Abstract: In many outlier detection tasks, only training data
belonging to one class, i.e., the positive class, is available. The
task is then to predict a new data point as belonging either to
the positive class or to the negative class, in which case the
data point is considered an outlier. For this task, we propose a
novel corrupted Generative Adversarial Network (CorGAN). In the
adversarial process of training CorGAN, the Generator generates
outlier samples for the negative class, and the Discriminator is trained
to distinguish the positive training data from the generated negative
data. The proposed framework is evaluated using an image dataset
and a real-world network intrusion dataset. Our outlier-detection
method achieves state-of-the-art performance on both tasks.
Abstract: This study examines two bilingual phenomena in the narratives of Arabic Hebrew and Hebrew-English bilingual adults in Israel: CO memories and code-switching (CS). The study examined these phenomena in the context of autobiographical memory, using a cue word technique. Student experimenters held two sessions in the homes of the participants. In separate language sessions, the participant was asked to look first at each of 16 cue words and then to state a concrete memory. After stating the memory, participants reported whether their memories were in the same language of the experiment session or different. Memories were classified as ‘Crossovers’ (CO) or ‘Same Language’ (SL) according to participants' self-reports. Participants were also required to elaborate about the setting, interlocutors and other languages involved in the specific memory. Beyond replicating the procedure of cuing technique, one memory from a specific lifespan period was chosen per participant, and the participant was required to provide further details about it. For the more detailed memories, CS count was conducted. Both bilingual groups confirmed the Reminiscence Bump phenomenon, retrieving more memories in the 10-30 age period. CO memories prevailed in second language sessions (L2). Same language memories were more abundant in first language sessions (L1). Higher CS frequency was found in L2 sessions. Finally, as predicted, 'individual' CS was prevalent in L2 sessions, but 'community-based' CS was not higher in L1 sessions. The two bilingual measures in this study, crossovers, and CS came from different research traditions, the former from an experimental paradigm in the psychology of autobiographical memory based on self-reported judgments, the latter a behavioral measure from linguistics. This merger of approaches offers new insight into the field of bilingual autobiographical memory. In addition, the study attempted to shed light on the investigation of motivations for CS, beginning with Walters’ SPPL Model and concluding with a distinction between ‘community-based’ and individual motivations.
Abstract: Earthquakes are considered to be the most destructive rapid-onset disasters human beings are exposed to. The amount of loss it brings in is sufficient to take careful considerations for designing of structures and facilities. Seismic Hazard Analysis is one such tool which can be used for earthquake resistant design. Ground Response Analysis is one of the most crucial and decisive steps for seismic hazard analysis. Rapar district of Kutch, Gujarat falls in Zone 5 of earthquake zone map of India and thus has high seismicity because of which it is selected for analysis. In total 8 bore-log data were studied at different locations in and around Rapar district. Different soil engineering properties were analyzed and relevant empirical correlations were used to calculate maximum shear modulus (Gmax) and shear wave velocity (Vs) for the soil layers. The soil was modeled using Pressure-Dependent Modified Kodner Zelasko (MKZ) model and the reference curve used for fitting was Seed and Idriss (1970) for sand and Darendeli (2001) for clay. Both Equivalent linear (EL), as well as Non-linear (NL) ground response analysis, has been carried out with Masing Hysteretic Re/Unloading formulation for comparison. Commercially available DEEPSOIL v. 7.0 software is used for this analysis. In this study an attempt is made to quantify ground response regarding generated acceleration time-history at top of the soil column, Response spectra calculation at 5 % damping and Fourier amplitude spectrum calculation. Moreover, the variation of Peak Ground Acceleration (PGA), Maximum Displacement, Maximum Strain (in %), Maximum Stress Ratio, Mobilized Shear Stress with depth is also calculated. From the study, PGA values estimated in rocky strata are nearly same as bedrock motion and marginal amplification is observed in sandy silt and silty clays by both analyses. The NL analysis gives conservative results of maximum displacement as compared to EL analysis. Maximum strain predicted by both studies is very close to each other. And overall NL analysis is more efficient and realistic because it follows the actual hyperbolic stress-strain relationship, considers stiffness degradation and mobilizes stresses generated due to pore water pressure.
Abstract: This case study explores the impact of two major computer software programs Learn to Speak English and Learn English Spelling and Pronunciation, and some Internet search engines such as Google on mending the decoding and spelling deficiency of Simon X, a dyslexic student. The improvement in decoding and spelling may result in better reading comprehension and composition writing. Some computer programs and Internet materials can help regain the missing awareness and consequently restore his self-confidence and self-esteem. In addition, this study provides a systematic plan comprising a set of activities (four computer programs and Internet materials) which address the problem from the lowest to the highest levels of phoneme and phonological awareness. Four methods of data collection (accounts, observations, published tests, and interviews) create the triangulation to validly and reliably collect data before the plan, during the plan, and after the plan. The data collected are analyzed quantitatively and qualitatively. Sometimes the analysis is either quantitative or qualitative, and some other times a combination of both. Tables and figures are utilized to provide a clear and uncomplicated illustration of some data. The improvement in the decoding, spelling, reading comprehension, and composition writing skills that occurred is proved through the use of authentic materials performed by the student under study. Such materials are a comparison between two sample passages written by the learner before and after the plan, a genuine computer chat conversation, and the scores of the academic year that followed the execution of the plan. Based on these results, the researcher recommends further studies on other Lebanese dyslexic learners using the computer to mend their language problem in order to design and make a most reliable software program that can address this disability more efficiently and successfully.
Abstract: To transform a space into a better livable and sustainable zone, United Nations Summit in New York 2015, has decided upon 17 sustainable development goals (SDGs) that approach directly to achieve inclusive, people-centric, sustainable developments. Though sustainability has been majorly constructed by four pillars, namely, Ecological, Economic, Social and Cultural, but it is essentially reduced to economic and ecological consideration in the context of developing countries. Therefore, in most cases planning has reduced its ambit to concentrate around the tangible infrastructure, ignoring the fundamentals of socio-cultural heritage. With the accentuating hype of infrastructural augmentation, lack of emphasis of traditional concerns like ethnicity and social connection have further diluted the situation, disintegrating cultural continuity. As cultural continuity lacks its cohesion, it’s growing absence increasingly acts as a catalyst to degrade the heritage structures, spaces around and linking these structures, and the ability of stakeholders in identifying themselves rooted in that particular space. Hence, this paper will argue that sustainability depends on the people and their interaction with their surroundings, their culture and livelihood. The interaction between people and their surroundings strengthen community building and social interaction that abides by stakeholders reverting back to their roots. To assess the socio-cultural sustainability of the city of Kolkata, two study areas are selected, namely, an old settlement from the northern part of the city of Kolkata (KMA), imbued with social connection, age-old cultural and ethnic bonding and, another cluster of new high-rises coming up in the Newtown area having portions of planned city extension on the eastern side of the city itself. Whereas, Newtown prioritizes the surging post-industrial trends of economic aspiration and ecological aspects of urban sustainability; the former settlements of northern Kolkata still continue to represent the earliest community settlement of the British-colonial-cum native era and even the pre-colonial era, permeated with socio-cultural reciprocation. Thus, to compare and assess the inlayed organizational structure of both the spaces in the two cases, selected areas have been surveyed to portray their current imageability. The argument of this paper is structured in 5parts. First, an introduction of the idea has been forwarded, Secondly, a literature review has been conducted to ground the proposed ideas, Thirdly, methodology has been discussed and appropriate case study areas have been selected, Fourthly, surveys and analyses has been forwarded and lastly, the paper has arrived at a set of conclusions by suggesting a threefold development to create happy, healthy and sustainable community.
Abstract: In this paper, we propose an optimized brain computer
interface (BCI) system for unspoken speech recognition, based on
the fact that the constructions of unspoken words rely strongly on the
Wernicke area, situated in the temporal lobe. Our BCI system has four
modules: (i) the EEG Acquisition module based on a non-invasive
headset with 14 electrodes; (ii) the Preprocessing module to remove
noise and artifacts, using the Common Average Reference method;
(iii) the Features Extraction module, using Wavelet Packet Transform
(WPT); (iv) the Classification module based on a one-hidden layer
artificial neural network. The present study consists of comparing
the recognition accuracy of 5 Arabic words, when using all the
headset electrodes or only the 4 electrodes situated near the Wernicke
area, as well as the selection effect of the subbands produced by
the WPT module. After applying the articial neural network on the
produced database, we obtain, on the test dataset, an accuracy of
83.4% with all the electrodes and all the subbands of 8 levels of the
WPT decomposition. However, by using only the 4 electrodes near
Wernicke Area and the 6 middle subbands of the WPT, we obtain
a high reduction of the dataset size, equal to approximately 19% of
the total dataset, with 67.5% of accuracy rate. This reduction appears
particularly important to improve the design of a low cost and simple
to use BCI, trained for several words.
Abstract: In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset.
Abstract: Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.
Abstract: The implementation of lean manufacturing initiatives has produced significant impacts in improving operational performance and reducing manufacturing wastes in the production process. However, selecting an appropriate set of lean strategies is critical to avoid misapplication of the lean manufacturing techniques and consequential increase in non-value-adding activities. To the author’s best knowledge, there is currently no methodology to select lean strategies that considers their impacts on manufacturing wastes and performance metrics simultaneously. In this research, a multi-objective methodology is proposed that suggests an appropriate set of lean initiatives based on their impacts on performance metrics and manufacturing wastes and within manufacturers’ resource limitation. The proposed methodology in this research suggests the best set of lean initiatives for implementation that have highest impacts on identified critical performance metrics and manufacturing wastes. Therefore, manufacturers can assure that implementing suggested lean tools improves their production performance and reduces manufacturing wastes at the same time. A case study was conducted to show the effectiveness and validate the proposed model and methodologies.
Abstract: Motion recognition from videos is actually a very
complex task due to the high variability of motions. This paper
describes the challenges of human motion recognition, especially
motion representation step with relevant features. Our descriptor
vector is inspired from Laban Movement Analysis method. We
propose discriminative features using the Random Forest algorithm
in order to remove redundant features and make learning algorithms
operate faster and more effectively. We validate our method on
MSRC-12 and UTKinect datasets.
Abstract: Research background: Problems related to the occupational health and decreasing level of safety occur commonly in the construction industry. Important factor in the occupational safety in construction industry is scaffold use. All scaffolds used in construction, renovation, and demolition shall be erected, dismantled and maintained in accordance with safety procedure. Increasing demand for new construction projects unfortunately still is linked to high level of occupational accidents. Therefore, it is crucial to implement concrete actions while dealing with scaffolds and risk assessment in construction industry, the way on doing assessment and liability of assessment is critical for both construction workers and regulatory framework. Unfortunately, professionals, who tend to rely heavily on their own experience and knowledge when taking decisions regarding risk assessment, may show lack of reliability in checking the results of decisions taken. Purpose of the article: The aim was to indicate crucial parameters that could be modeling with Risk Assessment Model (RAM) use for improving both building enterprise productivity and/or developing potential and safety climate. The developed RAM could be a benefit for predicting high-risk construction activities and thus preventing accidents occurred based on a set of historical accident data. Methodology/Methods: A RAM has been developed for assessing risk levels as various construction process stages with various work trades impacting different spheres of enterprise activity. This project includes research carried out by teams of researchers on over 60 construction sites in Poland and Portugal, under which over 450 individual research cycles were carried out. The conducted research trials included variable conditions of employee exposure to harmful physical and chemical factors, variable levels of stress of employees and differences in behaviors and habits of staff. Genetic modeling tool has been used for developing the RAM. Findings and value added: Common types of trades, accidents, and accident causes have been explored, in addition to suitable risk assessment methods and criteria. We have found that the initial worker stress level is more direct predictor for developing the unsafe chain leading to the accident rather than the workload, or concentration of harmful factors at the workplace or even training frequency and management involvement.
Abstract: Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.
Abstract: Modelling of Timoshenko beams on elastic foundations has been widely used in the analysis of buildings, geotechnical problems, and, railway and aerospace structures. For the elastic foundation, the most widely used models are one-parameter mechanical models or two-parameter models to include continuity and cohesion of typical foundations, with the two-parameter usually considered the better of the two. Knowledge of free vibration characteristics of beams on an elastic foundation is considered necessary for optimal design solutions in many engineering applications, and in this work, the efficient and accurate variational iteration method is developed and used to calculate natural frequencies of a Timoshenko beam on a two-parameter foundation. The variational iteration method is a technique capable of dealing with some linear and non-linear problems in an easy and efficient way. The calculations are compared with those using a finite-element method and other analytical solutions, and it is shown that the results are accurate and are obtained efficiently. It is found that the effect of the presence of the two-parameter foundation is to increase the beam’s natural frequencies and this is thought to be because of the shear-layer stiffness, which has an effect on the elastic stiffness. By setting the two-parameter model’s stiffness parameter to zero, it is possible to obtain a one-parameter foundation model, and so, comparison between the two foundation models is also made.
Abstract: Obesity and overweight is one of the most common metabolic disorders in industrialized countries and in developing countries. One consequence of pathological obesity is cardiovascular disease and metabolic syndrome. Chemerin is an adipocyne that plays a role in the regulation of the adipocyte function and the metabolism of glucose in the liver and musculoskeletal system. Most likely, chemerin is involved in obesity-related disorders such as type 2 diabetes and cardiovascular disease. Aerobic exercises reduce the level of chemerin and cause macrophage penetration into fat cells and inflammatory factors. Several efforts have been made to clarify the cellular and molecular mechanisms of hypertrophy and muscular atrophy. Myostatin, a new member of the TGF-β family, is a transforming growth factor β that its expression negatively regulates the growth of the skeletal muscle; and the increase of this hormone has been observed in conditions of muscular atrophy. While in response to muscle overload, its levels decrease after the atrophy period, TGF-β is the most important cytokine in the development of skeletal muscle. Myostatin plays an important role in muscle control, and animal and human studies show a negative role of myostatin in the growth of skeletal muscle. Separation of myostatin from Golgi begins on the ninth day of the onset period and continues until birth at all times of muscle growth. Higher levels of myostatin are found in obese people. Resistance training for 10 weeks could reduce levels of plasma myostatin.
Abstract: An automated fibre placement method has been
developed to build through-thickness reinforcement into carbon fibre
reinforced plastic laminates during their production, with the goal
of increasing delamination fracture toughness while circumventing
the additional costs and defects imposed by post-layup stitching
and z-pinning. Termed ‘inter-weaving’, the method uses custom
placement sequences of thermoset prepreg tows to distribute regular
fibre link regions in traditionally clean ply interfaces. Inter-weaving’s impact on mode I delamination fracture toughness
was evaluated experimentally through double cantilever beam tests
(ASTM standard D5528-13) on [±15°]9 laminates made from Park
Electrochemical Corp. E-752-LT 1/4” carbon fibre prepreg tape.
Unwoven and inter-woven automated fibre placement samples were
compared to those of traditional laminates produced from standard
uni-directional plies of the same material system. Unwoven automated fibre placement laminates were found to
suffer a mostly constant 3.5% decrease in mode I delamination
fracture toughness compared to flat uni-directional plies. Inter-weaving caused significant local fracture toughness increases
(up to 50%), though these were offset by a matching overall
reduction. These positive and negative behaviours of inter-woven
laminates were respectively found to be caused by fibre breakage
and matrix deformation at inter-weave sites, and the 3D layering
of inter-woven ply interfaces providing numerous paths of least
resistance for crack propagation.
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: Feature selection and attribute reduction are crucial
problems, and widely used techniques in the field of machine
learning, data mining and pattern recognition to overcome the
well-known phenomenon of the Curse of Dimensionality. This paper
presents a feature selection method that efficiently carries out attribute
reduction, thereby selecting the most informative features of a dataset.
It consists of two components: 1) a measure for feature subset
evaluation, and 2) a search strategy. For the evaluation measure,
we have employed the fuzzy-rough dependency degree (FRFDD)
of the lower approximation-based fuzzy-rough feature selection
(L-FRFS) due to its effectiveness in feature selection. As for the
search strategy, a modified version of a binary shuffled frog leaping
algorithm is proposed (B-SFLA). The proposed feature selection
method is obtained by hybridizing the B-SFLA with the FRDD. Nine
classifiers have been employed to compare the proposed approach
with several existing methods over twenty two datasets, including
nine high dimensional and large ones, from the UCI repository.
The experimental results demonstrate that the B-SFLA approach
significantly outperforms other metaheuristic methods in terms of the
number of selected features and the classification accuracy.
Abstract: Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.
Abstract: In order to study the topic of cities crossing rivers, a Four-Dimensional Analysis Method consisting of timeline, X-axis, Y-axis, and Z-axis is proposed. Policies, plans, and their implications are summarized and researched along with the timeline. The X-axis is the direction which is parallel to the river. The research area was chosen because of its important connection function. It is proposed that more surface water network should be built because of the ecological orientation of the research area. And the analysis of groundwater makes it for sure that the proposal is feasible. After the blue water network is settled, the green landscape network which is surrounded by it could be planned. The direction which is transversal to the river (Y-axis) should run through the transportation axis so that the urban texture could stretch in an ecological way. Therefore, it is suggested that the work of the planning bureau and river bureau should be coordinated. The Z-axis research is on the section view of the river, especially on the Yellow River’s special feature of being a perched river. Based on water control safety demands, river parks could be constructed on the embankment buffer zone, whereas many kinds of ornamental trees could be used to build the buffer zone. City Crossing River is a typical case where we make use of landscaping to build a symbiotic relationship between the urban landscape architecture and the environment. The local environment should be respected in the process of city expansion. The planning order of "Benefit- Flood Control Safety" should be replaced by "Flood Control Safety - Landscape Architecture- People - Benefit".
Abstract: The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.