Abstract: The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.
Abstract: Satellite imagery is one of the emerging technologies which are extensively utilized in various applications such as detection/extraction of man-made structures, monitoring of sensitive areas, creating graphic maps etc. The main approach here is the automated detection of buildings from very high resolution (VHR) optical satellite images. Initially, the shadow, the building and the non-building regions (roads, vegetation etc.) are investigated wherein building extraction is mainly focused. Once all the landscape is collected a trimming process is done so as to eliminate the landscapes that may occur due to non-building objects. Finally the label method is used to extract the building regions. The label method may be altered for efficient building extraction. The images used for the analysis are the ones which are extracted from the sensors having resolution less than 1 meter (VHR). This method provides an efficient way to produce good results. The additional overhead of mid processing is eliminated without compromising the quality of the output to ease the processing steps required and time consumed.
Abstract: This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.
Abstract: Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.
Abstract: The Mexican educational system faces diverse challenges related with the quality and coverage of education. The development of Intelligent Tutoring Systems (ITS) may help to solve some of them by helping teachers to customize their classes according to the performance of the students in online courses. In this work, we propose the adaptation of a functional ITS based on Bloom’s taxonomy called Sistema de Apoyo Generalizado para la Enseñanza Individualizada (SAGE), to measure student’s metacognition and their emotional response based on Marzano’s taxonomy. The students and the system will share the control over the advance in the course, so they can improve their metacognitive skills. The system will not allow students to get access to subjects not mastered yet. The interaction between the system and the student will be implemented through Natural Language Processing techniques, thus avoiding the use of sensors to evaluate student’s response. The teacher will evaluate student’s knowledge utilization, which is equivalent to the last cognitive level in Marzano’s taxonomy.
Abstract: This study investigated the relation between processing information and fitness level of active (fit) and sedentary (unfit) children drawn from rural and urban areas in Botswana. It was hypothesized that fit children would display faster simple reaction time (SRT), choice reaction times (CRT) and movement times (SMT). 60, third grade children (7.0 – 9.0 years) were initially selected and based upon fitness testing, 45 participated in the study (15 each of fit urban, unfit urban, fit rural). All children completed anthropometric measures, skinfold testing and submaximal cycle ergometer testing. The cognitive testing included SRT, CRT, SMT and Choice Movement Time (CMT) and memory sequence length. Results indicated that the rural fit group exhibited faster SMT than the urban fit and unfit groups. For CRT, both fit groups were faster than the unfit group. Collectively, the study shows that the relationship that exists between physical fitness and cognitive function amongst the elderly can tentatively be extended to the pediatric population. Physical fitness could be a factor in the speed at which we process information, including decision making, even in children.
Abstract: In this modern era of automation, most of the academic
exams and competitive exams are Multiple Choice Questions (MCQ).
The responses of these MCQ based exams are recorded in the
Optical Mark Reader (OMR) sheet. Evaluation of the OMR sheet
requires separate specialized machines for scanning and marking.
The sheets used by these machines are special and costs more than a
normal sheet. Available process is non-economical and dependent on
paper thickness, scanning quality, paper orientation, special hardware
and customized software. This study tries to tackle the problem of
evaluating the OMR sheet without any special hardware and making
the whole process economical. We propose an image processing
based algorithm which can be used to read and evaluate the scanned
OMR sheets with no special hardware required. It will eliminate the
use of special OMR sheet. Responses recorded in normal sheet is
enough for evaluation. The proposed system takes care of color,
brightness, rotation, little imperfections in the OMR sheet images.
Abstract: In the present work we developed an image processing
algorithm to measure water droplets characteristics during dropwise
condensation on pillared surfaces. The main problem in this process is
the similarity between shape and size of water droplets and the pillars.
The developed method divides droplets into four main groups based
on their size and applies the corresponding algorithm to segment each
group. These algorithms generate binary images of droplets based
on both their geometrical and intensity properties. The information
related to droplets evolution during time including mean radius and
drops number per unit area are then extracted from the binary images.
The developed image processing algorithm is verified using manual
detection and applied to two different sets of images corresponding
to two kinds of pillared surfaces.
Abstract: High-density polyethylene reinforced with carbon nanofibers (HDPE/CNF) have been prepared via melt processing using dialkylimidazolium tetrafluoroborate (ionic liquid) as a dispersion agent. The prepared samples were characterized by thermogravimetric (TGA) and differential scanning calorimetric (DSC) analyses. The samples blended with imidazolium ionic liquid exhibit higher thermal stability. DSC analysis showed clear miscibility of ionic liquid in the HDPE matrix and showed single endothermic peak. The melt rheological analysis of HDPE/CNF composites was performed using an oscillatory rheometer. The influence of CNF and ionic liquid concentration (ranging from 0, 0.5, and 1 wt%) on the viscoelastic parameters was investigated at 200 °C with an angular frequency range of 0.1 to 100 rad/s. The rheological analysis shows the shear-thinning behavior for the composites. An improvement in the viscoelastic properties was observed as the nanofiber concentration increases. The progress in the modulus values was attributed to the structural rigidity imparted by the high aspect ratio CNF. The modulus values and complex viscosity of the composites increased significantly at low frequencies. Composites blended with ionic liquid exhibit slightly lower values of complex viscosity and modulus over the corresponding HDPE/CNF compositions. Therefore, reduction in melt viscosity is an additional benefit for polymer composite processing as a result of wetting effect by polymer-ionic liquid combinations.
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: 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: Fermentation as an important operation in the processing of cocoa beans is now affected by the recent climate change across the globe. The major requirement for effective fermentation is the ability of the material used to retain sufficient heat for the required microbial activities. Apart from the effects of climate on the rate of heat retention, the materials used for fermentation plays an important role. Most Farmers still restrict fermentation activities to the use of traditional methods. Improving on cocoa fermentation in this era of climate change makes it necessary to work on other materials that can be suitable for cocoa fermentation. Therefore, the objective of this study was to determine the effects of fermentation techniques on the quality of cocoa beans. The materials used in this fermentation research were heap-leaves (traditional), stainless steel, plastic tin, plastic basket and wooden box. The period of fermentation varies from zero days to 10 days. Physical and chemical tests were carried out for variables in quality determination in the samples. The weight per bean varied from 1.0-1.2 g after drying across the samples and the major color of the dry beans observed was brown except with the samples from stainless steel. The moisture content varied from 5.5-7%. The mineral content and the heavy metals decreased with increase in the fermentation period. A wooden box can conclusively be used as an alternative to heap-leaves as there was no significant difference in the physical features of the samples fermented with the two methods. The use of a wooden box as an alternative for cocoa fermentation is therefore recommended for cocoa farmers.
Abstract: Food safety is a significant and growing public health problem in the world and Nigeria as a developing country, since food-borne diseases are important contributors to the huge burden of sickness and death of humans. In Nigeria, traditional ready-to-eat meat products (RTE-MPs) like balangu, tsire, guru and dried meat products like kilishi, dambun nama, banda, were reported to be highly appreciated because of their eating qualities. The consumption of these products was considered as safe due to the treatments that are usually involved during their production process. However, during processing and handling, the products could be contaminated by pathogens that could cause food poisoning. Therefore, a hazard identification for pathogenic bacteria on some traditional RTE-MPs was conducted in Kebbi and Sokoto States, Nigeria. A total of 116 RTE-MPs (balangu-38, kilishi-39 and tsire-39) samples were obtained from retail outlets and analyzed using standard cultural microbiological procedures in general and selective enrichment media to isolate the target pathogens. A six-fold serial dilution was prepared and using the pour plating method, colonies were counted. Serial dilutions were selected based on the prepared pre-labeled Petri dishes for each sample. A volume of 10-12 ml of molten Nutrient agar cooled to 42-45°C was poured into each Petri dish and 1 ml each from dilutions of 102, 104 and 106 for every sample was respectively poured on a pre-labeled Petri plate after which colonies were counted. The isolated pathogens were identified and confirmed after series of biochemical tests. Frequencies and percentages were used to describe the presence of pathogens. The General Linear Model was used to analyze data on pathogen presence according to RTE-MPs and means were separated using the Tukey test at 0.05 confidence level. Of the 116 RTE-MPs samples collected, 35 (30.17%) samples were found to be contaminated with some tested pathogens. Prevalence results showed that Escherichia coli, salmonella and Staphylococcus aureus were present in the samples. Mean total bacterial count was 23.82×106 cfu/g. The frequency of individual pathogens isolated was; Staphylococcus aureus 18 (15.51%), Escherichia coli 12 (10.34%) and Salmonella 5 (4.31%). Also, among the RTE-MPs tested, the total bacterial counts were found to differ significantly (P < 0.05), with 1.81, 2.41 and 2.9×104 cfu/g for tsire, kilishi, and balangu, respectively. The study concluded that the presence of pathogenic bacteria in balangu could pose grave health risks to consumers, and hence, recommended good manufacturing practices in the production of balangu to improve the products’ safety.
Abstract: The cardiopulmonary signal monitoring, without the
usage of contact electrodes or any type of in-body sensors, has
several applications such as sleeping monitoring and continuous
monitoring of vital signals in bedridden patients. This system has
also applications in the vehicular environment to monitor the driver,
in order to avoid any possible accident in case of cardiac failure.
Thus, the bio-radar system proposed in this paper, can measure vital
signals accurately by using the Doppler effect principle that relates
the received signal properties with the distance change between the
radar antennas and the person’s chest-wall. Once the bio-radar aim
is to monitor subjects in real-time and during long periods of time,
it is impossible to guarantee the patient immobilization, hence their
random motion will interfere in the acquired signals. In this paper,
a mathematical model of the bio-radar is presented, as well as its
simulation in MATLAB. The used algorithm for breath rate extraction
is explained and a method for DC offsets removal based in a motion
detection system is proposed. Furthermore, experimental tests were
conducted with a view to prove that the unavoidable random motion
can be used to estimate the DC offsets accurately and thus remove
them successfully.
Abstract: Being made with the lightest commercially available industrial metal, Magnesium (Mg) alloys are of interest for light-weighting. Expanding their application to different material processing methods requires Mg properties at large strains. Several room-temperature processes such as shot and laser peening and hole cold expansion need compressive large strain data. Two methods have been proposed in the literature to obtain the stress-strain curve at high strains: 1) anti-buckling guides and 2) small cubic samples. In this paper, an anti-buckling fixture is used with the help of digital image correlation (DIC) to obtain the compression-tension (C-T) of AZ31B-H24 rolled sheet at large strain values of up to 10.5%. The effect of the anti-bucking fixture on stress-strain curves is evaluated experimentally by comparing the results with those of the compression tests of cubic samples. For testing cubic samples, a new fixture has been designed to increase the accuracy of testing cubic samples with DIC strain measurements. Results show a negligible effect of anti-buckling on stress-strain curves, specifically at high strain values.
Abstract: The dynamic variation in memory devices such as the Static Random Access Memory can give errors in read or write operations. In this paper, the effect of low-frequency and random telegraph noise on the dynamic variation of one SRAM cell is detailed. The effect on circuit noise, speed, and length of time of processing is examined, using the Supply Read Retention Voltage and the Read Static Noise Margin. New test run methods are also developed. The obtained results simulation shows the importance of noise caused by dynamic variation, and the impact of Random Telegraph noise on SRAM variability is examined by evaluating the statistical distributions of Random Telegraph noise amplitude in the pull-up, pull-down. The threshold voltage mismatch between neighboring cell transistors due to intrinsic fluctuations typically contributes to larger reductions in static noise margin. Also the contribution of each of the SRAM transistor to total dynamic variation has been identified.
Abstract: Lung CT image segmentation is a prerequisite in lung
CT image analysis. Most of the conventional methods need a
post-processing to deal with the abnormal lung CT scans such as
lung nodules or other lesions. The simplest similarity measure in
the standard Graph Cuts Algorithm consists of directly comparing
the pixel values of the two neighboring regions, which is not
accurate because this kind of metrics is extremely sensitive to minor
transformations such as noise or other artifacts problems. In this work,
we propose an improved version of the standard graph cuts algorithm
based on the Patch-Based similarity metric. The boundary penalty
term in the graph cut algorithm is defined Based on Patch-Based
similarity measurement instead of the simple intensity measurement
in the standard method. The weights between each pixel and its
neighboring pixels are Based on the obtained new term. The graph
is then created using theses weights between its nodes. Finally,
the segmentation is completed with the minimum cut/Max-Flow
algorithm. Experimental results show that the proposed method is
very accurate and efficient, and can directly provide explicit lung
regions without any post-processing operations compared to the
standard method.
Abstract: This document describes an advanced system and methodology for Cross Traffic Alert (CTA), able to detect vehicles that move into the vehicle driving path from the left or right side. The camera is supposed to be not only on a vehicle still, e.g. at a traffic light or at an intersection, but also moving slowly, e.g. in a car park. In all of the aforementioned conditions, a driver’s short loss of concentration or distraction can easily lead to a serious accident. A valid support to avoid these kinds of car crashes is represented by the proposed system. It is an extension of our previous work, related to a clustering system, which only works on fixed cameras. Just a vanish point calculation and simple optical flow filtering, to eliminate motion vectors due to the car relative movement, is performed to let the system achieve high performances with different scenarios, cameras and resolutions. The proposed system just uses as input the optical flow, which is hardware implemented in the proposed platform and since the elaboration of the whole system is really speed and power consumption, it is inserted directly in the camera framework, allowing to execute all the processing in real-time.
Abstract: The present article discusses design and development of Information System for monitoring ecology within the Black Sea basin of Georgia. Sea parameters, river, estuary, vulnerable district, water sample, etc. were considered as the major parameters of the sea ecosystem. A conceptual schema has been developed for the Black Sea ecosystem based on object-role model. The experimental database for the Black Sea ecosystem has been constructed using Ms SQL Server, while the object-role model NORMA has been developed using graphical instrument Ms Visual Studio within the integrated environment of .NET Framework 4.5. Web portal has been designed based on Ms SharePoint Server. The server database connection with web-portal has been carried out by means of External List of Ms SharePoint Server Designer.
Abstract: Due to the constant development of measurement systems and the aim for computerization, unavoidable improvements are made for the main disadvantages of air gauges. With the appearance of the air-electronic measuring devices, some of their disadvantages are solved. The output electrical signal allows them to be included in the modern systems for measuring information processing and process management. Producer efforts are aimed at reducing the influence of supply pressure and measurement system setup errors. Increased accuracy requirements and preventive error measures are due to the main uses of air electronic systems - measurement of geometric dimensions in the automotive industry where they are applied as modules in measuring systems to measure geometric parameters, form, orientation and location of the elements.