Reviewerly/exHarmony huggingface dataset:
Shaping the Intelligent Classroom of the Future

This paper explores the general concept of the classroom of the future from a technological perspective, and proposes a set of indicative key facilities that such an environment should incorporate. Over the years, there has been an abundance of related research work aiming to build a “smarter” classroom, initially incorporating distance learning, educational games, and intelligent tutoring systems. More recently, many approaches have revolved around the advancements in the domains of Ambient Intelligence and Internet of Things, resulting in the enhancement of the traditional classroom equipment and furniture with processing power and interaction capabilities (e.g. intelligent desk, smart whiteboard) and the integration of emerging solutions in teaching and learning methods (e.g. AR, VR). The proposed intelligent classroom though is a holistic approach towards a student-centric educational ecosystem, which will incorporate state-of-the-art technologies to support (among others) alternative pedagogies, learning through immersive hands-on experiences and collaboration via flexible class layouts. To that end, this paper reports the various ambient facilities of the classroom and the accompanying software, while a prototype of this environment is currently under development in the AmI Facility of FORTH-ICS.
An Analysis of UI/UX Designing With Software Prototyping Tools

In an age where everyone is carrying a smart phone, it is of utmost importance to make efficient use of the upcoming technologies. This indicates the rise in number of applications being created for mobile devices. As a result, mobile user interface designing has become a significant part of application designing. There has been an increasing number of devices today providing powerful graphics capabilities helping users to deal with huge amount of information. However, the prototyping tools currently being used in the industry are lacking features and are not addressing some of the prime issues like user friendliness, functionalities, representation, and enforcement. This chapter presents a tool based on an analysis of different popular prototyping tools in the industry which will overcome some or all of the major issues faced by application designers. The authors describe the prototyping tool's concept, design, features, as well as how it is suitable for making great user interfaces helping application designers to design exactly what they want.
Constructive Policy: Reinforcement Learning Approach for Connected Multi-Agent Systems

Policy based reinforcement learning methods are widely used for multi-agent systems to learn optimal actions given any state; with partial or even no model representation. However multi-agent systems with complex structures (curse of dimensionality) or with high constraints (like bio-inspired (a) snake or serpentine robots) show limited performance in such environments due to sparse-reward nature of environment and no fully observable model representation. In this paper we present a constructive learning and planning scheme that reduces the complexity of high-diemensional agent model by decomposing it into identical, connected and scaled down multiagent structure and then apply learning framework in layers of local and global ranking. Our layered hierarchy method also decomposes the final goal into multiple sub-tasks and a global task (final goal) that is bias-induced function of local sub-tasks. Local layer deals with learning `reusable' local policy for a local agent to achieve a sub-task optimally; that local policy can also be reused by other identical local agents. Furthermore, global layer learns a policy to apply right combination of local policies that are parameterized over entire connected structure of local agents to achieve the global task by collaborative construction of local agents. After learning local policies and while learning global policy, the framework generates sub-tasks for each local agent, and accepts local agents' intrinsic rewards as positive bias towards maximum global reward based of optimal sub-tasks assignments. The advantage of proposed approach includes better exploration due to decomposition of dimensions, and reusability of learning paradigm over extended dimension spaces. We apply the constructive policy method to serpentine robot with hyper-redundant degrees of freedom (DOF), for achieving optimal control and we also outline connection to hierarchical apprenticeship learning methods which can be seen as layered learning framework for complex control tasks.
3DSportNet: 3D sport reconstruction by quality-aware deep multi-video summation

Automatically reconstructing 3D sceneries from video sequences is an indispensable technique in computer 3D games, urban planning, and intelligent navigation. Many previous work relies on complicated and expensive equipment to fulfill 3D reconstruction under constrained environments. Nevertheless, such schemes are not readily to be applied for reconstructing 3D sport sceneries, such as basketball and mountain climbing. In this work, we propose a novel deep architecture: 3DSportNet, which reconstructs 3D sport sceneries by making use of multiple handheld videos captured by smart phones. In particular, given a rich of mobile videos captured by users, we extract multiple deep/shallow visual features from each sport video frame by leveraging the weakly-supervised semantic encoding. Afterward, a geometry-aware quality model is designed to summarize the multiple videos into multiple key frames from each single video, wherein the objective is that the selected key frames can maximally reconstruct the multiple sport videos. Based on this, we employ the key frames to reconstruct sport videos by utilizing the PMVS2 software. Comprehensive experimental comparisons and visualization results have shown that our method can produce very real 3D sport sceneries and athletes. Besides, the 3D reconstruction time consumption is reduced by 95% compared to conventional methods.
Identification of Fraudulent Alteration by Similar Pen Ink in Handwritten Bank Cheque

In the research field of document image analysis, especially in handwritten documents, fraudulent alteration identification is a crucial task due to several forgery activities that are happening for few decades which affect a nation economically. In this paper, we are differentiating visually identical ink of different pens used for alteration in such documents by considering this problem as a binary classification problem. Here, we have used $$YC_bC_r$$ color model. To formulate the problem a number of statistical and texture features are extracted to create feature vectors. These feature vectors are accommodated to generate data set which are classified by multilayer perceptron technique. The method has been executed on both blue and black ink samples. On average, the proposed method produces an efficient result of accuracy of more than $$93\%$$ for known and $$82.30\%$$ for unknown pen data, respectively. This performance measurement shows the efficacy of the proposed method comparing with other existing methods.
GO-FOR: A Goal-Oriented Framework for Ontology Reuse

Ontologies have been successfully used to assign semantics in the Semantic Web context and to enable integration of data from different systems or different sources. However, building ontologies is not a trivial task. Ontology reuse can help in this matter. The search and selection of ontologies to be reused should consider the alignment between their scope and the scope of the ontology being developed. In this paper, we discuss how goal modeling can be helpful in this context and we propose GO-FOR, a framework in which goals are the central elements to promote ontology reuse. We introduce goal-oriented ontology patterns as a new type of pattern to be applied to develop ontologies in a goal-oriented approach. Results of the use of GO-FOR to build an ontology used to integrate water quality data are also shown in this paper.
Core Metadata Element Recommendations for Institutional Repositories at Texas A&amp;M University Libraries

In 2017, a working group was formed at Texas A&M University Libraries to propose policies that would ensure the quality and consistency of descriptive metadata in the Libraries' new DAME (Digital Asset Management Ecosystem), which consists of multiple repositories. This article describes the process that it used to identify a "core" set of metadata elements for all resources that would be stored within the DAME, and their recommendations for a three-tiered approach to address different levels of completeness. In addition, this article describes the issues that were encountered when recommending controlled vocabularies and standards for inputting certain metadata elements.
Review of various Artificial Intelligence Techniques and its applications

Abstract With the upgrading needs of automation and prediction requirements in the industries, automation for improving the quality of the product and prediction of the product cycle to make product more economical. Now a days the use of artificial intelligence techniques becoming crucial to apply in the industries which makes the products more reliable, robust and economical. This paper deals and explains the different techniques of artificial intelligence. Also covers the review of the different applications of these techniques in the industries. This paper covers the review of Artificial intelligence techniques applied in the different fields like civil engineering, biomedical engineering, mechanical engineering, electrical and electronics engineering and many more.
Heavenly Bodies: Why It Matters That Cyborgs Have Always Been About Disability, Mental Health, and Marginalization

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Defining Meta-Model for Value-Oriented Requirement Prioritization Technique

Software requirements prioritization is a well-known practice in Requirements Engineering (RE) that helps in managing the requirements as stated in their requirements listing. It helps to identify that which requirements should be dealt first. Quality of software to be developed depends on the ordering of requirements and assists the project team to find out the most important requirements. In order to perform efficient software requirement prioritization, decision making process to prioritize the requirements and eliminating complexities caused by unclear and incomplete requirements are two main challenges. It is also analyzed that existing techniques of requirements prioritization such as grouping, MoSCoW and validated learning are working well for functional requirements but insufficient to deal with non-functional requirements. In order to bridge this gap, meta-model for value oriented requirement prioritization technique is proposed in order to prioritize functional as well as non-functional requirements. Meta modeling technique is highly beneficial that helps to define things at an abstract level and helps in sorting out the associated problems by identifying major components and reducing complexity to requirement engineer.
Agent-Based Methods for Medical Image Registration

Medical imaging registration is a critical step in a wide spectrum of medical applications from diagnosis to therapy and has been an extensively studied research field. Prior to the popularity of deep learningDeep learning, image registrationImage registration was commonly performed by optimizing an image matching metric as a cost function in search for the optimal registration. However, the optimization task is known to be challenging due to (1) the non-convex nature of the matching metric over the registration parameter space and (2) the lack of effective approaches for robust optimization. With the latest advance in deep learningDeep learning and artificial intelligence, the field of medical image registrationImage registration had a major paradigm shift, whereby learning-based image registrationImage registration methods are developed to employ deep neural networks to analyze images in order to estimate plausible registrations. Among the latest advances in learning-based registration methods, agent-based methods have been shown to be effective in both 3-D/3-D and 2-D/3-D registrations with significant robustness advantage over conventional optimization-based methods. In this chapter, we give an overview of agent-based methods for medical image registrationImage registration and its two applications on rigid-body 3-D/3-D and 2-D/3-D registrations.
Image Colorfulness Measure for Multilayer Compressed Sensing Model

Colorfulness is an aspect of the visual perception, according to which the color of an object is perceived to be more or less chromatic. However, the existing colorfulness measures face a problem that may fail to measure color distortion for the compressed sensing and even multilayer compressed sensing images. In this paper, we propose a Multilayer Compressed Sensing (Mul-CS) model and a new colorfulness measure (CM-MulCS) for the model. The Mul-CS model is based on an improved Gaussian random measurement matrix, in which the feature map of each layer is visualized by a proposed Gaussian Fitting Visualization (GFV) algorithm. The CM-MulCS is based on a color noise intensity adaptive weighting model which benefits measure of color distortion for the compressed sensing images and the feature maps in Mul-CS model. The experimental results show that GFV and CM-MulCS for Mul-CS model perform better than some popular existing visualization algorithms and colorfulness measures, respectively.
Data Clustering Using Online Variational Learning of Finite Scaled Dirichlet Mixture Models

With a massive amount of data created on a daily basis, the ubiquitous demand for data analysis is obvious. Recent development of technology has made machine learning techniques applicable to various problems. In this paper, we emphasize on cluster analysis, an important aspect of data analysis. In other words, being able to automatically discover different groups containing similar data is crucial for further information retrieving and anomaly detection tasks. Thus, we propose an online variational inference framework for finite Scaled Dirichlet mixture models. By efficiently handling large scale data, online approach is capable of enhancing the scalability of finite mixture models for demanding applications in real time. The proposed method can simultaneously update the model's parameters and determine the optimal number of components without the complex computation of conventional Bayesian algorithm. The effectiveness of our model is affirmed with challenging problems including spam detection and image clustering.
Integral Privacy Compliant Statistics Computation

Abstract Data analysis is expected to provide accurate descriptions of the data. However, this is in opposition to privacy requirements when working with sensitive data. In this case, there is a need to ensure that no disclosure of sensitive information takes place by releasing the data analysis results. Therefore, privacy-preserving data analysis has become significant. Enforcing strict privacy guarantees can significantly distort data or the results of the data analysis, thus limiting their analytical utility (i.e., differential privacy). In an attempt to address this issue, in this paper we discuss how “integral privacy”; a re-sampling based privacy model; can be used to compute descriptive statistics of a given dataset with high utility. In integral privacy, privacy is achieved through the notion of stability, which leads to release of the least susceptible data analysis result towards the changes in the input dataset. Here, stability is explained by the relative frequency of different generators (re-samples of data) that lead to the same data analysis results. In this work, we compare the results of integrally private statistics with respect to different theoretical data distributions and real world data with differing parameters. Moreover, the results are compared with statistics obtained through differential privacy. Finally, through empirical analysis, it is shown that the integral privacy based approach has high utility and robustness compared to differential privacy. Due to the computational complexity of the method we propose that integral privacy to be more suitable towards small datasets where differential privacy performs poorly. However, adopting an efficient re-sampling mechanism can further improve the computational efficiency in terms of integral privacy.
PageCmp: Bandwidth Efficient Page Deduplication through In-memory Page Comparison

KSM-based page deduplication is an important Linux system service for reducing main memory consumption on cloud servers. However, it tends to incur large computation and memory bandwidth overheads. Recently proposed hardware-assisted KSM approaches, while effectively addressing the computation overhead, still need to consume a dramatic amount of off-chip memory bandwidth. In this paper, we propose PageCmp, a PIM (Processing-In-Memory) based page deduplication approach, to achieve bandwidth efficiency on cloud servers. PageCmp exploits the bitwise operation capability inside the DRAM cell array to enable fast page comparison. By integrating a lightweight local comparator inside the output buffer of DRAM modules, PageCmp sends only the page comparison result back to the processor. Our experimental results show that, comparing to the state-of-the-art, PageCmp achieves 4x memory bandwidth reduction while introducing less than 1% hardware overhead.
WLMS-based Transmission Refined Self-Adjusted No Reference Weather Independent Image Visibility Improvement

Single image dehazing is an ill-posed, challenging issue out of several dehazing techniques due to its limited information. Transmission map is a crucial reference to retrieve the dehazed output. An attempt has been taken to remove artefacts in the depth map of the hazy image with Weighted Least Mean Square-based edge preserved smoothing technique which in turn estimate artifact-free transmission map. Original bright, clear, and natural image radiance is restored using refined transmission estimation and image recovery optical model developed and it has been showed to be effective in comparison with the state-of-the art techniques. Extensive experiments (quality and quantity) validate that the proposed technique is computationally less expensive, simple, and efficient. Computational complexity is linear or quasi-linear depending on the size of the image, O(n) or O(nlogn). In either case complexity is still lower than other state-of-the art techniques. The proposed algorithm is adaptable to remove different types of haze. Visible distance, as well as extinction coefficient of the medium of the hazy and recovered images, are also measured.
An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering

Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems.
Introduction of Blockchain and Usage of Blockchain in Internet of Things

Since the advent of the web, the number of users who started using the internet for everyday purpose has increased tremendously. Most of the common purposes are to access their data whenever they want and wherever they want. So many companies have started providing these services to normal users. These companies store huge volume of data in the data centers. So protecting the integrity of the data is the main responsibility of these companies. Blockchain is one of the trending solutions that gives storage immutability to the users. This chapter starts with the working of blockchain and smart contracts and advantages and disadvantages of blockchain and smart contracts and then goes on to explain how blockchain can be integrated into the internet of things (IOT). This chapter ends with an architecture based on the proof-of-concept for access management, which is blockchain-based fully distributed architecture.
Robustly Clipped Sub-equalized Histogram-Based Cosine-Transformed Energy-Redistributed Gamma Correction for Satellite Image Enhancement

In this paper, a new proposal is reported for image quality enhancement. Here, statistically clipped, bi-histogram equalization-based adaptive gamma correction along with its cosine-transformed energy redistribution is introduced for improvement of low-contrast dark images. This approach computes the clipping limit adaptively by observing stretched histogram bins, mean, and median values for each sub-histogram. Considering the clipping limit as the lowest of these three values, this limit ensures the conservation of information content of the image to a great extent. This adaptive clipping limit selection also resolves the issue of over-emphasization of high-frequency bins during sub-histogram equalization. For harvesting more information and better illumination, gamma correction is imparted by using the adaptive gamma value-set. The corresponding gamma value-set is itself derived from the previously sub-equalized interim intensity channel. In addition to this, two-dimensional (2D) discrete cosine transformation (DCT) for gamma-corrected channel is also employed for incorporating the energy-based textural enhancement framework. Validation is performed here by analyzing the enhancement for various remotely sensed dark satellite images by evaluating standard performance indices.
Energy Efficient Distributed Processing in Vehicular Cloud Architecture

Facilitating the revolution for smarter cities, vehicles are getting smarter and equipped with more resources to go beyond transportation functionality. On-Board Units (OBU) are efficient computers inside vehicles that serve safety and non-safety based applications. However, much of these resources are underutilised. On the other hand, more users are relying now on cloud computing which is becoming costly and energy consuming. In this paper, we develop a Mixed Integer linear Programming (MILP) model that optimizes the allocation of processing demands in an architecture that encompasses the vehicles, edge and cloud computing with the objective of minimizing power consumption. The results show power savings of 70% - 90% compared to conventional clouds for small demands. For medium and large demand sizes, the results show 20% - 30% power saving as the cloud was used partially due to capacity limitations on the vehicular and edge nodes.
A Formal Model to Integrate Behavioral and Structural Adaptations in Self-adaptive Systems

An approach for modelling adaptive complex systems should be flexible and scalable to allow a system to grow easily, and should have a formal foundation to guarantee the correctness of the system behavior. In this paper, we present the architecture, and formal syntax and semantics of HPobSAM which is a model for specifying behavioral and structural adaptations to model large-scale systems and address re-usability concerns. Self-adaptive modules are used as the building blocks to structure a system, and policies are used as the mechanism to perform both behavioral and structural adaptations. While a self-adaptive module is autonomous to achieve its local goals by collaborating with other self-adaptive modules, it is controlled by a higher-level entity to prevent undesirable behavior. HPobSAM is formalized using a combination of algebraic, graph transformation-based and actor-based formalisms.