolution has led to 55% increase in digital payments in India in the past one year. Currently there are 10 million locations that accept digital payments in the country. Though, internet connectivity, digital literacy and perceptual gaps in generations have proved to be the major hurdles for service providers to capture the full market potential for digital payments. This study holistically approaches the topic by critically analyzing the data collected from different strata based on age groups. It was observed that all the generations are readily adopting digital payments whereas there is still a perceptual gap that exist in the regular usage of mobile payments among the elderly and the young generations. Data analysis also reveals that mobile payments are mostly used for the low value purchase by the customers in comparison to the high value purchase as there is high risk and high channel cost associated with mobile transactions and also government regulations do not support very high value transactions. Another trend observed was Impulsive purchase behavior in customers (especially millennial) using mobile payments as these purchases are unplanned, unintentional and unthoughtful. These facts can be used as an input by the Government departments and the financial service providers to design strategies to bring convenience in the adoption and usage of digital transactions, to bridge the gap in the adoption of digital payments between young and old generations.
Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.