Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination the accuracy of TELPOD SVP 45 resistive sensors as tools for measuring the relative displacement of points

These devices are slide potentiometers encased in a metal package with a carbon resistive element. The manufacturer has two types of potentiometers on o er. The rst type has a linear resistance characteristics, and the second – a logarithmic one. The length of the slider path is 45 mm. The primary use of potentiometers of the SVP 45 group is their application as a regulatory element in electronic audio-visual equipment and household appliances. Other applications include various measurement systems. The main advantage of such devices is their low price – the cost of one potentiometer of the measurement devices group is an expense of circa 10 PLN. Individual sensors can be combined in the form of surveying rose es, surveying micro-lines, parallel sensor units, etc. Reference points, depending on the needs, are stabilized in the form of concrete blocks with metal rods in the ground, plugs in the walls, etc. The performance principle of a single element of the system is based on measuring changes in the distance between two points. A rod is permanently a ached to one point, and resistive sensor to the second one. The resistive sensor is connected to the second end of the metal rod with a sti leg. With the change in the distance between the test points, the rod causes a change in the slider position in the resistive sensor. The sensor is connected to the check chart with signal wires. A single check chart allows us to connect, depending on the model, from six to eight sensors. Further data transfer is carried via a USB cable to a PC. The record of changes in the section length is executed by a PC using an appropriate computer program. The data record is kept as a text le on the computer disc.
Developing intrapreneurship as a career perspective for senior professionals : towards an innovative HRM and career management approach

How can intrapreneurship, being a stimulant for innovation, serve as a perspective for employees in later career stages? Career development in later stages is hardly studied in combination with intrapreneurship and vice versa. Intrapreneurship, also referred to in this article as internal entrepreneurship, is described in entrepreneurship studies as a role or approached as a set of activities, as a process, with behavioural and attitudinal aspects. In HRM or career management theory, however, the phenomenon is hardly mentioned. A reason for that may be that it is quite an intangible phenomenon, not recognised as a profession, vocation, function or job, the common terms in these specific domains. Internal entrepreneurship can be a clear option for HR development or personal career development when it is termed as a role or specific set of competences. Active career development or career management is often considered and propagated as a rational and well planned process based on conscious decisions. But career choices and decisions are in fact grounded on affective reasons and unconscious layers of the mind. Planning a career, specially on the long term, seems to make not to much sense in our rapid changing world. In this study it is assumed that under specific conditions an intrapreneurial perspective can be constructed in later career stages. A passionate entrepreneurial drive is considered as the key-factor on individual level to create this perspective. Answers to a set of research questions are found based on a mixed and multi method approach. Qualitative interviews and simulations applied in field experiments are the main methods. The objectives of the study are the design of a theoretical model and an innovative tool for HRM practice for the development of intrapreneurship in later stages. This paper presents the outline of a study in progress and the results of a pilot study. The latter provides a 12 element description of the intrapreneurship phenomenon, both on individual and organizational level; it contains opinions and experiences of experts out of practice. This description in combination with a literature review makes clear that an innovative career management approach is needed for the development of internal entrepreneurship in later stages. An approach characterised by a pro age attitude, dialogue between employer and employee, that makes it possible to reveal unaware and unconscious aspects of intrapreneurial talents and motivation.
Measurement of under-helmet force distribution on FMVSS 218 headform

An experimental method for measuring the under-helmet force distribution on the headform in a helmeted drop test has been developed. This work supports the research in developing a skull fracture criterion for motorcycle helmets. The current Federal Motor Vehicle Safety Standards (FMVSS) 218 for motorcycle helmets is a generalized safety standard based on acceleration peaks and dwell times. FMVSS 218 uses a rigid headform mounted with a unidirectional accelerometer for conducting helmeted drop tests at a specified height. However, the biomechanical basis of FMVSS 218 is not known. Recent work has validated a linear skull fracture criterion (SFC) based on skull strain correlation with fracture data, with the skull strain computed using an anthropomorphic finite element model simulating the head impact process (Vander Vorst, et al. 2003, 2004 AAAM). A direct application of the strain-based SFC to motorcycle helmets will require the simulation of the helmeted head impact, which will be impractical since it requires the modeling of the helmet. However, if the under-helmet load distribution on the headform can be measured during the FMVSS 218 test and used as inputs to the finite element model, skull strain can be computed. This approach preserves the FMVSS 218 test procedure without the need to model the helmet. A method has been developed to measure the force distribution on the headform using an array of Tekscan’s FlexiForce pressure sensors attached to the FMVSS 218 headform. FlexiForce sensors have high accuracy for measuring the normal load; however, they are subjected to shear-induced errors. A technique has been developed to minimize the shear error seen by FlexiForce sensors, giving an accurate force-history profile over the entire headform. Data validating the FlexiForce sensor performance on the headform are presented. An algorithm for mapping the FlexiForce data as pressure contours on the headform for finite element model simulations was developed and preliminary finite element model simulations using helmeted headform drop test data as inputs were conducted.
Event-driven Architecture for Sensor Data Integration for Logistics Services

Sensor data offers a massive potential for the logistics sector. To achieve an optimal, effective and productive supply chain, operators and manufacturers are challenged to use this information and extract value from it. They have to comply with the main task of efficiently managing logistics processes as well as fulfilling requirements and guidelines. To do so, it is necessary to monitor all processes and understand exceptions and anomalies. Sensor and Internet of Things (IoT) data is the key for these tasks. Currently, the data is available but not (sufficiently) used. The heterogeneity of sensor data is a major obstacle for the usage. Therefore, we present an architecture, which addresses these challenges by integrating heterogenic data in well-formed data sets.
Fuzzy Soft-Set Based Approach for Femto-Caching in Wireless Networks

5G networks provide an interesting research challenge with the widespread use of Internet and the scale of mobile data traffic that grows explosively. The emerging need to bring data closer to users and minimizing the traffic off the macrocell base station (MBS) introduces the use of small, low power small base stations (SBS) with small cache space (termed femto caches, or more popularly, helpers). Helpers have low rate backhaul but high storage capacity that can be used to cache most popular files. When users request files that do not exist in helpers, then the files will be transmitted from MBS to helpers. The availability of the data in local cache can significantly improve performance since it overcomes the constraints in wireless environment. This leads to improving network throughput and reducing end-toend and backhaul delay. Caching the optimal contents into femto caches proactively depending on the knowledge about files popularity distribution is not enoughthe since Internet users have different context and different preferences. In this paper, we propose an algorithm for proactive caching based on fuzzy softset (FSS) approach for decision making. The algorithm decides which files to cache and where to cache them depending on file popularity distribution, file to user preferences, file clustering, and helpers to connected users clustering. The simulation results show significant overall cache hit rate increase with the increasing number of user requests. The algorithm can effectively improve system performance by reducing the delay for downloading the files and proactively cache those files which will improve user satisfaction.
The role of magnetic field geometry in the evolution of neutron star merger accretion discs

Neutron star mergers are unique laboratories of accretion, ejection, and r-process nucleosynthesis. We used 3D general relativistic magnetohydrodynamic simulations to study the role of the post-merger magnetic geometry in the evolution of merger remnant discs around stationary Kerr black holes. Our simulations fully capture mass accretion, ejection, and jet production, owing to their exceptionally long duration exceeding 4 s. Poloidal post-merger magnetic field configurations produce jets with energies Ejet ∼ (4–30) × 1050 erg, isotropic equivalent energies Eiso ∼ (4–20) × 1052 erg, opening angles θjet ∼ 6–13°, and durations tj ≲ 1 s. Accompanying the production of jets is the ejection of $f_\mathrm{ej}\sim 30\! -\! 40{{\ \rm per\ cent}}$ of the post-merger disc mass, continuing out to times >1 s. We discover that a more natural, purely toroidal post-merger magnetic field geometry generates large-scale poloidal magnetic flux of alternating polarity and striped jets. The first stripe, of $E_\mathrm{jet}\simeq 2\times 10^{48}\, \mathrm{erg}$, Eiso ∼ 1051 erg, θjet ∼ 3.5–5°, and tj ∼ 0.1 s, is followed by ≳4 s of striped jet activity with $f_\mathrm{ej}\simeq 27{{\ \rm per\ cent}}$. The dissipation of such stripes could power the short-duration gamma-ray burst (sGRB) prompt emission. Our simulated jet energies and durations span the range of sGRBs. We find that although the blue kilonova component is initially hidden from view by the red component, it expands faster, outruns the red component, and becomes visible to off-axis observers. In comparison to GW 170817/GRB 170817A, our simulations underpredict the mass of the blue relative to red component by a factor of few. Including the dynamical ejecta and neutrino absorption may reduce this tension.
Supervised classification with temporal data

In supervised classification there is a categorical dependent variable y and a set of independent variables (the predictors). which may be represented by a vector X. Given training data in the form of N observations $(X\sb1,y\sb1)\. (X\sb{N},y\sb{N}),$ the objective is to build a model that accurately predicts the values of y for unseen values of X. This dissertation examines a special case of the supervised classification problem. In the problem considered, predictor measurements have a natural ordering; in particular, an ordering due to a dependence with a special variable such as time. An observation consists of a finite series of measurements rather than an unordered set. The dissertation's thesis is that the information encapsulated in the ordering of predictors can be used to improve both the accuracy and understandability of classification models. Traditional induction algorithms are not designed to take advantage of this information. This dissertation explores ways that enable classifiers to do so. The ordering of predictors enables the characterization of observations over intervals of time, in terms of trends, effectively reducing the dimensionality of the input space. Questions relating to the representation, extraction, and evaluation of such features are addressed. A method is proposed to compress, segment, and smooth 2D scatterplots, using minimum description length estimation. The concept of line episode is introduced to capture local trend characteristics. Line episodes can be extracted and used effectively by any classifier using trend-episode analysis, an algorithm presented in the thesis. Trends can be described in various scales, with varying levels of detail. The role of scale in the induction of classification models is explored, and a local scaling algorithm for line episodes is developed. Trend-episode analysis is extended to enable any classifier to identify appropriate features at appropriate scales and induce classification models for instances with multiscale representations. The proposed framework is evaluated on synthetic data, under a wide range of carefully controlled experimental conditions, and on a NASA telemetry monitoring application. The results show that multiscale trend-episode analysis can increase both the separability of classes (leading to more accurate models) and the understandability of models.
Adaptive autonomous navigation of mobile robots in unknown environments

Autonomous navigation of a mobile robot is a challenging task. Much work has been done in indoor navigation in the last decade. Fewer results have been obtained in outdoor robotics. Since the early 90's, the Global Positioning System (GPS) has been the main navigation system for ships and aircrafts. In open fields, satellite navigation gives absolute position accuracy. The absolute heading information is also obtained by satellite navigation when the mobile robot is in motion. However, the use of GPS satellite navigation is mainly restricted to open areas where at least three satellites can be seen. For example, mobile robots working in underground or deep open mines cannot use satellite navigation at all, and in forest or city areas, there are serious limitations to its use. Laser range finder technology has evolved remarkably over the last decade, and offers a fast and accurate method for environment modeling. Furthermore, it can be used to define robot position and heading relative to the environment. It is obvious that the use of several alternative sensors according to the environment will make the navigation system more flexible. Laser range finder technology is particularly suitable for indoors or feature rich outdoor environments. The goal of this thesis is to develop a multi sensor navigation system for unknown outdoor environments, and to verify the system with a service robot. Navigation should be possible in unstructured outdoors as well as indoor environments. The system should use all available sensor information and emphasize those that best suit the particular environment. The sensors considered in this thesis include a scanning laser range finder, a GPS receiver, and a heading gyro. The main contribution of the thesis is a flexible navigation system developed and tested with a service robot performing versatile tasks in an outdoor environment. The used range matching method is novel and has not been verified earlier in outdoor environments. No unique solution can be guaranteed in the developed map matching algorithm, although it seems to work well in the practical tests. Position and heading errors grow without bound in successive map matchings, which could be referred to as laser odometry. Therefore, the position and heading have been corrected by means of global matching when the robot returns to a place it has previously visited. Alternatively, structured landmarks have been used for position and heading correction. In field tests, tree trunks and walls have been used as structured …
Samba: Identifying Inappropriate Videos for Young Children on YouTube

YouTube videos are one of the most effective platforms for disseminating creative material and ideas, and they appeal to a diverse audience. Along with adults and older children, young children are avid consumers of YouTube materials. Children often lack means to evaluate if a given content is appropriate for their age, and parents have very limited options to enforce content restrictions on YouTube. Young children can thus become exposed to inappropriate content, such as violent, scary or disturbing videos on YouTube. Previous studies demonstrated that YouTube videos can be classified into appropriate or inappropriate for young viewers using video metadata, such as video thumbnails, title, comments, etc. Metadata-based approaches achieve high accuracy, but still have significant misclassifications, due to the reliability of input features. In this paper, we propose a fusion model, called Samba, which uses both metadata and video subtitles for content classification. Using subtitles in the model helps better infer the true nature of a video improving classification accuracy. On a large-scale, comprehensive dataset of 70K videos, we show that Samba achieves 95% accuracy, outperforming other state-of-the-art classifiers by at least 7%. We also publicly release our dataset.
A SURVEY ON SECURITY PATTERNS AND ISSUES IN CLOUD COMPUTING ENVIRONMENT

Cloud Computing becomes an attractive proposition because of long-term potential advantage by reducing the cost of services and thereby gaining more business outcomes. It is capable of transferring facilities by using the internet in a secured manner economically. It has got interested from both industry and academia. The shared utilization characteristics enhance the proficiencies of the hardware resources of cloud computing. By virtue of this feature, a cloud is being used by commercial and specific users to make available their data in form of either application or service. Security concern originates because of the sharing environment in which external administration for the migration of user's assets takes place. A comprehensive review in order to find major gaps and issues related to security comes under cloud computing has been tried to present. The issues belong to security and its possible countermeasure has been found out and present. The increased number of cloud services becomes the root cause of infrastructural complexity behind the services. For operating and managing this complex infrastructure effective monitoring is essential. After conducting the study I found the survey lacks the comprehensive analysis for the monitoring of the cloud. So this paper also includes the detailed discussion of monitoring systems for the cloud. Current platforms and services related to Cloud monitoring have been discussed which is needed for making informed decisions. This survey also includes the discussions belong to research platform required for cloud infrastructure so that the researchers can identify the appropriate way for simulation or prototype implementation for further evaluation. Keywords— Cloud computing, Multi-tenancy, Virtualization, Cloud resource monitoring, simulation.
What is the role of adjuvant therapy after liver transplantation for hepatocellular carcinoma?

Liver transplantation (LT) is a potentially curative treatment for hepatocellular carcinoma (HCC) because it removes both the tumor and the underlying cirrhotic liver (which can give rise to new tumors). However, the early experience with LT for HCC in the 1980s was disappointing because of the relatively high recurrence rates (65% of all patients in 1 series1) and the discouraging overall survival results (5-year survival = 10%–35%). 1–4 In particular, the prognosis of patients with recurrent HCC after LT was dismal: in historical series, the median survival time after recurrence was 6 months, and there were virtually no survivors beyond 3 years (Fig. 1). Figure 1 Natural history of HCC recurrence after LT (94 recurrences in 497 French patients transplanted between 1988 and 1998). Because of these early results with LT and worldwide organ shortages, orthotopic LT is generally restricted to HCC patients with an expected 5-year post-LT survival rate > 50%. In addition, most LT programs require HCC patients to have an expected 5-year post-LT survival rate similar to that of patients with benign liver diseases (ie, 70%). 5 To achieve these post-LT survival goals, the Milan criteria6 were adopted as a liver recipient prioritization tool more than 10 years ago by the United Network for Organ Sharing and by the majority of transplant programs worldwide. In multiple evaluations, the Milan criteria have consistently identified patients who will achieve 5-year post-LT survival rates ranging from 65% to 80% and experience 5-year tumor recurrence rates ranging from 8% to 15%. 7–9 The continued rise in the number of patients diagnosed with HCC means that HCC has emerged as a major LT indication despite the restrictive Milan criteria: in most LT programs, HCC is now the LT indication for 25% to 35% of patients. Because of the number of organs directed to HCC patients, the persistence of post-LT recurrence is a vexing medical and ethical problem of increasing importance. Therefore, efforts to decrease the rates of post-LT tumor recurrence, to prevent recurrence-induced graft losses, and to further improve overall survival have included antitumor adjuvant therapy after LT. However, the data for this approach remain sparse and controversial; this is particularly true for the potential of adjuvant therapy to extend transplant eligibility criteria. In this article, we discuss the following questions about adjuvant therapy after LT for HCC: What are the results of the current experience with adjuvant therapy after LT for HCC? What are the approaches that should be considered to improve the quality of trials for adjuvant therapy after LT for HCC? What are the best compounds to test in the adjuvant setting? What are the target populations for adjuvant therapy? What are the optimal endpoints for testing the efficacy of adjuvant therapy after LT for HCC?