A smart parking system using Raspberry Pi and android app access is proposed in this article. An automated smart parking system which provides the user to pre-book along with on the spot booking facility of parking spaces available around the area is implemented. The system also helps to store the user’s information in database which enables for fast parking access thus reducing the traffic congestion. The hardware is realized using Raspberry Pi and IR sensor. In addition, the system is controlled through an app which is made using Kodular App Development tool, that helps the user to keep track of the updates about available parking spaces. The scalable and precise database can be created on firebase and host it on an application in real-time which is useful to store data in a json format (bucket format). The system is a perfect partner for people to book the parking spaces at their ease from remote locations without wasting time and avoiding traffic congestion.

We present a freely available Russian language sentiment lexicon PolSentiLex designed to detect sentiment in user-generated content related to social and political issues. The lexicon was generated from a database of posts and comments of the top 2,000 LiveJournal bloggers posted during one year (∼1.5 million posts and 20 million comments). Following a topic modeling approach, we extracted 85,898 documents that were used to retrieve domain-specific terms. This term list was then merged with several external sources. Together, they formed a lexicon (16,399 units) marked-up using a crowdsourcing strategy. A sample of Russian native speakers (n = 105) was asked to assess words’ sentiment given the context of their use (randomly paired) as well as the prevailing sentiment of the respective texts. In total, we received 59,208 complete annotations for both texts and words. Several versions of the markedup lexicon were experimented with, and the final version was tested for quality against the only other freely available Russian language lexicon and against three machine learning algorithms. All experiments were run on two different collections. They have shown that, in terms of Fmacro, lexicon-based approaches outperform machine learning by 11%, and our lexicon outperforms the alternative one by 11% on the first collection, and by 7% on the negative scale of the second collection while showing similar quality on the positive scale and being three times smaller. Our lexicon also outperforms or is similar to the best existing sentiment analysis results for other types of Russian-language texts.

LEACH model was proposed 19 years back and research community from the field of WSN has been exploring LEACH variants. To discover unseen areas in the field of WSN it is a great idea to study approved variants of LEACH through the time. Present paper studies the popular and approved versions of LEACH. The study categorizes all the variants in single and multi-hop communication mode, depends upon packets transmitted from the CH and the BS. The paper makes a comparative analysis based on parameters like cluster formation, complexity, energy efficiency, overhead, and scalability. Advantages and disadvantages of all the variants are discussed. Finally, the paper suggests upcoming research in the field of WSN.

Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions. Due to energy and bandwidth constraints, wireless sensors are prone to packet loss during communication. To overcome the physical constraints ofWSNs, there is an extensive renewed interest in applying data-driven machine learningmethods. In this paper, we present amission-critical surveillance systemmodel for industrial environments. In our proposed system, a decision tree algorithm is installed on a centralized server to predict the wireless channel quality of the wireless sensors. Based on the machine-learning algorithm directives, wireless sensor nodes can proactively adapt their duty cycle to mobility, interference and hidden terminal. Extensive simulation results validate our proposed system. The prediction algorithm shows a classification accuracy exceeding 73%, which allows the duty cycle adaptation algorithm to significantly minimize the delay and energy cost compared to using pure TDMA or CSMA/CA protocols.

Remote health monitoring for patients is increasing with the advancements of various types of health related mobile applications. The vital signs such as pulse rate, blood pressure and temperature are the basic parameters, used for monitoring a patient’s health. The designed healthcare kit consists of different sensors that are used for sensing and monitoring a patient’s health. The data from healthcare kit is transmitted via the internet to save in a cloud-based server, which helps to monitor the consistent health situation of a candidate. The sensing information will be collected constantly over specified intervals of time and will be used to aware the patient about any concealed problem to endure possible diagnosis. The higher and lower range of temperature, blood pressure and heartbeat can be defined by the doctor according to the patient’s health. Afterward, the system starts monitoring the patient and sends alert to the concerned doctor as soon as the sensing parameters cross the defined limits. The recorded values are transmitted via internet to the cloud server and a notification in the form of a tweet is also displayed on the doctor’s twitter account and as well as the patient’s account.

This paper presents the results from a comprehensive field study on BIM adoption and AEC integration targeting the Turkish AEC firms. The research objectives were to develop well-reasoned arguments supporting the role of BIM in the transformation of current business models in the Turkish AEC Industry as well as identifying the inclinations, challenges, and obstacles to BIM adoption. With in-depth interviews with industry representatives, the study returned valuable information that lead to context-dependent insights about the local AEC industry and influence of BIM adoption on all AEC operations. The findings from the study suggest that the existence of a rapidly growing interest in BIM-enabled processes and services due to local and global market dynamics and pressure. The influence of established business culture within the industry drastically affect perceptions towards the value propositions of BIM and IPD which are broadly known among the Turkish AEC firms which are different from the US and European counterparts. Results from this study motivate new discussions about BIM adoption and IPD from the perspective of the Turkish AEC Industry, and also provide arguments about the potential impacts of BIM deployment for the local and global construction projects which are undertaken by the Turkish AEC firms.

This paper focuses on the cognitive competencies enabling front-line employees to grasp context, which previous has not taken into account. To this end, we developed two studies. Study 1 identifies the key factors and models the cognition during service based on interview data from flight attendants. Interview data was analyzed by Grounded Theory Approach. In study 2, the cognition of 155 flight attendants was obtained and features representing cognitive competency were quantitatively identified. Our findings propose the hospitality concept of “serving not to serve” from a cognitive perspective, for which risk perception and thoughtfulness are important cognitive competencies.

The behavior of Polish residents changes from year to year. Currently, residents are moving from the city center beyond its borders, to smaller towns or to the countryside. In order to learn about their current mobility trends, the article presents the results of mobility studies of the inhabitants of Poland. A surveywas conductedwhere theywere asked, among others o:main and secondary destination of their journey,way of reaching the destination, distance to destination andduration of the journey.These studieswere carried out on a groupof inhabitants including: age, sex, place of residence and professional status.

In this article we discuss how users of Mobile Instant Messaging (MMI) (WhatsApp) applications collaborate in environments that promote knowledge networks. For this study the researcher analyzed the interaction of users added to a group in WhatsApp, which is based on concepts of connectivism, communication, collaborative learning and knowledge creation. This study deals with the granting of knowledge on a specific computer subject and its transmission by all the members of the group, facilitating the work for the instructor and increasing the users’ compression capacity. Each user can become an instructor for the new members of the group. Strengthening collaborative learning processes. Part of the study is the use of the questionnaire tool that collected information from group members, reception capacity, age and difficulties with MMI.

Brain functional networks are essential for understanding the functional connectome. Computing the temporal dependencies between the regions of brain activities from the functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining.

Multi-task learning has shown promising results in many applications of machine learning: given several related tasks, it aims to generalize better on the original tasks, by leveraging the knowledge among tasks. The knowledge transfer mainly depends on task relationships. Most of existing multi-task learning methods guide learning processes based on predefined task relationships. However, the associated relationships have not been fully exploited in these methods. Replacing predefined task relationships with the adaptively learned ones may lead to superior performance as it can avoid the misguiding of improper predefinition. Therefore, in this paper, we propose Task Relation Attention Networks to adaptively model the task relationships and dynamically control the positive and negative knowledge transfer for different samples in multi-task learning. To evaluate the effectiveness of the proposed method, experiments on various datasets are conducted. The experimental results demonstrate that the proposed method outperforms both classical and state-of-the-art multi-task learning baselines.

Real-time implementation and robustness against illumination variation are two essential issues for traffic congestion classification systems, which are still challenging issues. This paper proposes an efficient automated system for traffic congestion classification based on compact image representation and deep residual networks. Specifically, the proposed system comprises three steps: video dynamics extraction, feature extraction, and classification. In the first step, we propose two approaches for modeling the dynamics of each video and produce a compact representation. In the first approach, we aggregate the optical flow in front direction, while in the second approach, we use a temporal pooling method to generate a dynamic image describing the input video. In the second step, we use a deep residual neural network to extract texture features from the compact representation of each video. In the third step, we build a classification model to discriminate between the classes of traffic congestion (low, medium, or high). We use the UCSD and NU1 traffic congestion datasets to assess the performance of the proposed method. The two datasets contain different illumination and shadow variations. The proposed method gives excellent results compared to state-of-theart methods. It also can classify the input video in a short time (37 fps), and thus, we can use it with real-time applications.

As the population is increasing rapidly day by day the pollution level is also increasing significantly. Several campaigns like Swachh Bharat Abhiyaan (SBA) are aiming to reduce the pollution level. Our approach is to use computer vision technique to classify the garbage based on its severity. For this we have rated garbage on a scale of 1 to 5 with 5 as cleanest and 1 as the dirtiest. To achieve our aim, we have used Faster-RCNN Inception v2 model, and have procured an accuracy of 89.14% using SVM and 89.68% using CNN in detecting different classes of garbage.

Social Media have been recognised as supportive tools in education for creating benefits that supplement students’ collaboration, class interactions, as well as communication between instructors and students. Active informal interaction and feedback between instructors and students outside class belong to the main reasons behind social media pedagogy. Despite the prevalence of traditional email methods of providing feedback to students, the literature shows that they do not check their emails as frequently as they check their social media accounts. In this paper we present the automatic generation of feedback messages and tweets with context-free grammars (CFG). Our system takes a class list of students and their mark sheets and automatically composes Twitter tweets concerning statistical ‘fun facts’ about programming problems, exercises, class performances, as well as private messages about individual student performances. A survey with 116 participating students showed that the majority of them would like to receive such notifications on social media rather than emails. Lecturers found our system promising, too.

Artificial intelligence (AI) is the future of computer technologies and at present it has achieved great progress in different areas. In this paper, we study the general AI problem from a standardization point of view, and introduce a semantic interoperability for intelligent computer systems. We propose a standard for the interior semantic representation of knowledge in the memory of an intelligent computer system, which is called the SC-code (Semantic Code). Integration of various types of knowledge is performed due to hybrid knowledge base models. This model includes a hierarchical set of top-level ontologies that provide semantic compatibility of various types of knowledge and permits to integrate facts, specifications of various objects, logical statements, events, situations, programs and algorithms, processes, problem formulations, domain models, ontologies, and so on. A variant of the presentation of the AI standard based on the semantic representation of knowledge, in the form of a part of the knowledge base of the Intelligent Computer Metasystem (IMS.ostis) is proposed.

In this paper, we present a novel type of location-based queries, named the min cost queries (or MCQ for short). Given the n types of spatial objects, O1, O2, ..., On, where Oi has a cost attribute, and a user-defined distanced. The MCQ finds a set of n objects, {o1, o2, ..., on}, such that the distance between any pair of objects in {o1, o2,..., on} does not exceed d and the total cost of {o1, o2,..., on} is smallest. To efficiently process the MCQ, we design a R-tree-based index, the Rtree, to man-age the spatial objects with their locations and costs. Then, we develop a top k-based MCQ algorithm combined with the R-tree to retrieve the MCQ result.

Choice of initial centroids has a major impact on the performance and accuracy of k-means algorithm to group the data objects into various clusters. In basic k-means, pure arbitrary choice of initial centroids lead to construction of different clusters in every run and consequently affects the performance and accuracy of it. To date, several attempts have been made by the researchers to increase the performance and accuracy of it. However, scope of improvement still exists in this area. Therefore, a new approach to initialize centroids for k-means is proposed in this paper on the basis of the concept to choose the well separated data-objects as initial cluster centroids instead of pure arbitrary selection. As a consequence, it leads to higher probability of closeness of the chosen centroids to the final cluster centroids. The proposed algorithm is empirically assessed on 6 different well-known datasets. The results confirms that the proposed approach is considerably better than the pure arbitrary selection of centroids.

In this talk, we will present a few optimization principles that have been shown to be useful to address large-scale problems in machine learning. We will focus on recent variants of the stochastic gradient descent method that benefit from several acceleration mechanisms such as variance reduction and Nesterov’s extrapolation. We will discuss both theoretical results in terms of complexity analysis, and practical deployment of these approaches, demonstrating that even though Nesterov’s acceleration method is almost 40 years old, it is still highly relevant today.

In this paper, we present results of a contextual inquiry study in a Community-Based Tourism (CBT) village. We investigate the influence of an enabling digital service platform for tourism that rural Tanzanians utilise to attract and host guests. Our interviews and observations show that hosting tourists delivers positive short-term livelihood outcomes (income, visitors), but the long-term impact (social, infrastructure) to the communities requires deeper consideration. We recommend that sustainable digital service platforms for CBT should be developed and assessed including their features in addressing long-term impacts on livelihood. We propose the following topics for consideration in future development of digital CBT platforms: the role of surrounding communities, rewards to platform contributors, enabling of indirect economic activities, understanding conflict-of-interest between communities and platform, empowerment all of the users, and monitoring the local performance of the platform for its users.

China’s scenic spots are facing the dual pressure of increasing the number of tourists and the decline in the quality of viewing. How to effectively coordinate the contradiction between the development of scenic spots and the quality of viewing, and realize the maximization of resources within the scenic spots and the improvement of visual senses have become common problems faced by the academic and tourism development departments. In the past few decades, the planning methods of scenic spots and the selection methods of scenic areas have been more traditional. It is impossible to accurately simulate and judge when dealing with complex topographical conditions such as mountains, which has a subjective impact on the exploration and development of scenic landscape resources. This method has caused problems such as narrow viewing horizons, incomplete coverage of the tour route, and occlusion of the line of sight. Practice has proved that the traditional scenic area planning theory based on qualitative and the analysis method based on field research are one of the main sources of the above problems, especially in mountainous scenic areas. With the development of social economy, the selection of traditional scenic spots and the planning of scenic spots urgently need a change to adapt to the new development and new requirements of the current viewing experience. This study used the GIS viewshed analysis function and the analytic hierarchy process to identify blind spots in the scenic area by simulating the visibility of different viewing distance ranges of the current scenic spots and tour lines, and then combined with the questionnaire survey results of tourists, planners, scenic spot managers and college landscape teachers, the analytic hierarchy process is used to determine the evaluation system of new viewpoints. Finally, according to the evaluation factor, the weighted superposition in the GIS is used to determine the newly added viewpoints, so as to improve the quality and effective utilization of the scenic spots in the mountain-type scenic areas. The key to the GIS landscape viewshed analysis is that the scenic spots and tour lines planning of the scenic area must be based on objective geographical factors and current resources, and priority is given to the analysis of the current situation quality. Then, according to the distribution of the current visual blind zone, new viewpoints and tour lines will be carried out. Compared with developed countries like the WEST, which have relatively perfect geographic information platforms, China’s geographic information data acquisition requires authorization frommultiple departments, and the © Springer Nature Singapore Pte Ltd. 2020 Y. Xie et al. (Eds.): GSES 2019/GeoAI 2019, CCIS 1228, pp. 3–18, 2020. https://doi.org/10.1007/978-981-15-6106-1_1 data quality is uneven. In response to this problem, this paper used python combined with Amap to obtain the vector boundary of the study area, and imported it into the rivermap downloader X3 to download DEM data, reducing the difficulty of data acquisition. This method can provide data security for the study area simulation and provide an important scientific basis for landscape viewshed analysis. Taking Baiyun mountain scenic area as an example, this paper comprehensively utilizes GIS technology to identify the viewing space of the above process through simulation and analysis of elevation, slope, slope direction and view area analysis, and constructs the best viewing pattern. Moreover, the whole analysis process is taken as the framework to enrich the evaluation system of mountain scenic spots. The results shown that the current viewing area of the 26 scenic spots in the near-view, mid-view and distant-view analysis results are 6.09%, 11.89% and 26.62%, respectively, and the overall view is poor, and the open field is mainly concentrated in the south-central area of the study area, the northeast and southwest sides have narrower views, mainly due to the lesser distribution of viewpoints. The near, middle and distant view along the four main tour lines may be better, but the central area and the northwest side are not transparent due to the topography and geomorphology. On the other hand, due to the lack of travel coverage on the north side of the study area, there is a large area of visual blind spots in the north. In order to solve the above problems, this paper will re-classify the evaluation system of the site selection and the elevation, slope and aspect of the study area. Under the principle of low cost and ecological protection, the site selection of newly-built scenic spots was obtained, and then fine-tuned according to the site investigation, and the location of newly-built scenic spots was finally determined. This method can effectively enhance the quality of the selected viewpoints in the planning process and reduce subjective errors in the case of high data quality. Moreover, this method also provides an essential reference for the analysis of geographical elements in the early stage of mountain-type scenic areas. We should transform from traditional subjective planning ideas into objective planning thinking based on data analysis, respect objective reality and maximize the exploitation of available resources to provide reference for the sustainable development of scenic spots.