Abstract: Human activity recognition based on the computer vision is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here we propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. We contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that our method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

Abstract: The most frequent challenge faced by mobile user is stay connected with online data, while disconnected or poorly connected store the replica of critical data. Nomadic users require replication to store copies of critical data on their mobile machines. Existing replication services do not provide all classes of mobile users with the capabilities they require, which include: the ability for direct synchronization between any two replicas, support for large numbers of replicas, and detailed control over what files reside on their local (mobile) replica. Existing peer-to- peer solutions would enable direct communication, but suffers from dramatic scaling problems in the number of replicas, limiting the number of overall users and impacting performance. Roam is a replication system designed to satisfy the requirements of the mobile user. Roam is based on the Ward Model, replication architecture for mobile environments. Using the Ward Model and new distributed algorithms, Roam provides a scalable replication solution for the mobile user. We describe the motivation, design, and implementation of Roam and report its performance. Replication is extremely important in mobile environments because nomadic users require local copies of important data. Model, a new replication architecture that has been designed especially with mobility in mind, though of course it applies equally well to stationary environments (1). The Ward Model provides a new replication paradigm that is neither strictly peer nor client-server; rather, it is a hybrid model of the two that allows everyone to be effectively a peer while maintaining good scalability in the number of replicas. Additionally, Roam provides a number of new distributed algorithms. For instance, Roam contains new and improved distributed algorithms for garbage collection and for dynamic management of the version vector, the main data structure behind most optimistic replication systems. In this paper we present an approach for data replication that considers the mobility of clients: The data sets which have to be replicated to mobile clients depend often on dynamic parameters like location of the client or time. Therefore, we introduce a generic model for specifying fragments of the global database with respect to such parameters. The increasing availability of mobile devices and wireless

Abstract: Topic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy.

Abstract: To this day, digital object reconstruction is a quite complex area that requires many techniques and novel approaches, in which high-resolution 3D objects present one of the biggest challenges. There are mainly two different methods that can be used to reconstruct high resolution objects and images: passive methods and active methods. This methods depend on the type of information available as input for modeling 3D objects. The passive methods use information contained in the images and the active methods make use of controlled light sources, such as lasers. The reconstruction of 3D objects is quite complex and there is no unique solution- The use of specific methodologies for the reconstruction of certain objects it's also very common, such as human faces, molecular structures, etc. This paper proposes a novel hybrid methodology, composed by 10 phases that combine active and passive methods, using images and a laser in order to supplement the missing information and obtain better results in the 3D object reconstruction. Finally, the proposed methodology proved its efficiency in two complex topological complex objects

Abstract: This work proposes a platform for estimating personality and happiness. Starting from Eysenck's theory about human's personality, authors seek to provide a platform for collecting text messages from social media (Whatsapp), and classifying them into different personality categories. Although there is not a clear link between personality features and happiness, some correlations between them could be found in the future. In this work, we describe the platform developed, and as a proof of concept, we have used different sources of messages to see if common machine learning algorithms can be used for classifying different personality features and happiness.

Abstract: Mobile ad hoc network (MANET) is now days become very famous due to their fixed infrastructure-less quality and dynamic nature. They contain a large number of nodes which are connected and communicated to each other in wireless nature. Mobile ad hoc network is a wireless technology that contains high mobility of nodes and does not depend on the background administrator for central authority, because they do not contain any infrastructure. Nodes of the MANET use radio wave for communication and having limited resources and limited computational power. The Topology of this network is changing very frequently because they are distributed in nature and self- configurable. Due to its wireless nature and lack of any central authority in the background, Mobile ad hoc networks are always vulnerable to some security issues and performance issues. The security imposes a huge impact on the performance of any network. Some of the security issues are black hole attack, flooding, wormhole attack etc. In this paper, we will discuss issues regarding low performance of Watchdog protocol used in the MANET and proposed an improved Watchdog mechanism, which is called by I-Watchdog protocol that overcomes the limitations of Watchdog protocol and gives high performance in terms of throughput, delay.

Abstract: Learning is considered as a social activity, a student does not learn only of the teacher and the textbook or only in the classroom, learn also from many other agents related to the media, peers and society in general. And since the explosion of the Internet, the information is within the reach of everyone, is there where the main area of opportunity in new technologies applied to education, as well as taking advantage of recent socialization trends that can be leveraged to improve not only informing of their daily practices, but rather as a tool that explore different branches of education research. One can foresee the future of higher education as a social learning environment, open and collaborative, where people construct knowledge in interaction with others, in a comprehensive manner. The mobility and ubiquity that provide mobile devices enable the connection from anywhere and at any time. In modern educational environments can be expected to facilitate mobile devices in the classroom expansion in digital environments, so that students and teachers can build the teaching-learning process collectively, this partial derivative results in the development of draft research approved by the CONADI in "Universidad Cooperativa de Colombia", "Social Networks: A teaching strategy in learning environments in higher education."

Abstract: Abtract — In the Persian language, an Ezafe construction is a linking element which joins the head of a phrase to its modifiers. The Ezafe in its simplest form is pronounced as –e, but generally not indicated in writing. Determining the position of an Ezafe is advantageous for disambiguating the boundary of the syntactic phrases which is a fundamental task in most natural language processing applications. This paper introduces a framework for combining genetic algorithms with rule-based models that brings the advantages of both approaches and overcomes their problems. This framework was used for recognizing the position of Ezafe constructions in Persian written texts. At the first stage, the rulebased model was applied to tag some tokens of an input sentence. Then, in the second stage, the search capabilities of the genetic algorithm were used to assign the Ezafe tag to untagged tokens using the previously captured training information. The proposed framework was evaluated on Peykareh corpus and it achieved 95.26 percent accuracy. Test results show that this proposed approach outperformed other approaches for recognizing the position of Ezafe constructions.

Abstract: This work presents the integration of an automatic assessment system for virtual/remote laboratories and the institutional Learning Management System (LMS), in order to analyze the students� progress and their collaborative learning in virtual/remote laboratories. As a result of this integration, it is feasible to extract useful information for the characterization of the students� learning process and detecting the students� engagement with the practical activities of our subjects. From this integration, a dashboard has been created to graphically present to lecturers the analyzed results. Thanks to this, faculty can use the analyzed information in order to guide the learning/teaching process of each student. As an example, a subject focused on the configuration of network services has been chosen to implement our proposal.

Abstract: The internet provides a wide range of scientific information for different areas of research, used by the related scientific communities. Often the design or architecture of these web pages does not correspond to the mental model of their users. As a result the wanted information is difficult to find. Methods established by Usability Engineering and User Experience can help to increase the appeal of scientific internet information services by analyzing the users� requirements. This paper describes a procedure to analyze and optimize scientific internet information services that can be accomplished with relatively low effort. It consists of a combination of methods that already have been successfully applied to practice: Personas, usability inspections, Online Questionnaire, Kano model and Web Analytics.

Abstract: This paper argues that a person's happiness must be understood as a phenomenon that emerges not only from her individual condition but also from her place in society. Understanding that a person is socially immersed implies giving a greater role to social interactions and social structure. The paper presents a simple model to take into consideration the role of human relations. An agent-based model (ABM) is used to illustrate the implementation of the model in understanding people's happiness.

Abstract: This research paper can be classified as pertaining to the group of empirical studies that try to measure subjective well- being. The article presents as its greatest contributions the use of a subjective measurement of well-being based on social networks for the Latin American setting, as well as its comparative analysis with another traditional method.

Abstract: Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning), and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks.

Abstract: Emerging online educational communities provide spaces for teachers to find resources, create instructional activities, and share these activities with others. Within these online communities, individual users' activities may vary widely, and thus different user types can be identified. In addition, users' patterns of activities in online communities are dynamic, and further can be affected by dissemination activities. Through analyzing usage analytics in an online teacher community called the Instructional Architect, this study explores the influences of dissemination activities on the usage patterns of different user types. Results show that dissemination activities can play an important role in encouraging users' active participation, while the absence of dissemination activities can further increase participation inequality.

Abstract: Multi-sensor information fusion is a rapidly developing research area which forms the backbone of numerous essential technologies such as intelligent robotic control, sensor networks, video and image processing and many more. In this paper, we have developed a novel technique to analyze and correlate human emotions expressed in voice tone & facial expression. Audio and video streams captured to populate audio and video bimodal data sets to sense the expressed emotions in voice tone and facial expression respectively. An energy based mapping is being done to overcome the inherent heterogeneity of the recorded bi-modal signal. The fusion process uses sampled and mapped energy signal of both modalities�s data stream and further recognize the overall emotional component using Support Vector Machine (SVM) classifier with the accuracy 93.06%.

Abstract: The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and selfdescriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour.

Abstract: This paper presents our work in the field of Intelligent Tutoring System (ITS), in fact there is still the problem of knowing how to ensure an individualized and continuous learners follow-up during learning process, indeed among the numerous methods proposed, very few systems concentrate on a real time learners follow-up. Our work in this field develops the design and implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learner's traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces on the machine. The traces are stored in database, this operation enriches collective past experiences. The traces left by the learner during the learning session evolve dynamically over time; the case-based reasoning must take into account this evolution in an incremental way. In other words, we do not consider each evolution of the traces as a new target, so the use of classical cycle Case Based reasoning in this case is insufficient and inadequate. In order to solve this problem, we propose a dynamic retrieving method based on a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). Through monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject. To help and guide the learner, the system is equipped with combined virtual and human tutors.

Abstract: This paper analyzes the viability of the use of employees smartphones as a valid tool for companies in order to conduct presence control. A Mobile Location Aware Information System is also proposed for a non intrusive Presence Control using exclusively terminal-based reactive location technologies, meeting cost minimization, and universal access criteria. The focus is providing trust to the employees, so that they feel safe and in control of when the location data is gathered while satisfying the control needs of the employer. LAMS platform is a state-of- the-art framework for synchronous mobile location-aware content personalization, using A-GPS terminal-based/network assisted mobile positioning techniques and UAProf data processing at the origin server.

Abstract: The flexible Job-shop Scheduling Problem (fJSP) considers the execution of jobs by a set of candidate resources while satisfying time and technological constraints. This work, that follows the hierarchical architecture, is based on an algorithm where each objective (resource allocation, start-time assignment) is solved by a genetic algorithm (GA) that optimizes a particular fitness function, and enhances the results by the execution of a set of heuristics that evaluate and repair each scheduling constraint on each operation. The aim of this work is to analyze the impact of some algorithmic features of the overlap constraint heuristics, in order to achieve the objectives at a highest degree. To demonstrate the efficiency of this approach, experimentation has been performed and compared with similar cases, tuning the GA parameters correctly.

Abstract: We introduce a hybrid system composed of a convolutional neural network and a discrete graphical model for image recognition. This system improves upon traditional sliding window techniques for analysis of an image larger than the training data by effectively processing the full input scene through the neural network in less time. The final result is then inferred from the neural network output through energy minimization to reach a more precize localization than what traditional maximum value class comparisons yield. These results are apt for applying this process in a mobile device for real time image recognition.

Abstract:   Abstract — Every job demands an employee with some specific qualities in addition to the basic educational qualification For example, an introvert person cannot be a good leader despite of a very good academic qualification Thinking and logical ability is required for a person to be a successful software engineer So, the aim of this paper is to present a novel approach for advising an ideal job to the job seeker while considering his personality trait and educational qualification both Very well-known theories of personality like MBTI indicator and OCEAN theory, are used for personality mining For education mining, score based system is used The score based system captures the information from attributes like most scoring subject, dream job etc After personality mining, the resultant values are coalesced with the information extracted from education mining And finally, the most suited jobs, in terms of personality and educational qualification are recommended to the job seekers The experiment is conducted on the students who have earned an engineering degree in the field of computer science, information technology and electronics Nevertheless, the same architecture can easily be extended to other educational degrees also To the best of the author's knowledge, this is a first e-job advisory system that recommends the job best suited as per one's personality using MBTI and OCEAN theory both

Abstract: Multi-agent systems are now wide spread in scientific works and in industrial applications. Few applications deal with the Human/Multi-agent system interaction. Multi-agent systems are characterized by individual entities, called agents, in interaction with each other and with their environment. Multi- agent systems are generally classified into complex systems categories since the global emerging phenomenon cannot be predicted even if every component is well known. The systems developed in this paper are named reactive because they behave using simple interaction models. In the reactive approach, the issue of Human/system interaction is hard to cope with and is scarcely exposed in literature. This paper presents Sphericall, an application aimed at studying Human/Complex System interactions and based on two physics inspired multi-agent systems interacting together. The Sphericall device is composed of a tactile screen and a spherical world where agents evolve. This paper presents both the technical background of Sphericall project and a feedback taken from the demonstration performed during OFFF Festival in La Villette (Paris).

Abstract: Internet security finds it difficult to keep the information secure and to maintain the integrity of the data. Sending messages over the internet secretly is one of the major tasks as it is widely used for passing the message. In order to achieve security there must be some mechanism to protect the data against unauthorized access. A lossless data hiding scheme is proposed in this paper which has a higher embedding capacity than other schemes. Unlike other schemes that are used for embedding fixed amount of data, the proposed data hiding method is block based approach and it uses a variable data embedding in different blocks which reduces the chances of distortion and increases the hiding capacity of the image. When the data is recovered the original image can be restored without any distortion. Our experimental results indicate that the proposed solution can significantly support the data hiding problem. We achieved good Peak signal-to-noise ratio (PSNR) while hiding large amount of data into smoother regions.

Abstract: This paper presents a scene composition approach that allows the combinational use of standard three dimensional objects, called models, in order to create X3D scenes. The module is an integral part of a broader design aiming to construct large scale online advertising infrastructures that rely on virtual reality technologies. The architecture addresses a number of problems regarding remote rendering for low end devices and last but not least, the provision of scene composition and integration. Since viewers do not keep information regarding individual input models or scenes, composition requires the consideration of mechanisms that add state to viewing technologies. In terms of this work we extended a well-known, open source X3D authoring tool.

Abstract: This paper shows the implementation of mutual exclusion in PCBSD-FreeBSD operating systems on SMPng environments, providing solutions to problems like investment priority, priority propagation, interlock, CPU downtime, deadlocks, between other. Mutex Control concept is introduced as a solution to these problems through the integration of the scheduling algorithm of multiple queues fed back and mutexes.

Abstract: This paper describes the implementation of a prototype REST service for triangulation of point sets collected by mobile GPS receivers. The first objective of this paper is to test functionalities of an application, which exploits mobile devices' capabilities to get data associated with their spatial location. A triangulation of a set of points provides a mechanism through which it is possible to produce an accurate representation of spatial data. Such triangulation may be used for representing surfaces by Triangulated Irregular Networks (TINs), and for decomposing complex two-dimensional spatial objects into simpler geometries. The second objective of this paper is to promote the use of oriented matroids for finding alternative solutions to spatial data processing and analysis tasks. This study focused on the particular case of the calculation of triangulations based on oriented matroids. The prototype described in this paper used a wrapper to integrate and expose several tools previously implemented in C++.

Abstract: Road traffic is a problem which is increasing in cities with large population. Unrelated to this fact the number of portable and wearable devices has also been increasing throughout the population of most countries. With this advent, the capacity to monitor and register data about people habits and locations as well as more complex data such as intensity and strength of movements has created an opportunity to contribute to the general wealth and comfort within these environments. Ambient Intelligence and Intelligent Decision Making processes can benefit from the knowledge gathered by these devices to improve decisions on everyday tasks such as deciding navigation routes by car, bicycle or other means of transportation and avoiding route perils. The concept of computational sustainability may also be applied to this problem. Current applications in this area demonstrate the usefulness of real time system that inform the user of certain conditions in the surrounding area. On the other hand, the approach presented in this work aims to describe models and approaches to automatically identify current states of traffic inside cities and use methods from computer science to improve overall comfort and the sustainability of road traffic both with the user and the environment in mind. Such objective is delivered by analyzing real time contributions from those mobile ubiquitous devices to identifying problematic situations and areas under a defined criteria that have significant influence towards a sustainable use of the road transport infrastructure.

Abstract: The Economics of Happiness is one of the research areas of greatest growth in recent years. Throughout this work, a venture based model in which satisfaction of Spanish entrepreneurs with their professional life is performed. We analyze the responses of 9,989 entrepreneurs using data from the Global Entrepreneurship Monitor (GEM), and six hypothesis are discussed. The results show that, for the Spanish case, there is a strong consistency in the results the opportunity entrepreneurs present greater satisfaction than necessity entrepreneurs.

Abstract: Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below.