Reviewerly/exHarmony huggingface dataset:
Active Incremental Learning of a Contextual Skill Model

Contextual skill models are learned to provide skills over a range of task parameters, often using regression across optimal task-specific policies. However, the sequential nature of the learning process is usually neglected. In this paper, we propose to use active incremental learning by selecting a task which maximizes performance improvement over entire task set. The proposed framework exploits knowledge of individual tasks accumulated in a database and shares it among the tasks using a contextual skill model. The framework is agnostic to the type of policy representation, skill model, and policy search. We evaluated the skill improvement rate in two tasks, ball-in-a-cup and basketball. In both, active selection of tasks lead to a consistent improvement in skill performance over a baseline.
A Novel Image Fusion Scheme for FTV View Synthesis Based on Layered Depth Scene Representation &amp; Scale Periodic Transform

This paper presents a novel image fusion scheme for view synthesis based on a layered depth profile of the scene and scale periodic transform. To create a layered depth profile of the scene, we utilize the unique properties of scale transform considering the problem of depth map computation from reference images as a certain shift-variant problem. The problem of depth computation is solved without deterministic stereo correspondences or rather than representing image signals in terms of shifts. Instead, we pose the problem of image signals being representable as scale periodic function, and compute appropriate depth estimates determining the scalings of a basis function. The rendering process is formulated as a novel image fusion in which the textures of all probable matching points are adaptively determined, leveraging implicitly the geometric information. The results demonstrate superiority of the proposed approach in suppressing geometric, blurring or flicker artifacts in rendered wide-baseline virtual videos.
Application of Artificial Neural Network in Cryptography

In today's scenario where everything is just a click away, there is a huge concern for Intelligence Security. In this work, artificial neural network(ANN) has been explored to prevent any unaccredited access of data. Several kinds of cryptographic techniques are already in use to handle this problem. However, each of these techniques have their own advantages and limitations. Some techniques are more secure whereas some are less. In this work ANN is investigated in data security application by analyzing different types of semantic nets along with their architectures and compared. Further, scrutiny is done on the footing of their training algorithms by listing down their merits/demerits. A recurrent network is used to carry out the implementation of a finite state sequential machine which is used for cryptography purpose. Based on the analysis of obtained results, it is concluded that Artificial Neural Network can be effectively utilized as a contemporary technique to encrypt and decrypt a data.
Visual Design and Code Generation of User Interface Based on Responsive Web Design Approach

At present, web users may access web sites from various computing devices and the ability to access the web sites on all screen sizes is very important. Responsive web design is an approach that makes web contents render well on desktop, notebook, tablet, and smartphone platforms by adapting to different screen sizes. To facilitate web developers, this paper proposes a visual user interface design and front-end code generation tool for web sites based on responsive web design principles that were compiled from different sources. The principles cover the design of different web elements including navigation, image, layout, form, and text. The proposed responsive web design tool lets any web developer create a visual design of the web UI for the smartphone platform first, and then the tool will automatically create the visual designs for other platforms as well as generate front-end code for all platforms. In an evaluation by experts who are experienced web-based software engineers, all experts strongly agreed that the web UI designed by the tool followed the responsive web design principles correctly. In addition, the experts used a cognitive walkthrough method to evaluate usability of the UI of a responsive web site that was designed by the tool. The experts also agreed that usability of the web UI that was designed by the tool was still attained on all screen sizes.
RankSVM for Offline Signature Verification

Signature verification systems suffer from imbalanced learning, which imposes strict requirements on classifiers. The standard classification approaches, such as SVM, often degrade the performance for imbalanced data or require additional parameters for data balancing. In this study, as a new approach for signature verification, we use RankSVM as the writer-dependent classifiers, which theoretically guarantees the generalization performance for imbalanced data. To investigate the ability of RankSVM for solving imbalanced learning problems in signature verification tasks, the extensive experiments are conducted on bitmaps of GPDS-150, GPDS-300, GPDS-600, and GPDS-1000 datasets and deep features of GPDS-960 dataset. The experimental results demonstrate that the RankSVM-based approach obtains a nearly equivalent performance with the state-of-the-art method on deep features of the GPDS-960 dataset, and achieves significantly better performance than standard-SVM-based approach on bitmaps of GPDS-150, GPDS-300, GPDS-600, and GPDS-1000 datasets.
Hand Gesture Recognition based on SVM Classification

The paper proposes a hand gesture recognition system based on support vector machine classification. The analysis was performed on two publicly available databases and one acquired in our laboratory. Methods based on computer vision provide improved results for the analysis of the databases in the literature. The analysis of the database acquired in our laboratory also provides very good classification precision of 95.7% when using HOG descriptors. This database has a higher degree of complexity due to the image size, the representation in the RGB color space, the presence of artifacts and the number of classes analyzed.
Cognitive Approaches for Sensor Data Analysis in Transformative Computing

Recently a Transformative Computing paradigm has been defined. It is a new scientific approach, which allow to join global wireless communication with distributed sensor networks and AI. In this paper we’ll try to describe the possible extensions of such computing technologies towards application of advanced cognitive systems for data analysis, allowing semantic interpretation of context data, in which computation task is performed. This allow to define a new approach called cognitive transformative computation oriented on specific user working in particular environment.
Smart Cities at Play: Lived Experiences, Emerging Forms of Playfulness, and Problems of Participation

In recent years, the notion of smart cities has become the focus of a growing body of research. To date, much of this attention has revolved around the technical aspect, with related concerns including the creation and implementation of suitable smart city technologies. What is notably missing from these discussions, however, is a consideration of the lived experience of supposedly 'smart spaces' and the extent to which physical and digital environments are currently producing new forms of play and playfulness that can be contextualized within this field. With this in mind, the purpose of our workshop is as follows. First, to provide a platform for researchers and practitioners to engage with these issues that often remain hidden when discussions solely focus on technology. Second, to develop a draft research agenda for challenges that will serve as a primer for future studies examining the topic.
Big Data and Cloud Computing: An Emerging Perspective and Future Trends

Big Data is a collection of large amount of data which is growing very rapidly with the popularity of social networking sites. The size of the Big data has been extended from terabytes to petabytes. Big data are characterized by four important attributes: volume, velocity, variety and veracity. The volume attributes describe the data at rest in the range from terabytes to Exabyte's, the velocity deals with data in motion, i.e streaming the data to respond within milliseconds rather than in seconds, the variety discuss the data in many different forms such as structured, unstructured, text and multimedia data, whereas the veracity deals with the data in doubt, i.e. uncertainty in data due to data inconsistency. These attributes of big data make it a challenge for organizations to have control over, and use such data. In today's era we are overloaded with the information, however we are lacking the insight. The 90% of the data in the world today has been created in the last two years by various social networking sites. As big data grows it challenges the capabilities of traditional data warehouses that collect and store large amounts of internal and external data. Data drawn from these repositories are used to improve decision making, increase organizational efficiencies, and improve organizational effectiveness. In this paper an attempt has been made to explore the real life application of Big Data , cloud database, Hadoop, Map Reduce and Cloud Computing, in various domains.
Ambient Intelligence Using Smart Mirror-Personalized Smart Mirror for Home Use

A Smart Mirror is a device that looks and feels like a simple mirror but is able to actively interact with the user in real time. With advancement in technology, it has now become possible to able to do a multitude of things from face recognition to voice authentication. These days the primary focus of making new technology is also ensuring their adaptability in a variety of ways. The purpose of this paper is to develop a Smart Mirror with the capability to provide weather, time, cryptocurrency, Google calendar and a greeting from the mirror in the form of a compliment. Additionally, the mirror will also be able to provide various make up effects so that users are able to view the possible outcomes of different makeup shades in the smart mirror without affecting the real face appearance in the process. Voice commands for modules implemented in the paper will also be present in place to control the modules as needed by the user. Face Recognition for notifications of a particular user will also be present. The aim here is to provide a seamless experience to the user making everyday tasks easier.
Understanding signcryption security in standard model

Abstract Signcryption is an advanced technique that combines both signature and encryption in one step which costs lower than any ordinary signature‐then‐encryption technique. Many ID‐based signcryption ( ) techniques have been constructed, but they are considered secure in the random Oracle (RO) model, where the proofs in this environment are criticized as it always depends on the assumption that Oracle is truly random. This assumption of RO is not possible in reality. Therefore, many of the ID‐based signcryptions were proposed in the widely accepted standard model. As signcryption schemes have much important application, the verification of their correctness, security, and performance are required. In this article, we have discussed two important security features in the standard model under decision bilinear Diffie‐Hellman (DBDH) and computational Diffie‐Hellman(CDH) assumptions, namely, (a) indistinguishable against chosen cipher text attack and (b) indistinguishable against chosen‐plaintext attack. To understand the security of existing schemes, we present cryptanalysis, which illustrates weak security. The study on performance is also performed to analyze the communication and computational efficiency. The study of performance and security analysis helps us to understand the requirement of signcryption scheme in standard model.
High Dynamic Range Video Synthesis Using Superpixel-Based Illuminance-Invariant Motion Estimation

We propose a robust high dynamic range (HDR) video synthesis algorithm using the superpixel-based illuminance-invariant motion estimation technique. The proposed algorithm first selects an input frame in an alternating exposed input video as the reference. Then, the correspondences between two adjacent frames are estimated by employing a feature descriptor, which is robust against illuminance variation, and a superpixel segmentation technique. Next, the input frames are warped to the reference frame using the estimated motion maps. Finally, the final HDR frame is synthesized by constructing a weight map, which can handle complex motions and poor exposures by considering the underlying structures in the input frames. Experimental results on real test sequences show that the proposed algorithm can provide high-quality HDR videos compared with those obtained by state-of-the-art algorithms in terms of both subjective and objective evaluations.
Automated Approach to Rhythm Figures Search in English Text

Text rhythm is recognized as being one of the most important subject areas of modern linguistic studies. There is a considerable amount of literature on the analysis of rhythm in poetry and literary prose. However, few researchers have addressed the problem of using automated tools for rhythm analysis, whereas automated methods can be of great benefit to this cause, especially when the research is conducted on large text corpora. This paper presents a new automated approach to integrated search of rhythm figures in fiction including anaphora, epiphora, anadiplosis, symploce and simple repetition provided for by an original lexical tool designed within the framework of the research. The ad hoc experiments have proved this approach to be reliable and informative.
Integration of Virtual Reality and Augmented Reality for Immersive Gaming and Learning

Augmented reality and Virtual reality provide two different types of immersive gaming and learning experience, by integrating the two together we can enhance both the gaming and learning experience separately. Integration is the act of combining small components into a unified system that works as one. In terms of Information Technology, it means adding features of one technology to another. This paper proposes a new hardware which will use existing hardware called a head mounted display (HMD) with a dual camera on the front along with a various array of sensors to integrate Virtual Reality and Augmented Reality. This method of using traditional Hardware will be a more cost effective solution when compared to other Head mounted display currently available. a Virtual Reality is a 3D interactive stimulation of an environment. It requires a wearable display to work. Whereas augmented reality is a superimposed Computer-generated image on a view of actual word. Combining the two together can result is a more immersive augmented reality experience which will help in learning and an enhanced gaming experience
DNN Based Expressive Text-to-Speech with Limited Training Data

Modern text-to-speech synthesis systems should deliver speech which is not just intelligible, but whose style corresponds to the domain in which synthesized speech is used. In this paper three approaches based on deep neural networks aimed at synthesis of expressive speech are presented: style code, model re-training and an architecture using shared hidden layers. Their usability is tested on a speech corpus with a limited amount of expressive speech data. A new architecture for transplanting speech styles is also presented and compared with a referent approach from literature.
On Training Flexible Robots using Deep Reinforcement Learning

The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest in developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL), we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.
ISOCOV: a new MCDM method to handle value constraints in Web service selection

The aim of the current research is to define a new MCDM method that has the ability to rank Web services when value constraints are expressed on QoS criteria. This method is called, ISOCOV (Ideal SOlution with COnstraint on Values). In presence of values constraints, the majority of existing MCDM methods proceeds either by discarding from the research space all the non satisfactory Web services, or by providing a partial integration of the requirements in the decision making process. On the contrary, ISOCOV takes into account all the aspects thus providing to the user a more accurate solution while dealing with the given constraints. The purpose is to be able to promote services that do not meet the constraints, when the cleaned research space stands empty or not representative enough. A case study using a real dataset is considered to highlight the functioning and the accuracy of our method comparatively to other similar MCDM approaches.
A Two layer secure image encryption technique

The growing use of networking in almost every possible field, data transferring or data sharing has become an essential need. All kinds of users directly or indirectly involved in data transfer. But data transferring is susceptible to various risks i.e. hacking of potential data, tempering, etc. Various services based on data sharing or transferring across the network needs security and reliability of the system. Most of the stakeholders may not be fully aware about the fact that their communication is unsecure. Users are relying upon the system they are using and assume that their ongoing communication is secure. Generally, data categorized into text and image messages. There are many works on securing of text message through encryption has been achieved in last decades. But securing images are differs in many ways from that because text. Nowadays, securing image is necessary and quite in demand. In this paper, we have proposed an image encryption algorithm to secure image while being transmitted in two layers.
A Novel Algorithm for Analysis of a Local Shape In the 3-D Gray Image

For shape analysis in 3-D, probably skeletonization will become a momentous tool for analyzing the different shapes in 3-D, as similar to the 2-D. This paper discusses about topology preserving 3-D skeletonization sequential algorithm which computes curve skeletons for the given solid object. The original geometry of the given object will be well-preserved by curve skeleton. In the proposed algorithm, each voxel of the three-dimensional image is classified as two classes: boundary, corner. The object is scanned with the use of a structuring element, border and corner voxels are classified based on the certain condition proposed in this paper. This algorithm does not require any computations which are complex, large number of windows which make implementation a complex. The proposed algorithm is various 3-D simulated images and resulting skeletons are found to satisfactory. The 3-D simulated images and its skeletons are viewed with the help of ImageJ software.
A streaming approach to reveal crowded events from cellular data

Anomaly detection has been a very popular research topics over the last few years and applies to many scenarios from different disciplines. This research focuses on crowded phenomena, and addresses the detection of popular events by looking for “anomalous” patterns in cellular traffic data. In particular, the paper elaborates upon previous proposals and presents two streaming algorithms based on the wavelet decomposition of traffic data. The new algorithms consume traffic samples as soon as they are available, elaborate the data in real time and possibly raise alarms upon threshold crossing. The effectiveness of the approach is assessed by using the public dataset containing the real cellular data acquired over the network of the most popular Italian traffic operator. The experiments prove that the streaming algorithms generally achieve performance comparable to that of their offline counterparts, and that the small degradation that may occasionally be observed is however well counterbalanced by the obvious advantage of detecting anomalies in real-time with no need to wait for the elaboration of overly long traffic timeseries.
Rainfall analysis in the Indian Ocean by using 6-States Markov Chain Model

Abstract In this study, we used Markov chain approach to analyze rainfall dataset from one buoy (4N90E) in the eastern Equatorial Indian Ocean. This study aims to determine the opportunity for transition (displacement) of daily rainfall intensity, where there are six states or conditions of rainfall intensity, i.e. no rain, very weak, weak rain, moderate rain, heavy rain, and very heavy rain. The Markov Chain method used is the Chapman-Kolmogorov equation and the steady state equation. The investigation of the 6-states in Markov chain model show that dynamic probability of transition state for rainfall data is reflexive properties majority. By using the model, it is concluded that the transition rate matrix of the largest transition probability in the area of 4N90E occurs at the transition from state-1 to state-1 as much is 0.72 and state-2 to state-2 is 0.60. The transition probability value becomes 0.5184 and 0.36 for the same state dominant of two periods P 2 . The use of the 1st order Markov chain is better than 2nd order.
Lightweight Method for Detecting Fake Authentication Attack on Wi-Fi

Wireless networks, despite providing better access and flexibility, have vulnerabilities that are easier to realize compared to wired networks. Fake authentication attack can be taken by an attacker prior to carrying out a Man in the Middle attack to intercept the other party's communication. Such an attack is generally carried out in public places that provide free Wi-Fi access. Detection of fake authentication is necessary to maintain communication success. Several methods have been applied to detect fake authentication such as the use of Wireless Intrusion Detection System (WIDS) or certificates on Transport Layer Security (TLS). However, attackers can trick the use of WIDS or TLS. Moreover, the WIDS and TLS techniques require large costs and computations. In this study, a lightweight method based on the comparison of BSSID/MAC address for detecting fake authentication is proposed. The lightweight method is implemented by creating an application that runs on Android mobile phones, and Linux operating system. We compared the detection performance of the device with the proposed application and the one without the proposed application. It can be concluded that the proposed method using comparison of BSSID / MAC address is an effective way to detect fake authentication attacks on Wi-Fi networks.