Abstract: Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.
Abstract: Over the past decade, there has been a steep rise in
the data-driven analysis in major areas of medicine, such as clinical
decision support system, survival analysis, patient similarity analysis,
image analytics etc. Most of the data in the field are well-structured
and available in numerical or categorical formats which can be used
for experiments directly. But on the opposite end of the spectrum,
there exists a wide expanse of data that is intractable for direct
analysis owing to its unstructured nature which can be found in the
form of discharge summaries, clinical notes, procedural notes which
are in human written narrative format and neither have any relational
model nor any standard grammatical structure. An important step
in the utilization of these texts for such studies is to transform
and process the data to retrieve structured information from the
haystack of irrelevant data using information retrieval and data mining
techniques. To address this problem, the authors present Q-Map in
this paper, which is a simple yet robust system that can sift through
massive datasets with unregulated formats to retrieve structured
information aggressively and efficiently. It is backed by an effective
mining technique which is based on a string matching algorithm
that is indexed on curated knowledge sources, that is both fast
and configurable. The authors also briefly examine its comparative
performance with MetaMap, one of the most reputed tools for medical
concepts retrieval and present the advantages the former displays over
the latter.
Abstract: Underground structures are of those structures that have uncertainty in design procedures. That is due to the complexity of soil condition around. Under passing tunnels are also such affected structures. Despite geotechnical site investigations, lots of uncertainties exist in soil properties due to unknown events. As results, it possibly causes conflicting settlements in numerical analysis with recorded values in the project. This paper aims to report a case study on a specific under passing tunnel constructed by New Austrian Tunnelling Method in Iran. The intended tunnel has an overburden of about 11.3m, the height of 12.2m and, the width of 14.4m with 2.5 traffic lane. The numerical modeling was developed by a 2D finite element program (PLAXIS Version 8). Comparing displacement histories at the ground surface during the entire installation of initial lining, the estimated surface settlement was about four times the field recorded one, which indicates that some local unknown events affect that value. Also, the displacement ratios were in a big difference between the numerical and field data. Consequently, running several numerical back analyses using laboratory and field tests data, the geotechnical parameters were accurately revised to match with the obtained monitoring data. Finally, it was found that usually the values of soil parameters are conservatively low-estimated up to 40 percent by typical engineering judgment. Additionally, it could be attributed to inappropriate constitutive models applied for the specific soil condition.
Abstract: A critical problem in wireless sensor networks is limited battery and memory of nodes. Therefore, each node in the network could maintain only a subset of its neighbors to communicate with. This will increase the battery usage in the network because each packet should take more hops to reach its destination. In order to tackle these problems, spanner graphs are defined. Since each node has a small degree in a spanner graph and the distance in the graph is not much greater than its actual geographical distance, spanner graphs are suitable candidates to be used for the topology of a wireless sensor network. In this paper, we study Yao graphs and their behavior for a randomly selected set of points. We generate several random point sets and compare the properties of their Yao graphs with the complete graph. Based on our data sets, we obtain several charts demonstrating how Yao graphs behave for a set of randomly chosen point set. As the results show, the stretch factor of a Yao graph follows a normal distribution. Furthermore, the stretch factor is in average far less than the worst case stretch factor proved for Yao graphs in previous results. Furthermore, we use Yao graph for a realistic point set and study its stretch factor in real world.
Abstract: In this paper, by constructing a special set and utilizing
fixed point theory, we study the existence and multiplicity of the
positive solutions for systems of nonlinear third-order differential
equations with p-laplacian, which improve and generalize the result
of related paper.
Abstract: The water-based bioconvection of a nanofluid
containing motile gyrotactic micro-organisms over nonlinear
inclined stretching sheet has been investigated. The governing
nonlinear boundary layer equations of the model are reduced to a
system of ordinary differential equations via Oberbeck-Boussinesq
approximation and similarity transformations. Further, the modified
set of equations with associated boundary conditions are solved using
Finite Element Method. The impact of various pertinent parameters
on the velocity, temperature, nanoparticles concentration, density of
motile micro-organisms profiles are obtained and analyzed in details.
The results show that with the increase in angle of inclination δ,
velocity decreases while temperature, nanoparticles concentration,
a density of motile micro-organisms increases. Additionally, the
skin friction coefficient, Nusselt number, Sherwood number, density
number are computed for various thermophysical parameters. It
is noticed that increasing Brownian motion and thermophoresis
parameter leads to an increase in temperature of fluid which results
in a reduction in Nusselt number. On the contrary, Sherwood number
rises with an increase in Brownian motion and thermophoresis
parameter. The findings have been validated by comparing the
results of special cases with existing studies.
Abstract: The article explores one of the important relations between numbers-the Pythagorean triples (triplets) which finds its application in distance measurement, construction of roads, towers, buildings and wherever Pythagoras theorem finds its application. The Pythagorean triples are numbers, that satisfy the condition “In a given set of three natural numbers, the sum of squares of two natural numbers is equal to the square of the other natural number”. There are numerous methods and equations to obtain the triplets, which have their own merits and demerits. Here, quadratic approach for generating triples uses the hypotenuse leg difference method. The advantage is that variables are few and finally only three independent variables are present.
Abstract: Poor air quality is one of the main environmental causes of premature deaths worldwide, and mainly in cities, where the majority of the population lives. It is a consequence of successive land cover (LC) and use changes, as a result of the intensification of human activities. Knowing these landscape modifications in a comprehensive spatiotemporal dimension is, therefore, essential for understanding variations in air pollutant concentrations. In this sense, the use of air quality models is very useful to simulate the physical and chemical processes that affect the dispersion and reaction of chemical species into the atmosphere. However, the modelling performance should always be evaluated since the resolution of the input datasets largely dictates the reliability of the air quality outcomes. Among these data, the updated LC is an important parameter to be considered in atmospheric models, since it takes into account the Earth’s surface changes due to natural and anthropic actions, and regulates the exchanges of fluxes (emissions, heat, moisture, etc.) between the soil and the air. This work aims to evaluate the performance of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), when different LC classifications are used as an input. The influence of two LC classifications was tested: i) the 24-classes USGS (United States Geological Survey) LC database included by default in the model, and the ii) CLC (Corine Land Cover) and specific high-resolution LC data for Portugal, reclassified according to the new USGS nomenclature (33-classes). Two distinct WRF-Chem simulations were carried out to assess the influence of the LC on air quality over Europe and Portugal, as a case study, for the year 2015, using the nesting technique over three simulation domains (25 km2, 5 km2 and 1 km2 horizontal resolution). Based on the 33-classes LC approach, particular emphasis was attributed to Portugal, given the detail and higher LC spatial resolution (100 m x 100 m) than the CLC data (5000 m x 5000 m). As regards to the air quality, only the LC impacts on tropospheric ozone concentrations were evaluated, because ozone pollution episodes typically occur in Portugal, in particular during the spring/summer, and there are few research works relating to this pollutant with LC changes. The WRF-Chem results were validated by season and station typology using background measurements from the Portuguese air quality monitoring network. As expected, a better model performance was achieved in rural stations: moderate correlation (0.4 – 0.7), BIAS (10 – 21µg.m-3) and RMSE (20 – 30 µg.m-3), and where higher average ozone concentrations were estimated. Comparing both simulations, small differences grounded on the Leaf Area Index and air temperature values were found, although the high-resolution LC approach shows a slight enhancement in the model evaluation. This highlights the role of the LC on the exchange of atmospheric fluxes, and stresses the need to consider a high-resolution LC characterization combined with other detailed model inputs, such as the emission inventory, to improve air quality assessment.
Abstract: The effect of linear stability analysis of triple diffusive convection in a vertically oscillating viscoelastic liquid of Oldroyd-B type is studied. The correction Rayleigh number is obtained by using perturbation method which gives prospect to control the convection. The eigenvalue is obtained by using perturbation method by adopting Venezian approach. From the study, it is observed that gravity modulation advances the onset of triple diffusive convection.
Abstract: The problem of Entity relation discovery in structured
data, a well covered topic in literature, consists in searching within
unstructured sources (typically, text) in order to find connections
among entities. These can be a whole dictionary, or a specific
collection of named items. In many cases machine learning and/or
text mining techniques are used for this goal. These approaches
might be unfeasible in computationally challenging problems, such
as processing massive data streams. A faster approach consists in collecting the cooccurrences of any
two words (entities) in order to create a graph of relations - a
cooccurrence graph. Indeed each cooccurrence highlights some grade
of semantic correlation between the words because it is more common
to have related words close each other than having them in the
opposite sides of the text. Some authors have used sliding windows for such problem: they
count all the occurrences within a sliding windows running over the
whole text. In this paper we generalise such technique, coming up
to a Weighted-Distance Sliding Window, where each occurrence of
two named items within the window is accounted with a weight
depending on the distance between items: a closer distance implies
a stronger evidence of a relationship. We develop an experiment in
order to support this intuition, by applying this technique to a data
set consisting in the text of the Bible, split into verses.
Abstract: This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.
Abstract: One possible approach for maintaining the security of communication systems relies on Physical Layer Security mechanisms. However, in wireless time division duplex systems, where uplink and downlink channels are reciprocal, the channel estimate procedure is exposed to attacks known as pilot contamination, with the aim of having an enhanced data signal sent to the malicious user. The Shifted 2-N-PSK method involves two random legitimate pilots in the training phase, each of which belongs to a constellation, shifted from the original N-PSK symbols by certain degrees. In this paper, legitimate pilots’ offset values and their influence on the detection capabilities of the Shifted 2-N-PSK method are investigated. As the implementation of the technique depends on the relation between the shift angles rather than their specific values, the optimal interconnection between the two legitimate constellations is investigated. The results show that no regularity exists in the relation between the pilot contamination attacks (PCA) detection probability and the choice of offset values. Therefore, an adversary who aims to obtain the exact offset values can only employ a brute-force attack but the large number of possible combinations for the shifted constellations makes such a type of attack difficult to successfully mount. For this reason, the number of optimal shift value pairs is also studied for both 100% and 98% probabilities of detecting pilot contamination attacks. Although the Shifted 2-N-PSK method has been broadly studied in different signal-to-noise ratio scenarios, in multi-cell systems the interference from the signals in other cells should be also taken into account. Therefore, the inter-cell interference impact on the performance of the method is investigated by means of a large number of simulations. The results show that the detection probability of the Shifted 2-N-PSK decreases inversely to the signal-to-interference-plus-noise ratio.
Abstract: The massive development of online social networks
allows users to post and share their opinions on various topics.
With this huge volume of opinion, it is interesting to extract and
interpret these information for different domains, e.g., product and
service benchmarking, politic, system of recommendation. This is
why opinion detection is one of the most important research tasks.
It consists on differentiating between opinion data and factual data.
The difficulty of this task is to determine an approach which returns
opinionated document. Generally, there are two approaches used
for opinion detection i.e. Lexical based approaches and Machine
Learning based approaches. In Lexical based approaches, a dictionary
of sentimental words is used, words are associated with weights. The
opinion score of document is derived by the occurrence of words from
this dictionary. In Machine learning approaches, usually a classifier
is trained using a set of annotated document containing sentiment,
and features such as n-grams of words, part-of-speech tags, and
logical forms. Majority of these works are based on documents text
to determine opinion score but dont take into account if these texts
are really correct. Thus, it is interesting to exploit other information
to improve opinion detection. In our work, we will develop a new
way to consider the opinion score. We introduce the notion of
trust score. We determine opinionated documents but also if these
opinions are really trustable information in relation with topics. For
that we use lexical SentiWordNet to calculate opinion and trust
scores, we compute different features about users like (numbers of
their comments, numbers of their useful comments, Average useful
review). After that, we combine opinion score and trust score to
obtain a final score. We applied our method to detect trust opinions in
TRIPADVISOR collection. Our experimental results report that the
combination between opinion score and trust score improves opinion
detection.
Abstract: Indonesia recognizes the right to privacy as a human right. Indonesia provides legal protection against data management activities because the protection of personal data is a part of human rights. This paper aims to describe the arrangement of data management and data management in Indonesia. This paper is a descriptive research with qualitative approach and collecting data from literature study. Results of this paper are comprehensive arrangement of data that have been set up as a technical requirement of data protection by encryption methods. Arrangements on encryption and protection of personal data are mutually reinforcing arrangements in the protection of personal data. Indonesia has two important and immediately enacted laws that provide protection for the privacy of information that is part of human rights.
Abstract: In addition to kidney transplant, renal replacement therapy involves hemodialysis and peritoneal dialysis (PD). PD possesses advantages such as maintaining stable physiological blood status and blood pressure, alleviating anemia, and improving mobility, which make it an ideal method for at-home dialysis treatment. However, potential danger still exists despite the numerous advantages of PD, particularly when patients require dialysis exchange four to five times a day, during which improper operation can easily lead to peritonitis. The process of draining and filling is called an exchange and takes about 30 to 40 minutes. Connecting the transfer set requires sterile technique. Transfer set may require a new cap each time that it disconnects from the bag after an exchange. There are many chances to get infection due to unsafe behavior (ex: hand tremor, poor eyesight and weakness, cap fall-down). The proposed semi-automatic connection mechanism used in the PD can greatly reduce infection chances. This light-weight connection device is portable. The device also does not require using throughout the entire process. It is capable of significantly improving quality of life. Therefore, it is very promising to adopt in home care application.
Abstract: This work aims to analyze the locative structure used by the locative games of the company Niantic. To fulfill this objective, a literature review on the representation and simulation of cities was developed; interviews with Ingress players and playing Ingress. Relating these data, it was possible to deepen the relationship between the virtual and the real to create the simulation of cities and their cultural objects in locative games. Cities representation associates geo-location provided by the Global Positioning System (GPS), with augmented reality and digital image, and provides a new paradigm in the city interaction with its parts and real and virtual world elements, homeomorphic to real world. Bibliographic review of papers related to the representation and simulation study and their application in locative games was carried out and is presented in the present paper. The cities representation and simulation concepts in locative games, and how this setting enables the flow and immersion in urban space, are analyzed. Some examples of games are discussed for this new setting development, which is a mix of real and virtual world. Finally, it was proposed a Locative Structure for electronic games using the concepts of heterotrophic representations and isotropic representations conjoined with immediacy and hypermediacy.
Abstract: In railway industry, train sets are designed based on contractual requirements (mission profile), where reliability targets are measured in terms of mean distance between failures (MDBF). However, during the beginning of revenue services, trains do not achieve the designed mission profile distance (mileage) within the timeframe due to infrastructure constraints, scarcity of commuters or other operational challenges thereby not respecting the original design inputs. Since trains do not run sufficiently and do not achieve the designed mileage within the specified time, car builder has a risk of not achieving the contractual MDBF target. This paper proposes a constant failure rate based model to deal with the situations where mileage accumulation is not a part of the design mission profile. The model provides appropriate MDBF target to be demonstrated based on actual accumulated mileage. A case study of rolling stock running in the field is undertaken to analyze the failure data and MDBF target demonstration during low mileage accumulation. The results of case study prove that with the proposed method, reliability targets are achieved under low mileage accumulation.
Abstract: The design of shallow foundations to withstand different dynamic loads has given considerable attention in recent years. Dynamic loads may be due to the earthquakes, pile driving, blasting, water waves, and machine vibrations. But, predicting the behavior of shallow foundations during earthquakes remains a difficult task for geotechnical engineers. A database for dynamic and static parameters for different soils in seismic active zones in Iraq is prepared which has been collected from geophysical and geotechnical investigation works. Then, analysis of a typical 3-D soil-raft foundation system under earthquake loading is carried out using the database. And a parametric study has been carried out taking into consideration the influence of some parameters on the dynamic behavior of the raft foundation, such as raft stiffness, damping ratio as well as the influence of the earthquake acceleration-time records. The results of the parametric study show that the settlement caused by the earthquake can be decreased by about 72% with increasing the thickness from 0.5 m to 1.5 m. But, it has been noticed that reduction in the maximum bending moment by about 82% was predicted by decreasing the raft thickness from 1.5 m to 0.5 m in all sites model. Also, it has been observed that the maximum lateral displacement, the maximum vertical settlement and the maximum bending moment for damping ratio 0% is about 14%, 20%, and 18% higher than that for damping ratio 7.5%, respectively for all sites model.
Abstract: Logistics and Supply Chain Management are of crucial importance for organisational success. In the era of Digitalization, several implications and improvement potentials for these domains arise, which at the same time could lead to decreased competitiveness and could endanger long-term company success if ignored or neglected. However, empirical research on the issue of Digitalization and benefits purported to it by practitioners is scarce and mainly focused on single technologies or separate, isolated Supply Chain blocks as e.g. distribution logistics or procurement only. The current paper applies a holistic focus group approach to elaborate practitioner use cases at the nexus of the concepts of Supply Chain Management (SCM) and Digitalization. In the course of three focus group workshops with over 45 participants from more than 20 organisations, a comprehensive set of benefit entitlements and areas for improvement in terms of applying digitalization to SCM is developed. The main results of the paper indicate the relevance of Digitalization being realized in practice. In the form of seventeen concrete research action fields, the benefit entitlements are aggregated and transformed into potential starting points for future research projects in this area. The main contribution of this paper is an empirically grounded basis for future research projects and an overview of actual research action fields from practitioners’ point of view.
Abstract: Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.