Miracle-dz/UniFAR-FTUs huggingface dataset:
Student Misconceptions in Solving Real Analysis Problem Based on Reasoning Framework

Real Analysis is the most important subject in mathematics. Student must have good reasoning ability in Real Analysis learning. This study aims to describe the misconception of students in solving Real Analysis problem based on reasoning framework. This is a qualitative descriptive research with 24 students of Mathematics Education Department, Universitas Muhammadiyah Surakarta who enrolled Real Analysis as participants. The data was collected using test, interview and documentation. The technique of data analysis conducted using the flow-method that consist of data reduction, data display and verification. The result shows that most student make misconceptions in analysis, synthesis, verification and generalization aspects in solving of Real Analysis problem.
CURRENTLY REQUIRED EDUCATION OF CRISIS MANAGEMENT PERSONNEL

The requirements of changes in the educational systems originate from the social requirement to increase the quality of education in specific fields, including the crisis management field. The common fault of current educational systems is detachment of the taught theory from the real practical requirements. That is the reason why it is necessary to focus more on the new way of education, in which the participants of lifelong learning can be prepared for today ́s new challenges, which emerge from the labor market as well as competencies required by the practice. In crisis management field is the problem how to link the different educational levels more efficiently and which forms and methods to apply in education process. By analysis of the current approaches in the area of crisis management personnel preparation we will identify the most appropriate forms and methods of education, focusing on the specific needs of crisis management, such as case studies, interactive methods, training, and other methods where information technology can be used as a support learning tool. In this way we can support the expertise of crisis management staff and their competencies. The main objective of the article is to propose possible forms and methods of education to make information technologies within crisis management field more effective. Our main suggestions are focused on the use of simulations and learning by experience.
Disability and the Unionized Workplace

The employment of people with disabilities has received significant attention, but little is known about how unions affect their employment experiences. To address this, we analyze monthly U.S. Current Population Survey (CPS) data from 2009 through 2017 and find that the unionization rate declined more rapidly among employees with disabilities. The results are not due to demographic or occupational factors, but to the lower rate at which people with disabilities are hired into unionized jobs. This lower hiring rate more than offsets the greater job retention of unionized workers with disabilities. Given that employers generally control hiring, it appears they are particularly reluctant to hire people with disabilities into jobs with union protections. Overall, in the union context, workers with disabilities appear more likely to be "last hired," but less likely to be "first fired. " We also find that a union wage premium of 29.8% for workers with disabilities is greater than the 23.9% premium for workers without disabilities. There remains a pay gap of -5.7% between union workers with and without disabilities, compared to a -10.1% pay gap between non-union workers with and without disabilities. Exploratory data reveal that both union coverage and disability status increase the likelihood of requesting accommodations, supporting the voice model of unions. Overall the results indicate that while unions appear to help workers with disabilities in the U.S., unionized positions are becoming less available to workers with disabilities.
Mobile edge computing network optimization

Smart mobile devices are becoming more and more important in every aspects of human life, and mobile application are becoming more and more resources demanding with a widening gap between the required resources and those available on mobile devices. To bridge this gap, Mobile Edge Computing paradigm has been introduced to bring IT applications, computational and storage resources to the periphery, or edges, of the cellular mobile network. Several complementary technologies have been presented to implement Mobile Edge Computing, all of them considering the deployment of virtualization facilities within mobile access and backhaul network. In this thesis we face several optimization problems related to the planning of a Mobile Edge Computing Network. In the first part of the thesis we present the Mobile Edge Computing Network Design Problem (MNDP), that considers the design of a full mobile access and backhaul network together with the location of the virtualization facilities. Two variants of the problem are considered: either assuming a static condition of the network or dynamic variations of traffic demands and the human mobility that causes these variations. Matheuristics are proposed to solve MNDP and best practices are drawn on real world data. In the second part of the thesis, we face a tactical side of the optimal Mobile Edge Computing network planning, that is the routing in time of access point traffic to specific Mobile Edge Computing facilities on a fixed network structure. We present exact Branch-and-Price algorithm to solve the problem, experimenting on real-world dataset. Finally, driven by the fact that the knowledge of mobile user mobility represents a key data for the MNDP, in the third part of the thesis we face the problem of estimating human mobility given very aggregated data, that is the network traffic demand variations in time. We propose mathematical programming formulation and column generation algorithm to solve this problem, experimenting on both real-world and synthetic datasets.
Spatial Dmbs Architecture for a Free and Open Source Bim

Abstract. Recent research on the field of Building Information Modelling (BIM) technology, revealed that except of a few, accessible and free BIM viewers there is a lack of Free & Open Source Software (FOSS) BIM software for the complete BIM process. With this in mind and considering BIM as the technological advancement of Computer-Aided Design (CAD) systems, the current work proposes the use of a FOSS CAD software in order to extend its capabilities and transform it gradually into a FOSS BIM platform. Towards this undertaking, a first approach on developing a spatial Database Management System (DBMS) able to store, organize and manage the overall amount of information within a single application, is presented.
Concurrent Error Detection in Finite Field Arithmetic Operations

With significant advances in wired and wireless technologies and also increased shrinking in the size of VLSI circuits, many devices have become very large because they need to contain several large units. This large number of gates and in turn large number of transistors causes the devices to be more prone to faults. These faults specially in sensitive and critical applications may cause serious failures and hence should be avoided. On the other hand, some critical applications such as cryptosystems may also be prone to deliberately injected faults by malicious attackers. Some of these faults can produce erroneous results that can reveal some important secret information of the cryptosystems. Furthermore, yield factor improvement is always an important issue in VLSI design and fabrication processes. Digital systems such as cryptosystems and digital signal processors usually contain finite field operations. Therefore, error detection and correction of such operations have become an important issue recently. In most of the work reported so far, error detection and correction are applied using redundancies in space (hardware), time, and/or information (coding theory). In this work, schemes based on these redundancies are presented to detect errors in important finite field arithmetic operations resulting from hardware faults. Finite fields are used in a number of practical cryptosystems and channel encoders/decoders. The schemes presented here can detect errors in arithmetic operations of finite fields represented in different bases, including polynomial, dual and/or normal basis, and implemented in various architectures, including bit-serial, bit-parallel and/or systolic arrays.
Pinna of ear: a potential biometric identifier

Background: Individual differences in morphometric and somatoscopic observations of pinna of ear were studied to explore the potential of pinna of ear as a biometric tool. Methods: Morphometric and somatoscopic data of right ear of 350 Indian individuals (Age: 17-25 years) was collected. Measurements of pinna length, pinna width, pinna root, pinna projection, intertragic distance, ear lobe length, and ear lobe width were taken. Observations were done for presence or absence of Darwin tubercle, flat or rolled helix, and attached or free ear lobe. Probability statistics was extrapolated to assess the variations in ear pinna characters. Results: All measurements showed a wide range. There was statistically significant difference between male and female pinna measurements. The somatoscopic observations showed 82.9% individuals with presence of Darwin tubercle, 99.1% with rolled helix, and 65.4% with free ear lobes. On the basis of extrapolation of ten different morphometric and somatoscopic parameters, the statistics showed the probability of any two individuals having exactly same observations to be 0.0008%. Conclusions: The wide range of measurements in the present study suggested a high possibility of variations amongst the population. If all the parameters including both morphometric and somatoscopic, are taken into consideration, the pinna of ear of each individual is a very distinctive structure, which makes it a potential biometric identifier and with the use of proper technologies, it will be a widely used biometric tool in the future.
Communications protocol for power management in smart homes

Emergency situations, incentives for energy efficiency, new pricing plans, and distributed electricity market jointly require home automation systems that would conform an instantaneous load to a dynamically set limit. In this paper, a novel machine-to-machine communications protocol that interconnects a smart grid and smart house is described. “Intelligent” domestic appliances use it to coordinate their switch-on times so that the assigned power quota will not be exceeded. In this way, the end-user experience can be improved by reducing the electricity bills, conveniently conforming to prepaid pricing plans, or even by providing continued service at possibly reduced consumption levels during power shortages.
Towards inclusive service delivery through social investment in England : An analysis of five sectors with a particular focus on mental health

This report examines trends in social investment in England following the financial crisis of 2007/8. The first section considers social investment in relation to four policy arenas: housing, financial services, early childhood education and care, and water. The second part of the report provides an overview of social investment and disinvestment trends in the healthcare system in England since the 1990s with a particular focus on mental health services. This section includes a detailed account of service users’ and professionals’ experiences of the impact of liberalisation and austerity measures on mental health service delivery drawing on qualitative data collection. Throughout the report we identify policy recommendations to address the effects and impacts of emergent trends towards social disinvestment and liberalisation of public services. This study is part of the wider pan-European RE-InVEST project to investigate the impact of the EU Social Investment package on marginalised groups since the 2007 crisis.
EDIS - Emotion-Driven Interactive Systems

Ambient interface approaches utilising facial expression recognition technology seem to have great potential not only in their typical application areas like security, marketing or commerce. This paper discusses the use of the face detection and analysis software SHORE™ (Sophisticated High-speed Object Recognition Engine) by the Fraunhofer Institute for Integrated Circuits in Erlangen, Germany in a digital art-oriented context and introduces the concept of EDIS - Emotion-Driven Interactive Systems – in which gesture and emotional expression replace the traditional mouse-keyboard-based interface and go beyond simple entertainment.
Learning Behavior Big Data Tells Us Which Students Can Get a MOOC Course Certificate in Chinese University MOOC

MOOC (Massive open online courses) is developing rapidly, but the pass rate of the course is very low. It is significant to study the characteristics of learners who have obtained certificates. This article analyzes the video and completion behavior of various learners. The results show that the learner who obtained the certificate did not watch enough videos, but often completed a lot of exercises. It means they have acquired knowledge from other sources. MOOC is only a means of consolidating knowledge. The results of this study can help us find the right application model for MOOC. At the same time, MOOC operators are also reminded to improve the MOOC model for more learners to accept.
Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.
Organizational design choices of high‐tech startups: How middle management drives innovation performance

Research Summary Innovative products and services are the inspiration for many startups. However, founders find that the management of existing operations competes with the attention that they can devote to innovation. We investigate whether and how establishing a middle management level frees up attention for innovation when firms are newly started. We argue that middle management is positively related to introducing product innovations and that the effect is stronger when founders have larger stocks of pre‐existing knowledge and when the startup's industry provides more innovation opportunities. These hypotheses are supported by an analysis of 2,431 German high‐tech startups founded between 2005 and 2012. Managerial Summary Most high‐tech entrepreneurs acknowledge that an overload of managerial tasks keeps them from advancing innovation in their startups. However, they are often times reluctant to introduce middle management because of a fear that the resulting bureaucratization will stifle innovation. Our study shows that these fears are not justified. Instead, we find for 2,431 high‐tech startups in Germany that startups with middle managers are significantly more innovative than those without. While middle managers might be a roadblock for innovation in large firms, startups benefit from having them. Founders are the central decision makers in startups and can easily be overburdened with management tasks. Middle managers can alleviate parts of this workload and allow founders to focus on creating innovative products and services.
Fast Retrial and Dynamic Access Control Algorithm for LTE-Advanced Based M2M Network

—Currently, increasing number of devices are connected to networks. Hence, M2M (Machine to Machine) communication, especially LTE/LTE-Advanced based M2M communication, is attracting more and more interests from the telecommunication industry. However, as the current LTE (Long Term evolution) system is designed for Human to Human system, it may be unable to support the massive M2M devices. This paper proposes a fast retrial and dynamic random access algorithm for LTE-Advanced network. It drastically reduces the delay of access comparing the back-off algorithm. In the meantime, it successfully prevents the system from severe congestion, which is inevitable in back-off algorithm when the arrival rate of random access is very high. To make the dynamic control of random access feasible in practical scenario, an estimation algorithm of the access arrival rate is also proposed in this paper. Simulation results reveal that the algorithm is able to provide better delay performance and greater throughput comparing with the back-off schemes deﬁned in the LTE network.
EFFECTS OF BULLWHIP ON SUPPLY CHAIN PERFORMANCE IN MANUFACTURING SECTOR IN KENYA, A CASE OF COOPER K BRANDS LIMITED

Purpose: This study focused to analyzing the effect of bullwhip on supply chain performance in manufacturing sector in Kenya. The specific objectives of the study were: to establish the effect of demand forecasting, production strategies, rationing game, and information sharing on supply chain Performance in the manufacturing sector in Kenya Materials and Methods: The study adopted a descriptive study approach. The total population of the study was all employees working at Coopers Kenya limited. The company had a total of one hundred and eighty (180) employees who are involved in the operations of the company. The target population was individuals which the researcher was interested in describing and making statistical inferences about. Therefore the target population was one hundred and eighty (180) employees working at CKL. Simple Random sampling method was used to select the one hundred and twenty four (124) respondents. This research applied both primary and secondary data. Pilot study was conducted by providing the questionnaires to 10% of the sample size. The collected data was analyzed through descriptive statistics and inferential statistics. Data presentation was through the use of pie charts, bar charts, graphs and frequency tables. Correlation analysis was used to establish the relationship between the independent and dependent variables. Results: The study found that demand forecasting, production strategies, rationing game, and information sharing were factors affecting supply chain Performance in the manufacturing sector in Kenya. Recommendations: The study recommended that organization should adopt modern methods and techniques of forecasting demand which will enhance the accuracy of the forecast demand, the use of production smoothing strategies which will results to consistency in the production levels irrespective of the variation of demands, study therefore recommends the elimination of order rationing strategy as a means of managing shortages in the supply chain and the organizations to fully embrace the use of modern information technology in order to enhance information flow.
Emotion Recognition from Text

: Harvesting knowledge from huge information is a tedious task. Recommender systems play an important role in suggesting to a user. Emotion is a personal feeling of every individual. Various types of emotions include anger, anticipate, disgust, fear, joy, sadness, surprise and trust. This paper details about the machine learning based emotion detection. Various classifiers like Naive Bayes, Support vector machines, Neural networks, and their combinations were used to detect the human emotions. The attained accuracy for each of the method against each of the emotions are provided. In addition to finding emotions, a recommendation approach is proposed based on the emotions from the text.
Case Study: Online Continuing Education for New Librarians

ABSTRACT Continuing education is vital for new librarians to gain skills and knowledge beyond library school. Professional development offered free in an online environment is often the best option for staying current on the issues and trends necessary for young librarians to grow and flourish. This paper presents a case study of an online professional development series for new librarians implemented by the Online Programs Committee of the American Library Association's New Members Round Table.
Enhancement in Thermal Stability of Polyoxymethylene (POM)-Based Biopolymer Blend Materials

In this study, we demonstrate the enhancement in thermal stability of polyoxymethylene (POM)-based biopolymer blend materials. Polyoxymethylene (POM)/Polylactic acid (PLA) blends have been used as alternative biopolymer materials for supporting environmental problems with thermal stability feature. The effects on POM biopolymer blend materials with a controlled PLA amount in their compositions under various injection conditions, were investigated by thermogravimetric analysis, dynamic mechanical analysis, and differential scanning calorimetry. The POM/PLA biopolymer blend materials showed POM phase acted as a homogeneous nucleating site for increasing PLA crystallization, which improves storage modulus of the blend materials. The decomposition temperatures of POM/PLA biopolymer blends tend to shift around 30˚C - 50˚C lower compared to the decomposition temperatures of PLA. Thus, the decomposition temperature behavior of POM/PLA blends seems to be closer to POM. The suitable ratio in POM70/PLA30 resulted in the high strength of the blend materials. The POM/PLA biopolymer blend material was tested at various injection speeds and good miscibility was obtained at an injection speed of 100 mm/s. This enhancement of thermal stability in POM/PLA blend materials should be useful in the development of high-performance bio-based thermoplastic materials.