Reviewerly/exHarmony huggingface dataset:
Systematic investigation and performance study of authentication and authorization techniques of Internet of Things

At present, Internet of Things (IoT) finds valuable application in the field of smart cards, smart grid, and intelligent transportation. However, security is a major concern of these IoT networks and there is a possibility for the information drip in the network. Thus, the major concerns in IoT net works are secure authentication and data integrity such that authentication and authorization are the two major countermeasures in the security paradigm, which manages, controls, and communicates a device. Accordingly, a multi-level authentication was proposed based on the memory and machine attributes-based profiling and elliptic curve cryptography. Additionally, adaptive level of authorization was developed based on data importance and feedback. Thus, this paper deliberates a systematic study and investigation of the authentication and authorization protocol. For evaluating the robustness of the method, various attacks, such as Distributed Denial of Service (DDOS), Denial of Service (DOS), Man in Middle, Reply Attack, Server Spoofing attack, stolen verifier attack, and Impersonation attack, are considered. The simulation of the methods is done using these attacks, and the analysis is progressed based on detection rate and detection time. The comparative analysis reveals the effectiveness of the methods against various attacks, discussing the security and the detection time.
Soft Intelligence Approaches for Selecting Products in Online Market

Soft Computing techniques are getting more popularity in Digital Media market as it helps to increase the efficiency and profitability in the e-commerce business. Soft intelligence approaches minimize the amount of time to converse the best solution from huge solution base; hence it can process real time huge data for the Business Environment especially for a product or a group of products in the online e-commerce business. To survive in the global competitive business environment for any enterprises, it's necessary to use artificial intelligence to harness its power to reshape the business with the change in demand of people needs. Consumers are highly educated and are aware of the product features along with the best deals over online business sites. The online market leaders in India like Flipkart, Amazon and Snapdeal are taking customer feedback on a product on regular basis and extracting values after analyzing them to devise strategies to attract more and more customers for their products. The purpose of our research work is to propose a new algorithm Hybrid Bee Optimization (HBO) based on Social behavior heuristic algorithms that can be utilized to attract customers based on their past feedbacks and purchased behaviors. The best feature of our proposed algorithm, Hybrid Bee optimization (HBO) is to search products from a huge listed product based on features based on the social behavior of consumers on a product or a group of products.
Smartwatch Application for Horse Gaits Activity Recognition

Activity recognition has been introduced as a means of detecting an action from a series of observations. Although in the literature, the terms "activity recognition" and "human activity recognition" are mostly used interchangeably, there exist several interesting applications for non-human subjects. In this work, we study animal activity recognition with special focus on horse gaits recognition. The on-body and unobtrusive system developed in this paper has several potential applications which can raise attention towards financial, emotional and veterinary health issues related to animals. Leveraging the pervasive use of smartwatches for activity tracking, we develop a smartwatch application to collect accelerometer data. The application is based on novel outlier detection and feature extraction techniques, in conjunction with state-of-the-art machine learning approaches. We perform real life experiments with two horses to evaluate the performances of our proposed system. To this aim, we place the monitoring device both on the horse saddle and the rider's wrist. The results show a high accuracy in both scenarios, which allows a seamless and unobtrusive use of our wearable device application by the rider. In addition, we study the effects of sliding window size and sampling frequency, providing useful insights for future research in horse gaits recognition.
Arcula: A Secure Hierarchical Deterministic Wallet for Multi-asset Blockchains

This work presents Arcula, a new design for hierarchical deterministic wallets that brings identity-based public keys to the blockchain. Arcula is built on top of provably secure cryptographic primitives. It generates all its cryptographic secrets from a user-provided seed and enables the derivation of new public keys based on the identities of users, without requiring any secret information. Unlike other wallets, it achieves all these properties while being secure against privilege escalation. We formalize the security model of hierarchical deterministic wallets and prove that an attacker compromising an arbitrary number of users within an Arcula wallet cannot escalate his privileges and compromise users higher in the access hierarchy. Our design works out-of-the-box with any blockchain that enables the verification of signatures on arbitrary messages. We evaluate its usage in a real-world scenario on the Bitcoin Cash network.
Choosing the optimal hybrid covariance estimators in adaptive elastic net regression models using information complexity

Elastic net (EN) is a regularization technique which is used for modelling and variable selection with high-dimensional data at the same time. In the literature, it is claimed that EN modelling can be used for undersized samples with high dimensions (i.e. n<<p). But, both the model matrix and the Gram matrix are not of full rank p and the inverse of the Gram matrix cannot be calculated. It degenerates and becomes singular. To overcome this problem in EN modelling, Mohebbi et. al. [A new data adaptive elastic net predictive model using hybridized smoothed covariance estimators with information complexity. J Stat Comput Simul. 2019;89(6):1060–1089] purposed a new adaptive elastic net (AEN) modelling using hybrid covariance estimators (HCEs) and information complexity (ICOMP) criteria. There are several forms of HCEs which can be used in AEN regression modelling. Thus, how to decide which HCEs is appropriate is an important problem to be solved. In this paper, we study the performance of the AEN models under several different HCEs using the ICOMP criterion for both the implementation of experimental data and Monte Carlo simulation study with different scenarios of the protocol.
Analytical percolation theory for topological color codes under qubit loss

Quantum information theory has shown strong connections with classical statistical physics. For example, quantum error correcting codes like the surface and the color code present a tolerance to qubit loss that is related to the classical percolation threshold of the lattices where the codes are defined. Here we explore such connection to study analytically the tolerance of the color code when the protocol introduced in Vodola et al. [Phys. Rev. Lett. 121, 060501 (2018)] to correct qubit losses is applied. This protocol is based on the removal of the lost qubit from the code, a neighboring qubit, and the lattice edges where these two qubits reside. We first obtain analytically the average fraction of edges $r(p)$ that the protocol erases from the lattice to correct a fraction $p$ of qubit losses. Then, the threshold ${p}_{c}$ below which the logical information is protected corresponds to the value of $p$ at which $r(p)$ equals the bond-percolation threshold of the lattice. Moreover, we prove that the logical information is protected if and only if the set of lost qubits does not include the entire support of any logical operator. The results presented here open a route to an analytical understanding of the effects of qubit losses in topological quantum error codes.
A Particle Swarm Optimization Based Path Planning Method for Autonomous Systems in Unknown Terrain

Path planning of an autonomous system in unknown terrain is a challenging task. For a risk free and robust navigation, autonomous systems must utilize intelligence to determine the types of terrain and the traversability when optimizing its total cost (function). This paper presents a Particle Swarm Optimization based path planning for autonomous systems in unknown terrain environments. In this work, a new method is proposed toward terrain traversability analysis and estimation. Environmental data is gathered from sensors. Using this information, the proposed method identifies the terrain ahead and classifies them based on their traversability. Different weights are assigned against different types of terrain and these weights measure the characteristics of traversability on this terrain. The methodology autonomously plans a most traversable optimal path. Furthermore, this algorithm is capable to work in dynamic environments by avoiding collisions with obstacles. All simulations are carried out in MATLAB. Simulation results show the effectiveness and robustness of the proposed methodology.
Comparison of Ensemble-Based Multiple Instance Learning Approaches

Multiple instance learning (MIL) is concerned with learning from training set of bags including multiple feature vectors. This paradigm has various algorithms as a solution for multiple instance problem. Recently, ensemble learning has become one of the most preferred machine learning technique because its high classification ability. The main goal of ensemble learning is combining multiple learning models and obtaining a decision from all outputs of these models. Considering this motivation, the study presented in this paper proposes an ensemble-based multiple instance learning approach which merges standard algorithms (MIWrapper and SimpleMI) with ensemble learning methods (Bagging and AdaBoost) to improve classification ability. The proposed approach includes ensemble of combination of MIWrapper and SimpleMI learners with Naive Bayes, Support Vector Machines (SVM), Neural Networks (Multilayer Perceptron (MLP)), and Decision Tree (C4.5) as base classifiers. In the experimental studies, the proposed ensemble-based approach was compared with individual MIWrapper and SimpleMI algorithms in terms of accuracy. The obtained results indicate that the ensemble-based approach shows higher classification ability than the conventional solutions.
Autoencoder Model for OFDM-based Optical Wireless Communication

An orthogonal frequency division multiplexing (OFDM) based Autoencoder (AE) model for optical wireless communication (OWC) is implemented. Symbol-error performance demonstrates the viability of using neural networks (NNs) and deep learning (DL) techniques in OWC systems.
Forensic Performance on Handwriting to Identify Forgery Owing to Word Alteration

Forgery activity in legal handwritten documents is an identifiable problem. Falsification of document due to minute alteration of existings not only causes immense financial loss to a person or to any organization but also lessens the economic growth of a country. Here, we introduce and present a solution to detect forgery in handwritten documents by analyzing perceptually similar ink of different pens. The research is all about forensic investigation of handwritten word alteration which is performed by adding extra letter in a way such that the whole meaning of the word changes. The problem is formulated as binary classification problem. If words of the corresponding document are written by same pen, these are classified as positive class and words of a document accompanied with little inclusion of letters as a forgery attack, are classified as negative class. The article proposes Multilayer Perceptron classifier which has been adopted to classify data instances that have been computed by extracting Y C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> color-based statistical features. This proposal has been tested on data set which has been generated by 10 blue and 10 black ball point pens. The respective obtained average accuracy is 83.71% and 78. 18% for blue pen data and black pen data.
A Novel Approach of Cloud Based Scheduling Using Deep-Learning Approach in E-Commerce Domain

Cloud computing represents a new era of using high quality and a lesser quantity of resources in a number of premises. In cloud computing, especially infrastructure base resources (IAAS), cost denotes an important factor from the service provider. So, cost reduction is the major challenge but at the same time, the cost reduction increases the time which affects the quality of the service provider. This challenge in depth is related to the balance between time and cost resulting in a complex decision-based problem. This analysis helps in motivating the use of learning approaches. In this article, the proposed multi-tasking convolution neural network (M-CNN) is used which provides learning of task-based deadline and cost. Further, provides a decision for the process of task scheduling. The experimental analysis uses two types of dataset. One is the tweets and the other is Genome workflow and the comparison of the method proposed has been done with the use of distinct approaches such as PSO and PSO-GA. Simulated results show significant improvement in the use of both the data sets.
Ultra Wideband Tracking Potential for Augmented Reality Environments

The main objective of this research is to implement a solution for precise visualization of 3D virtual elements where distances from the environment's participant to the object are up to ~200 m. The primary results of this research should ensure a dynamic and animated three-dimensional (3D) computer model depiction in augmented reality (AR) mode without the use of fiducial or image-based markers. Such technological innovation will offer ample cases of augmented reality use in various industries and have economic ramifications. Current outdoor AR solutions lack precision, stability, operational range and multiple 3D object depiction in one scenario, dynamic properties to allow a participant to move freely in an environment without the loss of immersion.
Algorithms for linear time reconstruction by discrete tomography

We present an algorithm that for any given rectangular grid A∈Z2 and set of directions D computes in linear time the values of any function f:A→R outside the convex hull of the union of the switching domains from its line sums in the directions of D. Moreover, the algorithm reconstructs f completely if there are no switching domains. We present a simpler algorithm in case the directions satisfy some monotonicity condition. Finally, for given A we propose how to choose the set D so that only a small number of directions is needed to reconstruct any f from its line sums in the directions of D.
Adaptive weighted aggregation in Group Improvised Harmony Search for lung nodule classification

This work, primarily, addresses the problem of automated diagnosis of lung cancer by classifying malignant nodules present in the lung, if any. To achieve the goal, we have posed a weighted dual objective optimisation problem so as to reduce the feature subset required for automated classification of malignant lung nodules and at the same time, we endeavour to increase the classification accuracy to minimise the false impression. The values of the respective weights associated with the discriminating feature subset and the classification accuracy have been evaluated in such a way that the accuracy of nodule classification increases with the help of a small discriminating feature subset. The strategic weight selection methodology, presented in this work, helps attain an optimal combination of these two objectives. As a solution methodology, subsequently, we propose a new adaptive weighted aggregation strategy based on an evolutionary optimisation technique, popularly known as Group Improvised Harmony Search (GrIHS). An adaptive KNN classifier has also been embedded with the GrIHS method to determine the optimal number of neighbourhoods, in the search space, that would further aid in increasing the nodule classification accuracy. The proposed method has been successfully applied to classify the lung nodules from the Lung Image Database Consortium (LIDC) database. The experimental results demonstrate a noteworthy efficacy of the proposition with sensitivity of 97.59% and 97.78% blind testing accuracy using only 12 discriminating features. Hence, the proposed methodology can be used to support the diagnosis pronounced by the radiologists interpreting manually the lung computed tomography images.
An altered four circulant construction for self-dual codes from group rings and new extremal binary self-dual codes I

We introduce an altered version of the four circulant construction over group rings for self-dual codes. We consider this construction over the binary field, the rings F2+uF2 and F4+uF4; using groups of order 4 and 8. Through these constructions and their extensions, we find binary self-dual codes of lengths 16, 32, 48, 64 and 68, many of which are extremal. In particular, we find forty new extremal binary self-dual codes of length 68, including twelve new codes with γ=5 in W68,2, which is the first instance of such a γ value in the literature.
Augmented Reality Technology in Library Practice

The article considers technology of augmented reality (AR) — the imposition of digital data on real objects in real time. The aim of the article is to analyse the modification of forms and methods of library work under the influence of active implementation of AR-technology. The author notes that the interactive potential of augmented reality interfaces is increasingly being used abroad for navigation within libraries, search for necessary literature in book depositories, for tracking the movement of books and providing users with extended textual and audio-visual information. The article discusses various forms of application of AR-technologies in library practice abroad: supplementing exhibitions and expositions with audio-visual and textual information; use of QR (Quick response) codes and RFID (Radio frequency identification) tags to facilitate access to online information, development of computer games for orientation in the library space, modernization of management of library collections, etc. The author describes the use of radio-frequency tags with special elements (RFID station) for personalized assistance to readers, optimization of social relations, so familiar to modern readers who are able to specify their location, leave a message to friends or staff, etc. Such a wide application of AR-technology in the library makes it a fragment of a “smart city”, convenient intellectual environment with a light transition membrane from offline to online. Among library mobile applications as the fastest growing sphere of library services providing online access to catalogue, databases, interlibrary lending, chat rooms, SMS services, Twitter, Facebook, YouTube, etc. accounts, the share of AR applications using QR codes and RFID tags is constantly growing. The use of augmented reality devices in libraries opens up new horizons in terms of introduction of modern methods of informatization of knowledge and user services, adequate to the era of comprehensive computerization, the ubiquitous “Internet of things”.
Fashion Outfit Generation for E-Commerce

The task of combining complimentary pieces of clothing into an outfit is familiar to most people, but has thus far proved difficult to automate. We present a model that uses multimodal embeddings of pieces of clothing based on images and textual descriptions. The embeddings and a shared style space are trained end to end in a novel deep neural network architecture. The network is trained on the largest and richest labelled outfit dataset made available to date, which we open source. This is the first public expert created, labelled dataset and contains 586,320 labelled outfits. We evaluate the performance of our model using an AB test and compare it to a template based model that selects items from the correct classes, but ignores style. Our experiments show that our model outperforms by 21% and 34% for womenswear and menswear respectively.
Accuracy Improvement of Face Recognition System based on Co-occurrence Matrix of Local Median Binary Pattern

Face recognition is an important subject because of its usefulness in many applications like banking system and door access control systems. For this reason, there is always the need to recognize faces automatically with high accuracy. In this paper, we improve the accuracy of face recognition systems using Co-occurrence Matrix of Local Median Binary Pattern features. In other words, the combination of two algorithms is used. That way local median binary pattern (LMBP) operator applied on the image and then the co-occurrence matrix of the LMBP image is calculated. Our experimental results on ORL and Yale databases show that the proposed algorithm has the accuracy of 96.25% and 100% on mentioned image databases, respectively.
Preserving Structural Relationships for Person Re-Identification

Recent processes on many computer vision and multimedia researches heavily rely on Convolutional Neural Network (CNN) with pooling layer incorporated, where pooling operation reduces the amount of parameters and brings in translation invariance. However, we discover that pooling operation may destroy valuable structural relationship information, leading to defective feature learning in tasks such as person re-identification. In this paper, we propose a method called Structural Relationship Learning (SRL) to capture structural relationships by constructing a spatially structured graph based on the convolved features and propagate information over the edges. Coupled with pooling operation by metric fusion, SRL provides more comprehensive information for identity discrimination. Experiments are conducted on the iLDIS-VID, PRID2011 and MARS datasets and the results demonstrate the advantages of our proposed method.
Automating Knowledge Transfer with Multi-Task Optimization

Algorithmic knowledge transfer can make difficult problems easier to solve well. Most existing work on knowledge transfer for optimization relies on humans to manually select source tasks to transfer information from, and attempts to automate the source selection process are few and far apart. In this work, we survey the existing methods that have been devised for knowledge transfer in evolutionary algorithms, and we present an experimental approach to automated source task selection based on a multi-task implementation of Cartesian genetic programming (MTCGP). Our experiments indicate that this strategy outperforms single-task CGP on solving a set of Boolean function synthesis tasks. We further develop a mutation weighting scheme aimed at protecting useful components from destructive mutation.
Hierarchical Fuzzy Inference System for Diagnosing Dengue Disease

Fuzzy inference systems (FIS) have been widely used in medical diagnosis. FIS uses fuzzy reasoning in order to represent the knowledge of experts about certain problem in a systematic manner. In the last decade, Dengue fever has been considered as a severe disease in the western area of Saudi Arabia, where millions of pilgrims come from all over the world to perform their Hajj. FIS can be used to early diagnose this disease and reduce its severity. However, because of many number of factors associated with Dengue, using traditional FIS causes generating huge number of fuzzy rules, which leads to high complexity. In order to tackle this issue, this paper proposes a Hierarchical Fuzzy Inference system (HFIS) that can be used to diagnose Dengue fever. The proposed model is compared to one from the literature and shows more promising results from the perspective of simplicity and extendibility.