Reviewerly/exHarmony huggingface dataset:
Transfer Learning based Computer Vision Technology for Assisting Visually Impaired for detection of Common Places

Visually impaired people face several problems in their daily life. One of the biggest problems is to visiting unfamiliar places and identifying public places like pharmacy store, restrooms, pedestrian signs on roads, etc. Although there are some conventional methods that are available to aid visually impaired people but these are inefficient to use without assistance. The proposed method presents a framework which will help visually impaired people to identify the common public amenities while visiting any unfamiliar places. This method uses deep learning for recognizing some daily used places. For this purpose, VGG16 model is used to extract features from the images and train the sequential model. The model has been tested on varying images of different class that are present in the database. The developed algorithm achieves an accuracy of 95.88%. The obtained result of the developed model shows that it is an efficient method for assisting visually impaired people in real time application.
Learning decision trees through Monte Carlo tree search: An empirical evaluation

Abstract Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. We propose a DT learning approach based on the Upper Confidence Bound applied to tree (UCT) algorithm, including procedures to expand and explore the space of DTs. To mitigate the computational cost of our method, we employ search pruning strategies that discard some branches of the search tree. The experiments show that proposed approach outperformed the C4.5 algorithm in 20 out of 31 datasets, with statistically significant improvements in the trade‐off between prediction performance and DT complexity. The approach improved locally optimal search for datasets with more than 1,000 instances, or for smaller datasets likely arising from complex distributions. This article is categorized under: Algorithmic Development &gt; Hierarchies and Trees Application Areas &gt; Data Mining Software Tools Fundamental Concepts of Data and Knowledge &gt; Data Concepts
Virtual Reality in Research and Education: A Case Study

First virtual reality (VR) applications were in gaming. Since that time, both the hardware and software reached maturity and new areas for applications opened. Industry and healthcare are very different application areas. But they also share some similarities, in particular simulations as close to reality as possible allow to train necessary skills without the danger of damaging machinery or causing a patient injury and to avoid critical situations. We present several examples of VR applications in various industrial areas. VR is not used on its own but it is interconnected with other advanced technologies, such as robotics and Internet of Things. In healthcare, several studies were performed that proved VR being a suitable means for treatment of certain health problems or an additional tool, for example in pain alleviation. Finally we discuss how VR can be used in university education not only as a tool for viewing 3D objects and scenes passively, but also as a tool for active interaction and execution of defined tasks.
A Study on the Icon and Layout Design of Device UI in Augmented Reality

As an new emerging design field, the user interface design of the augmented reality device has many characteristics that distinguish it from the design of former electronic device interface in the form of the expression and interaction. In this paper, it takes the user experience as the center to analyze the device user interface in augmented reality environment and to summarize the skeuomorphic and flat design features of the user interface icons during the interaction with the augmented reality product, as well as the impact of the fixed layout and floating layout of the interface on the user experience, and thus to provide reference for the design and application of the user interface of the augmented reality device.
TDOA-based Passive Tracking of Multiple Civilian Airplanes

Secondary Surveillance Radars (SSR) have been employed substantially in both military and civilian domains. In military applications, the SSR technology has been used for distinguishing between friendly and hostile targets. In civilian applications, the SSR technology has been implemented in air traffic control (ATC) and collision avoidance between airplanes. Today, the majority of civilian airplanes have been equipped with Automatic Dependent Surveillance - Broadcasting (ADS-B) transponders for reporting their identity, position, velocity and additional information. Traditional methods for tracking multiple civilian airplanes consist of detecting ADS-B preamble pulses, decoding ADS-B waveforms and parsing ADS-B messages. In this paper, we provide an alternative solution for passive tracking of multiple civilian airplanes using Time Difference of Arrival (TDOA) localization of mode-S signals. Though being more complicated than classical methods, the proposed solution provides numerous advantages such as the ability to track a larger number of targets and the robustness to jamming attacks.
A Context-Aware Adaptive Tourist Recommendation System

Tourist recommendation systems have been assisting tourists to select their preferred attractions or destinations based on some specified criteria. However, once a recommendation is made, it is unchanged until the travel plan is executed. Some changes occuring before the travel begins may trigger tourist disappointment because the changes may render the plan unexecutable. We propose a solution using a context-aware mechanism. Context-awareness binds some affecting factors to the computation of recommendation, making it sensible and adaptive to any changes. A prototype implementation is also discussed.
Quantum Resource Distribution Management in Multi-task Environment

A proposed resource distribution management model based on quantum optimization approach is introduced in this paper. The proposed quantum strategy can guarantee low computational complexity and high accuracy. moreover, we focused on setting up the parameters of the quantum algorithm with respect to the constraint's variables of the proposed model.
Automatic Decision Making for Parameters in Kernel Method

We propose to use the relationship between the parameter of kernel function and its decisional angle or distance metrics for selecting the optimal setting of the parameter of kernel functions in kernel method-based algorithms. Kernel method is established in the reproducing kernel Hilbert space, the angle and distance are two metrics in such space. We analyse and investigate the relationship between the parameter of kernel function and the metrics (distance or angle) in the reproducing kernel Hilbert space. We design a target function of optimization to model the relationship between these two variables, and found that (1) the landscape shapes of parameter and the metrics are the same in Gaussian kernel function because the norm of all the vectors are equal to one in reproducing kernel Hilbert space; (2) the landscape monotonicity of that are opposite in polynomial kernel function from that of Gaussian kernel. The monotonicity of designed target functions of optimization using Gaussian kernel and polynomial kernel is different as well. The distance metric and angle metric have different distribution characteristics for the decision of parameter setting in kernel function. It needs to balance these two metrics when selecting a proper parameter of the kernel function in kernel-based algorithms. We use evolutionary multi-objective optimization algorithms to obtain the Pareto solutions for optimal selection of the parameter in kernel functions. We found that evolutionary multi-objective optimization algorithms are useful tools to balance the distance metric and angle metric in the decision of parameter setting in kernel method-based algorithms.
Astro for Derivative-Based Stochastic Optimization: Algorithm Description &amp; Numerical Experiments

Adaptive Sampling Trust-Region Optimization (ASTRO) is a class of derivative-based stochastic trust-region algorithms developed to solve stochastic unconstrained optimization problems where the objective function and its gradient are observable only through a noisy oracle or using a large dataset. ASTRO incorporates adaptively sampled function and gradient estimates within a trust-region framework to generate iterates that are guaranteed to converge almost surely to a first-order or a second-order critical point of the objective function. Efficiency in ASTRO stems from two key aspects: (i) adaptive sampling to ensure that the objective function and its gradient are sampled only to the extent needed, so that small sample sizes result when iterates are far from a critical point and large sample sizes result when iterates are near a critical point; and (ii) quasi-Newton Hessian updates using BFGS. We describe ASTRO in detail, give a sense of its theoretical guarantees, and report extensive numerical results.
Named entity recognition using point prediction and active learning

Named entity recognition (NER) research has been spreading into specialty domains. A specialty domain corpus is smaller than a general domain corpus. Moreover, annotating a specialty domain corpus is more expensive than annotating a general corpus. Therefore, in this paper, we introduce a model that uses point-wise prediction and active learning to achieve a high extraction performance even in a small annotation corpus. We demonstrate the effectiveness of our approach through a simulation of active learning.
Facial Expression Recognition using Convolutional Neural Network and SoftMax function on Captured Images

Facial Expression Recognition is an extremely interesting topic of research owing to the uniqueness attached to emotions of different humans. Deep Learning is a novel zone within the domain of machine learning which is exceedingly efficient in image classification problems. Methods of Deep Learning, (CNN) Convolutional Neural Networks in particular, have been used with great precision for the purpose of feature extraction. This paper categorizes each facial image into one of the seven human emotion classes by making use of a specially designed Convolutional Neural Network which employs four subsequent sets of layers and a loss function. The model has been trained and tested on the FER2013 data set from the Kaggle Facial Expression Recognition Challenge, which consists of 35,887, 48-by-48-pixel pictures of human faces, which are grayscale in nature, each with a label of one of the 7 emotion categories. The model gives an accuracy of about 64%.
Approximate Querying for the Property Graph Language Cypher

Graph databases are well-suited to managing large, complex, dynamically evolving datasets. However, for data that is irregular and heterogeneous, it may be difficult to formulate queries that precisely capture a user's information seeking requirements. This points to the need for approximate query processing capabilities that can automatically make changes to a query so as to aid in the incremental discovery of relevant information. In this paper we motivate and explore techniques for providing such capabilities for the Cypher query language. This is the first time that query approximation has been investigated in the context of the property graph data model, which is becoming increasingly prevalent in research and industry.
AI-Based Recommendation System for Social Networking

It is being more than a decade that recommendation system is proposed as a key for solving the issue of information overload and number of algorithms and the system has been proposed for the same. In spite of these attempts, the recommendation system still has number of challenges like improvement in accuracy, cold start, and data sparsity issues. On the contrary, social networking emergence has provided huge amount of information for friend interactions and user behavior. This research work has proposed a novel mechanism of social networking based on recommendation system using genetic algorithm (GA) and artificial neural network (ANN). Parameters such as precision, recall, F-measure, and accuracy are considered to compute the effectiveness of the research work.
MENGUKUR KEMAMPUAN LOGIKA DAN ALGORITMA MAHASISWA MENGGUNAKAN ROUGH SET (STUDI KASUS : MAHASISWA AMIK LABUHAN BATU)

The ability of Logic and Algorithms for computer science students is important, because it affectsmore scientific. Data Mining is the method used to ektraction data in generating new knowledge of thestored data. Data from the entrance exam for new students can be determined the ability of logic andalgorithms meraka. The results of the data extraction can be used AMIK Labuhan Batu in the process ofnew admissions.
Exploring the Effects of Compression via Principal Components Analysis on X-ray Image Classification

Image compression in medical applications implores careful consideration of the effects on data veracity. The inexorable challenge of assessing the volume-veracity trade-off is becoming more prevalent in this critical application area, and particularly when machine learning is used for the purpose of assisted diagnostics. This paper investigates the impact of compressing X-ray images on the accuracy of fracture diagnostics. The accuracy of the classification system is assessed for X-ray images of both healthy and fracture bones when subjected to different levels of compression. Compression is achieved using principal components analysis. Results indicate that accuracy is only marginally affected under a level one compression but begins to deteriorate under level two compression. These results are potentially useful as the level one compression yields gains up to 94% with less than a 2% drop in classification accuracy.
Clustering of Association Rules on Microarray Gene Expression Data

Association rule mining is one of the most important procedures in data mining. In microarray gene expression analysis, often, large number rules are discovered. The rules have to be reduced significantly by techniques such as interest measures or clustering to knowledge mining. In this paper, a novel method for clustering association rules derived from microarray gene expression data is proposed. The gene expression data are converted into gene expression intervals using discretization technique. The association rules are extracted from the maximal frequent itemsets of the gene expression based on the support and confidence. The Euclidean distance measure is used to calculate similarity matrix for the derived association rules. Finally, the single linkage agglomerative clustering algorithm is employed to cluster the association rules based on the similarity matrix. The results obtained using the proposed method outperforms other methods like complete linkage and average linkage.
Synthesis of SQL Queries from Narrations

Structured Query Language (SQL) remains a standard language used in Relational Database Management Systems (RDBMSs), and has found applications in healthcare (patient registries), businesses (inventories, trend analysis), military, and education, etc. Although, SQL statements are English-like, the process of writing SQL queries is often problematic for non-technical end-users. To address this problem, a tool called Narrations-2-SQL is developed to allow an end-user to specify a query in natural language. Narrations-2-SQL is a desktop application that uses a Jumping Finite Automaton (JFA) - a type of Finite Machine for translating natural language descriptions into SQL queries, execute the queries, and provide a feedback to a user. An experimental evaluation was performed on 204 crowdsourced queries in natural language from the XNorthwind DB. Our results show an accuracy of 88%. To get the users' perceptions of this study, we carried out a survey on 167 end-users. Majority of the participants found Narrations-2-SQL to be very helpful, and agreed that it could be useful in industry. If implemented on a large scale, the tool may be helpful to many end-users in different domains.
Navigating Matters of Concern in Participatory Design

Abstract Among 75-90% of residents in Danish nursing homes are diagnosed with dementia. This article explore how a team of five 3rd semester bachelor's design students sought to improve quality of life for elderly people with dementia in a nursing home through a co-design process of a new sensory stimulation technology. Participatory Design researchers agree that it is important to involve diverse actors in the design process such as elderly with dementia, nursing home management, nursing staff etc. But when involving many different actors in designing new products, services and systems the challenge is to navigate the many perspectives and concerns of these actors, which is often conflicting and hence needs negotiating. ‘We draw upon the ANT framework to analyse how ‘matters of concern’ (MoCs) are negotiated and to discuss how designers navigate by staging and facilitating design interactions to support negotiation of MoCs among numerous actors during the design process.
LocoGAN — Locally convolutional GAN

We propose LocoGAN — a fully convolutional GAN model with latent space given by image-like noises of possibly different resolutions. Its learning procedure is local and processes not the whole image-like noises but only the sub-images of a fixed size. Consequently, LocoGAN produces images of arbitrary dimensions, which we present using the LSUN bedroom data set. By leveraging local learning and incorporating the position channels, our model gains an uncommon ability to generate fully periodic (e.g., cylindrical panoramic images) or almost periodic ”infinitely long” images. • We introduce GAN model, which uses a fully convolutional architecture. • The LocoGAN is based on generating patches and then stitching them together. • LocoGAN can generate textures thanks to image-type noise. • LocoGAN can learn on a single image to produce textures.
LSRN: A Recurrent Residual Learning Framework for Continuous Wireless Channel Estimation Using Super-Resolution Concept

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem.To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN.Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements.In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks.Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error.Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.
Combining Merkle Hash Tree and Chaotic Cryptography for Secure Data Fusion in IoT

With the wide applicability of sensors in our daily lives, security has become one of the primary concerns in an Internet of Things (IoT) environment. Particularly, user’s privacy and unauthorized access to sensitive information needs to be kept in mind while designing security algorithms. This paper puts forward a security protocol that integrates authentication of the deployed IoT devices and encryption of the generated data. We have modified the well-known Merkle Hash Tree to adapt to an IoT environment for authenticating the devices and utilized the concepts of Chaos theory for developing the encryption algorithm. The use of chaos in cryptography are known to satisfy the basic requirements of the cryptosystem such as, high sensitivity, high computational speed and high security. In addition, we have proposed a chaotic map named Quadratic Sinusoidal Map which exhibits better array of chaotic regime when compared to the traditional quadratic map. The security analysis demonstrate that the proposed protocol is simple having low computational requirements, has strong security capabilities and highly resilient to security attacks.
Learning Probabilistic Logic Programs over Continuous Data

The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming (PLP): the enabling of stochastic primitives in logic programming. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades, but a major limitation of this exciting landscape is that only discrete features and distributions are handled. Many disciplines express phenomena in terms of continuous models. In this paper, we propose a new computational framework for inducing probabilistic logic programs over continuous and mixed discrete-continuous data. Most significantly, we show how to learn these programs while making no assumption about the true underlying density. Our experiments show the promise of the proposed framework.
Raw Data Redundancy Elimination on Cloud Database

In the recent world, one of the buzzwords in information technology is cloud computing. Cloud computing provides a novel approach of service provision by rearranging numerous resources over the Internet. Cloud computing uses a cloud database to store information. Cloud database can be accessed by the users from anywhere, and at any time with the help of Internet. Cloud database can handle a huge amount of data. But, the data duplicity can be a major problem associated to cloud computing database. This redundant data may go unnoticed but can consume a lot of storage space. In this paper, a novel algorithm is proposed to reduce data redundancy in the cloud environment by separating and analyzing the raw data and the meta-data. The output result shows that the proposed redundancy elimination process in the cloud database could be highly beneficial for the vendor.
Security of Cloud Storage based on Extended Hill Cipher and Homomorphic Encryption

Cloud computing is one of the emerging area in the business world that help to access resources at low expense with high privacy. Security is a standout amongst the most imperative difficulties in cloud network for cloud providers and their customers. In order to ensure security in cloud, we proposed a framework using different encryption algorithm namely Extended hill cipher and homomorphic encryption. Firstly user data/information is isolated into two parts which is static and dynamic data (critical data). Extended hill cipher encryption is applied over more important dynamic part where we are encrypting the string using matrix multiplication. While homomorphic encryption is applied over static data in which it accepts n number of strings as information, encode each string independently and lastly combine all the strings. The test results clearly manifests that the proposed model provides better information security.