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: What people say on social media has turned into a
rich source of information to understand social behavior. Specifically,
the growing use of Twitter social media for political communication
has arisen high opportunities to know the opinion of large numbers
of politically active individuals in real time and predict the global
political tendencies of a specific country. It has led to an increasing
body of research on this topic. The majority of these studies have
been focused on polarized political contexts characterized by only
two alternatives. Unlike them, this paper tackles the challenge
of forecasting Spanish political trends, characterized by multiple
political parties, by means of analyzing the Twitters Users political
tendency. According to this, a new strategy, named Tweets Analysis
Strategy (TAS), is proposed. This is based on analyzing the users
tweets by means of discovering its sentiment (positive, negative or
neutral) and classifying them according to the political party they
support. From this individual political tendency, the global political
prediction for each political party is calculated. In order to do this,
two different strategies for analyzing the sentiment analysis are
proposed: one is based on Positive and Negative words Matching
(PNM) and the second one is based on a Neural Networks Strategy
(NNS). The complete TAS strategy has been performed in a Big-Data
environment. The experimental results presented in this paper reveal
that NNS strategy performs much better than PNM strategy to analyze
the tweet sentiment. In addition, this research analyzes the viability
of the TAS strategy to obtain the global trend in a political context
make up by multiple parties with an error lower than 23%.
Abstract: The additional relationships i.e., start-to-start, finish-to-finish, and start-to-finish, between activity in Precedence Diagram Method (PDM) provides a more flexible schedule than traditional Critical Path Method (CPM). But, changing the duration of critical activities in the PDM network will have an anomalous effect on the critical path and the project completion date. In this study, we classified the critical activities in two groups i.e., 1. activity on single critical path and 2. activity on multi-critical paths, and six classes i.e., normal, reverse, neutral, perverse, decrease-reverse and increase-normal, based on their effects on project duration in PDM. Furthermore, we determined the maximum float of time by which the duration each type of critical activities can be changed without effecting the project duration. This study would help the project manager to clearly understand the behavior of each critical activity on critical path, and he/she would be able to change the project duration by shortening or lengthening activities based on project budget and project deadline.
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.
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: In this study, we have proposed a gesture to emotion recognition method using flex sensors mounted on metacarpophalangeal joints. The flex sensors are fixed in a wearable glove. The data from the glove are sent to PC using Wi-Fi. Four gestures: finger pointing, thumbs up, fist open and fist close are performed by five subjects. Each gesture is categorized into sad, happy, and excited class based on the velocity and acceleration of the hand gesture. Seventeen inspectors observed the emotions and hand gestures of the five subjects. The emotional state based on the investigators assessment and acquired movement speed data is compared. Overall, we achieved 77% accurate results. Therefore, the proposed design can be used for emotional state detection applications.
Abstract: Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.
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: This paper reports on a joint research project in which a researcher in applied linguistics and elementary school teachers in Japan explored new ways to realize emotional synchrony in a classroom in childhood education. The primary purpose of this project was to develop a cross-curriculum of the first language (L1) and second language (L2) based on the concept of plurilingualism. This concept is common in Europe, and can-do statements are used in forming the standard of linguistic proficiency in any language; these are attributed to the action-oriented approach in the Common European Framework of Reference for Languages (CEFR). CEFR has a basic tenet of language education: improving communicative competence. Can-do statements are classified into five categories based on the tenet: reading, writing, listening, speaking/ interaction, and speaking/ speech. The first approach of this research was to specify the linguistic proficiency of the children, who are still developing their L1. Elementary school teachers brainstormed and specified the linguistic proficiency of the children as the competency needed to synchronize with others – teachers or peers – physically and mentally. The teachers formed original can-do statements in language proficiency on the basis of the idea that emotional synchrony leads to understanding others in communication. The research objectives are to determine the effect of language education based on the newly developed curriculum and can-do statements. The participants of the experiment were 72 third-graders in Uji Elementary School, Japan. For the experiment, 17 items were developed from the can-do statements formed by the teachers and divided into the same five categories as those of CEFR. A can-do checklist consisting of the items was created. The experiment consisted of three steps: first, the students evaluated themselves using the can-do checklist at the beginning of the school year. Second, one year of instruction was given to the students in Japanese and English classes (six periods a week). Third, the students evaluated themselves using the same can-do checklist at the end of the school year. The results of statistical analysis showed an enhancement of linguistic proficiency of the students. The average results of the post-check exceeded that of the pre-check in 12 out of the 17 items. Moreover, significant differences were shown in four items, three of which belonged to the same category: speaking/ interaction. It is concluded that children can get to understand others’ minds through physical and emotional synchrony. In particular, emotional synchrony is what teachers should aim at in childhood education.
Abstract: Teaching and learning EFL speaking modules is one of the most challenging productive modules for both instructors and learners. In a student-centered interactive communicative language teaching approach, learners and instructors should be aware of the fact that the target language must be taught as/for communication. The student must be empowered by tools that will work on more than one level of their communicative competence. Communicative learning will need a teaching and learning methodology that will address the goal. Teaching turn-taking rules, pragmatic principles and speech acts will enhance students' sociolinguistic competence, strategic competence together with discourse competence. Sociolinguistic competence entails the mastering of speech act conventions and illocutionary acts of refusing, agreeing/disagreeing; emotive acts like, thanking, apologizing, inviting, offering; directives like, ordering, requesting, advising, and hinting, among others. Strategic competence includes enlightening students’ consciousness of the various particular turn-taking systemic rules of organizing techniques of opening and closing conversation, adjacency pairs, interrupting, back-channeling, asking for/giving opinion, agreeing/disagreeing, using natural fillers for pauses, gaps, speaker select, self-select, and silence among others. Students will have the tools to manage a conversation. Students are engaged in opportunities of experiencing the natural language not as a mere extra student talking time but rather an empowerment of knowing and using the strategies. They will have the component items they need to use as well as the opportunity to communicate in the target language using topics of their interest and choice. This enhances students' communicative abilities. Available websites and textbooks now use one or more of these tools of turn-taking or pragmatics. These will be students' support in self-study in their independent learning study hours. This will be their reinforcement practice on e-Learning interactive activities. The students' target is to be able to communicate the intended meaning to an addressee that is in turn able to infer that intended meaning. The combination of these tools will be assertive and encouraging to the student to beat the struggle with what to say, how to say it, and when to say it. Teaching the rules, principles and techniques is an act of awareness raising method engaging students in activities that will lead to their pragmatic discourse competence. The aim of the paper is to show how the suggested pragmatic model will empower students with tools and systems that would support their learning. Supporting students with turn taking rules, speech act theory, applying both to texts and practical analysis and using it in speaking classes empowers students’ pragmatic discourse competence and assists them to understand language and its context. They become more spontaneous and ready to learn the discourse pragmatic dimension of the speaking techniques and suitable content. Students showed a better performance and a good motivation to learn. The model is therefore suggested for speaking modules in EFL classes.
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: A Markov model defines a system of states, composed
by the feasible transition paths between those states, and the
parameters of those transitions. The paths and parameters may be
a representative way to address healthcare issues, such as to identify
the most likely sequence of patient health states given the sequence
of observations. Furthermore estimating the length of stay (LoS) of
patients in hospitalization is one of the challenges that Markov models
allow us to solve. However, finding the maximum probability of
any path that gets to state at time t, can have high computational
cost. A quantum approach allows us to take advantage of quantum
computation since the calculated probabilities can be in several states,
ending up to outperform classical computing due to the possible
superposition of states when handling large amounts of data. The
aid of quantum physics-based architectures and machine learning
techniques are therefore appropriated to address the complexity of
healthcare.
Abstract: Social work education in Poland focuses mostly on developing competencies that address the needs of individuals and families affected by a variety of life's problems. As a result of the ageing of the Polish population, much attention is equally devoted to adults, including the elderly. However, social work with children and young people is the area of education which should be given more consideration. Social work students are mostly trained to cater to the needs of families and the competencies aimed to respond to the needs of children and young people do not receive enough attention and are only offered as elective classes. This paper strives to review the social work programmes offered by the selected higher education institutions in Poland in terms of social work training aimed at helping children and young people to address their life problems. The analysis conducted in this study indicates that university education for social work focuses on training professionals who will provide assistance only to adults. Due to changes in the social and political situation, including, in particular, changes in social policy implemented for the needy, it is necessary to extend this area of education to include the specificity of the support for children and young people; especially, in the light of the appearance of new support professions within the area of social work. For example, family assistants, whose task is to support parents in performing their roles as guardians and educators, also assist children. Therefore, it becomes necessary to equip social work professionals with competencies which include issues related to the quality of life of underage people living in families. Social work curricula should be extended to include the issues of child and young person development and the patterns governing this phase of life.
Abstract: Classical matrix calculus and Routh-Hurwitz stability conditions, applied to the snake-like motion of the conical wheel axle, lead to the conclusion that the hunting mode is inherently unstable, and its natural frequency is a complex number. In order to analytically solve such a complicated vibration model, either the inertia terms were neglected, in the model designated as geometrical, or restrictions on the creep coefficients and yawing diameter were imposed, in the so-called dynamical model. Here, an alternative solution is proposed to solve the hunting mode, based on the observation that the bullet train wheel axle is equipped with cylindrical wheels. One argues that for such wheel treads, the geometrical hunting is irrelevant, since its natural frequency becomes nil, but the dynamical hunting is significant since its natural frequency reduces to a real number. Moreover, one illustrates that the geometrical simplification of the wheel causes the stabilization of the hunting mode, since the characteristic quartic equation, derived for conical wheels, reduces to a quadratic equation of positive coefficients, for cylindrical wheels. Quite simple analytical expressions for the damping ratio and natural frequency are obtained, without applying restrictions into the model of contact. Graphs of the time-depending hunting lateral perturbation, including the maximal and inflexion points, are presented both for the critically-damped and the over-damped wheel axles.
Abstract: Competitiveness and sustainability issues not only put pressure on big companies, but also small and medium enterprises (SMEs). SMEs Batik Bogor is one of the local culture-based creative industries in Bogor city which is also dealing with the issue of sustainability. The purpose of this research is to develop framework of sustainability at SMEs Batik Indonesia case of SMEs Batik Bogor by integrating innovation of cleaner production in its supply chain. The approach used is desk study, field survey, in-depth interviews, and benchmarking best practices of SMEs sustainability. In-depth interviews involve stakeholders to identify the needs and standards of sustainability of SMEs Batik. Data analysis was done by benchmarking method, Multi Dimension Scaling (MDS) method, and Strength, Weakness, Opportunity, Threat (SWOT) analysis. The results recommend the framework of sustainability for SMEs Batik in Indonesia. The sustainability status of SMEs Batik Bogor is classified as Moderate Sustainable. Factors that support the sustainability of SMEs Batik Bogor such is a strong commitment of top management in adopting cleaner production innovation and creativity approach. Successful cleaner production innovations are implemented primarily in the substitution of dye materials from toxic to non-toxic, reducing the intensity of non-renewable energy use, as well as the reuse and recycle of solid waste. “Mosaic Batik” is one of the innovations of solid waste utilization of batik waste produced by company R&D center that gives benefit to three pillars of sustainability, that is financial benefit, environmental benefit, and social benefit. The sustainability of SMEs Batik Bogor cannot be separated from the support of Bogor City Government which proactively facilitates the promotion of sustainable innovation produced by SMEs Batik Bogor.
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.
Abstract: This qualitative study aims to answer the following research questions: i) What are the factors that students perceive as relevant to a) promoting and b) preventing good grades? ii) How does socio-economic status (SES) feature in those beliefs? We conducted in-depth interviews with 19 first- and second-year undergraduates of varying SES at a research-intensive university in the UK. The interviews yielded eight factors that students perceived as promoting and six perceived as preventing good grades. The findings suggested one significant difference between the beliefs of low and high SES students in that low SES students perceive themselves to be at a greater disadvantage to their peers while high SES students do not have such beliefs. This could have knock-on effects on their performance.
Abstract: A class of implicit systems is known as a more
generalized class of systems than a class of explicit systems. To
establish a control method for such a generalized class of systems, we
adopt model predictive control method which is a kind of optimal
feedback control with a performance index that has a moving
initial time and terminal time. However, model predictive control
method is inapplicable to systems whose all state variables are not
exactly known. In other words, model predictive control method is
inapplicable to systems with limited measurable states. In fact, it
is usual that the state variables of systems are measured through
outputs, hence, only limited parts of them can be used directly. It is
also usual that output signals are disturbed by process and sensor
noises. Hence, it is important to establish a state estimation method
for nonlinear implicit systems with taking the process noise and
sensor noise into consideration. To this purpose, we apply the model
predictive control method and unscented Kalman filter for solving
the optimization and estimation problems of nonlinear implicit
systems, respectively. The objective of this study is to establish a
model predictive control with unscented Kalman filter for nonlinear
implicit systems.
Abstract: Inclusive education stems from a philosophy and vision, which argues that all children should learn together at school. It is not only about treating all pupils in the same way. It is also about allowing all children to attend school without any restrictions. Ten primary school teachers in a circuit in Namibia volunteered to participate in face-to-face interviews about inclusive education. The teachers responded to three questions about their (i) understanding of inclusive education; (ii) whether inclusive education was implemented in primary schools; and (iii) whether they were able to work with learners with special needs. Findings indicated that teachers understood what inclusive education entailed; felt that inclusive education was not implemented in their primary schools, and they were unable to work with learners with special needs in their classrooms. Further, the teachers identified training and resources as important components of inclusive education. It is recommended that education authorities should perhaps verify the findings reported here as well as ensure that the concerns raised by the teachers are addressed.
Abstract: Most people see human faces in car front and back ends because of the process of pareidolia. 96 people were surveyed to see how many of them saw a face in the vehicle styling. Participants were aged 18 to 72 years. 94% of the participants saw faces in the front-end design of production models. All participants that recognized faces indicated that most styles showed some degree of an angry expression. It was found that women were more likely to see faces in inanimate objects. However, with respect to whether women were more likely to perceive anger in the vehicle design, the results need further clarification. Survey responses were correlated to the design features of vehicles to determine what cues the respondents were likely looking at when responding. Whether the features looked anthropomorphic was key to anger perception. Features such as the headlights which could represent eyes and the air intake that could represent a mouth had high correlations to trends in scores. Results are compared among models, makers, by groupings of body styles classifications for the top 12 brands sold in the US, and by year for the top 20 models sold in the US in 2016. All of the top models sold increased in perception of an angry expression over the last 20 years or since the model was introduced, but the relative change varied by body style grouping.