Reviewerly/exHarmony huggingface dataset:
Revolutionize Cosine Answer Matching Technique for Question Answering System

Imitating humans by computers i.e. Knowledge Engineering has been into trend from past few years, resulting into reducing the unnecessary manpower and skilled workers in the field of education when it comes to computer assisted assessments. Various techniques developed to evaluate answers by computers are known to be non-performing resulting into undeveloped system that revolutionizes traditional method of assessment and instant evaluation of results. One such technique is the TF-IDF using cosine similarity which proves to be a bit lacked out when comes to synonym correction. This paper aims at revolutionizing the traditional assessment technique of pen paper, replacing it with modern solutions where computer imitates the skilled person. The main objective is to achieve an algorithm that best represents the or imitates the human when it comes to evaluating the subjective answers using Natural Language Processing techniques. This method proves to be efficient enough to withdraw the traditional system and develop a distinct solution that will benefit various Educational Institutions.
Applications of Myo Armband Using EMG and IMU Signals

Human-Robot Interaction (HRI) plays an important role in a wide range of applications in healthcare, entertainment, and industries. Many ways are being devised to improve the interaction between humans and machines in the field of robotics. For instance, the existing approaches claim precise control over the robotic arm using many different controllers like the joystick, keypad control, or electroMyographic (EMG) sensors attached to the human arm as separate units. However, it requires a non-practical amount of calibration time and high operational expertise. Therefore, our goal is to mimic the human arm movements on the 6-degree of freedom (DOF) robotic arm in real-time, providing minimum latency. We practically show the interfacing of Myo armband with robots that provide tremendously low calibration time without compromising efficiency. We also allow a 6-DOF using EMG and inertial measurement unit (IMU) signals for Myo armband and extract the values of EMG and IMU sensors. The extracted raw EMG and IMU values are calibrated using Arduino as a standard micro-controller. Due to the extraction of raw values, Myo armband can also be integrated with other users' peripheral, which in our case is a 6-DOF robotic arm.
An Alternative Exploitation of Isolation Forests for Outlier Detection

Isolation Forests are one of the most successful outlier detection techniques: they isolate outliers by performing random splits in each node. It has been recently shown that a trained Random Forest-based model can also be used to define and extract informative distance measures between objects. Although their success has been shown mainly in the clustering field, we propose to extract these pairwise distances between the objects from an Isolation Forest and use them as input to a distance or density-based outlier detector. We show that the extracted distances from Isolation Forests are able to describe outliers meaningfully. We evaluate our technique on ten benchmark datasets for outlier detection: we employ three different distance measures and evaluate the obtained representation using a density-based classifier, the Local Outlier Factor. We also compare the methodology to the standard Isolation Forests scheme.
Co-operative Collision Avoidance for Unmanned Aerial Vehicles using both Centralised and Decoupled Approaches

This paper presents the design of co-operative collision avoidance algorithms for Unmanned Aerial Vehicles (UAVs) using vertical avoidance manoeuvres. The co-operative collision avoidance problem is formulated as an optimal control problem and solved using an A⁎ search algorithm. Two different approaches are developed and compared: a centralised approach where the collision avoidance trajectories for all UAVs are planned simultaneously, and a decoupled approach where the individual collision avoidance trajectories for each UAV is planned sequentially, with the planning sequence determined by a UAV priority order. The UAVs co-operate by sharing their state and intent information with one another and with a central node, if present. The co-operative collision avoidance algorithms are verified and evaluated using illustrative simulations. These simulations support the expected behaviour of the algorithms. The centralised approach finds the most optimal solution to the problem while the solution found by the decoupled approach depends on the priority allocation of the UAVs. The decoupled approach can find either the most optimal or a sub-optimal solution to the problem, with the priority allocation occasionally resulting in the decoupled approach being unable to find a solution. This suggests that the centralised approach will, on average, find solutions more often and find more optimal solutions than the decoupled approach.
Handwritten Numerals Recognition by Employing a Transfer Learned Deep Convolution Neural Network for Diverse Literature

Having around 6,500 languages worldwide, handwritten numerals recognition has been a domain of research for decades now as numerals are a common phenomenon among all these diverse languages. Previously, researchers have contributed significantly to recognize the handwritten numerals of diverse literature. Different approaches have been discovered to be feasible for language-specific numerals recognition. However, finding a common architecture to recognize numerals have been a goal from the very beginning. But despite having many efforts, discovering a common architecture for high recognition of numerals of diverse literature has always been a challenging task to solve and not many contributions have been made in this regard. Therefore, in this research, we focused on seven benchmark datasets of six languages and proposed a modified DenseNet-201 architecture. Our proposed architecture achieved an overall accuracy of 99.04%, 99.33%, 98.83%, 99.50%, 99.83%, 99.54%, and 99.74% for Bengali (CMATERdb 3.1.1), Devanagari (CMATERdb 3.2.1), Arabic (CMATERdb 3.3.1), Telugu (CMATERdb 3.4.1), Nepali, ARDIS II, and ARDIS III datasets respectively which outperformed all notable previous works by a noteworthy margin.
Data hiding in Image steganography using Range Technique for secure communication

With the spread of digital data across the world through the internet, the security of private information has raised concern as the data is the primary component of computer communication and lots of techniques are available to afford adequate protection to store the data securely. The cryptography and steganography methodologies are the most ideal ways to provide adequate safety in the field of data communication. Combining steganography with cryptography would be a perfect choice and pictures are the foremost common cover pictures for steganography. The present work focuses on the picture as cover-media and thus image-based steganography and cryptography methodologies are developed to attain significant characteristics such as enhancing data security, very high embedding capacity, and high stego image quality. The Blowfish encryption algorithm is used to enhance security. Uses Range Technique and Random Number Generator also to increase the hiding capacity. In arrange to arbitrarily select the pixel positions the information must cover-up in utilizing the Random Number Generator. The Range Technique is used for hiding the data in the corresponding pixel positions based on the pixel intensities. By fixing the range based on the intensities we can embed more data in an image without changing the image quality. To assess the image quality, the effectiveness of the stego image can be measured by certain common measurements such as Peak Signal to Noise Ratio and Mean Squared Error. Theproposed method tested extensively through many attacks on steganalysis, such as histogram and chi-square analysis.
Bangla Sign Digits: A Dataset For Real Time Hand Gesture Recognition

With current widespread computer vision study, research with a particular culture and aesthetic contexts lead to enhanced performance with improved user experience. Despite a large number of deaf and dumb people, Bangla Sign Language has been less-focused on the research area. This paper proposes the building of identifying Bangla Sign Language using hand gestures to reduce the isolation of the hearing-impairment world. For the last little advancement in deep learning, Convolution Neural Network (CNN) has been used to classify Bangla Sign Language with higher accuracy. Our methodology overcomes all constraints from existing research of Bangla hand gesture recognition. In this paper, we presented a novel real-time method for Bangla sign digits (0-9) that focuses on three sections: image acquisition, preprocessing, and lastly, recognition of Bangla sign digits. For training, we have created a dataset of 1674 images from different people from various conditions. The proposed CNN model architecture classifies all Bangla sign digits with an overall accuracy of 97.63% on real-time video that ensures a potential gesture model that contributed significantly in the domain of Bangla sign number recognition compared to previous researches.
Hardware Constructions for Error Detection in Lightweight Welch-Gong (WG)-Oriented Streamcipher WAGE Benchmarked on FPGA

Providing an acceptable level of security at low cost becomes a challenge in embedded systems that have limited resources, e.g., the devices used in the Internet of Things applications. Lightweight cryptography aims to overcome this challenge by adopting security measures which are well suited for resource-constrained usage models. As we move towards standardizing the approach for lightweight cryptography, several cipher implementations were proposed to NIST, out of which WAGE is one of the algorithms advanced to the next round. WAGE is a 259-bit lightweight stream cipher that derives its cryptographic properties from the WG stream cipher and is designed to provide Authenticated Encryption with Associated Data (AEAD) in hardware implementation. In this paper, the error detection schemes for the nonlinear sub-blocks of WAGE cipher have been presented for reliable implementations of the cipher on hardware. The signature-based error detection schemes presented in this paper are platform oblivious. Derivations for signature and interleaved signature schemes for both logic- and LUT-based implementations are presented for the 7-bit S-Box and Welch- Gong permutation (WGP) of WAGE. The presented schemes are evaluated for error coverage and are benchmarked on field-programmable gate array (FPGA) to show acceptable overheads and practical error coverage.
BYOD Cyber Threat Detection and Protection Model

Increased demand for Bring Your Own Device (BYOD) service enablement becomes an integral part of the critical infrastructure due to business agility, increase employee productivity, and employee satisfaction as an alternate method of the working environment. But unauthorized malicious access of critical infrastructure from the BYOD environment increased the cyber-attack which becomes a major cybersecurity risk for most organizations as a result business ecosystem gets fragmented. There are many methods and techniques developed in recent years to reduce cyber attacks in this area which solves multiple open issues. But due to new attack techniques and tools again development is an on-going requirement. Most of the organization is adopting secure certificate-based authentication mechanism to enable BYOD environment. But the certificate-based authentication mechanism has also many challenges that open a loophole for unauthorized access. Apart from this Cyber forensic digital investigation ecosystem development becomes an important requirement to conduct post-incident analysis, investigation, and to produce evidence of the crime. In this research, post-analysis of unauthorized malicious activities in the BYOD environment Detection Technique is proposed as a novel approach, apart from this subsequent protection algorithm also developed to build efficient secured BYOD environment. Finally, the research outcome contributes to building a Cyber-defense BYOD environment and cyber forensic BYOD ecosystem to protect the critical infrastructure of the organization adopting the BYOD services.
Video Anomaly Detection Using the Optimization-Enabled Deep Convolutional Neural Network

Abstract In video surveillance, automatic detection of the anomalies is the active research area in computer technology. Even though various video anomaly detection methods are introduced, detecting anomalous events, such as illegal actions and crimes, is a major challenging issue in video surveillance. Thus, an effective automatic video anomaly detection strategy based on the deep convolutional neural network (deep CNN) is developed in this research. Initially, the input video surveillance is passed into the spatiotemporal feature descriptor, named Histograms of Optical Flow Orientation and Magnitude. The features obtained from the descriptor provide the optical flow details with the aspect of normal patterns from the scene. These patterns are further subjected to the deep CNN, which is trained using the proposed dragonfly-rider optimization algorithm (DragROA) to assure the classification either as an anomalous activity or normal. The proposed DragROA is the combination of the standard dragonfly optimization algorithm and the standard rider optimization algorithm. The implementation of the proposed DragROA-based deep CNN is carried out using two datasets, namely anomaly detection dataset and UMN dataset; the performance is analyzed using the metrics, namely accuracy, sensitivity and specificity. From the analysis, it is depicted that the proposed method obtains the maximum accuracy, sensitivity and specificity of 0.9922, 0.9809 and 1, respectively, for the UCSD dataset.
Testing Independence Under Biased Sampling

Testing for dependence between pairs of random variables is a fundamental problem in statistics. In some applications, data are subject to selection bias that can create spurious dependence. An important example is truncation models, in which observed pairs are restricted to a specific subset of the X-Y plane. Standard tests for independence are not suitable in such cases, and alternative tests that take the selection bias into account are required. Here, we generalize the notion of quasi-independence with respect to the sampling mechanism, and study the problem of detecting any deviations from it. We develop two tests statistics motivated by the classic Hoeffding's statistic, and use two approaches to compute their distribution under the null: (i) a bootstrap-based approach, and (ii) a permutation-test with nonuniform probability of permutations. We also handle an important application to the case of censoring with truncation, by estimating the biased sampling mechanism from the data. We prove the validity of the tests, and show, using simulations, that they improve power compared to competing methods for important special cases. The tests are applied to four datasets, two that are subject to truncation, with and without censoring, and two to bias mechanisms related to length bias.
The predictive capabilities of mathematical models for the type-token relationship in English language corpora

We investigate the predictive capability of mathematical models of the type-token relationship applied to the vocabulary growth profiles of selected English language documents. We compare the existing Good-Toulmin and Heaps formulae with an alternative approach based on Bernoulli trial word selection from a fixed finite vocabulary using the Zipf and Zipf-Mandelbrot probability distributions. We make two major observations: firstly, while the Zipf-Mandelbrot model makes better predictions of vocabulary growth than the Zipf model, the optimized parameters of the latter correlate better than those of the former with statistics gleaned independently from the data. Secondly, the mean of the Zipf-Mandelbrot, Good-Toulmin and Heaps models provides a more consistent and unbiased prediction of vocabulary than any individual model alone.
eCaaS

Enterprises are containerizing their business applications and extending those applications from cloud to edge to achieve better flexibility, agility, and performance for their business workload. Unlike data centers, edge sites including infrastructure and orchestration systems are often heterogeneous and highly customized depending on the resource availability, business requirements of the use case, and technical requirements of the application. However, in many business use cases, the lack of IT professionals with proper domain expertise makes it very challenging to create, manage, and support heterogeneous containerized edge sites at a large scale.
Optimal Training of Echo State Networks via Scenario Optimization

Echo State Networks (ESNs) are widely-used Recurrent Neural Networks. They are dynamical systems including, in state-space form, a nonlinear state equation and a linear output transformation. The common procedure to train ESNs is to randomly select the parameters of the state equation, and then to estimate those of the output equation via a standard least squares problem. Such a procedure is repeated for different instances of the random parameters characterizing the state equation, until satisfactory results are achieved. However, this trial-and-error procedure is not systematic and does not provide any guarantee about the optimality of the identification results. To solve this problem, we propose to complement the identification procedure of ESNs by applying results in scenario optimization. The resulting training procedure is theoretically sound and allows one to link precisely the number of identification instances to a guaranteed optimality bound on relevant performance indexes, such as the Root Mean Square error and the FIT index of the estimated model evaluated over a validation data-set. The proposed procedure is finally applied to the simulated model of a pH neutralization process: the obtained results confirm the validity of the approach.
Location Data and COVID-19 Contact Tracing: How Data Privacy Regulations and Cell Service Providers Work In Tandem

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A New Decentralized Certification Verification Privacy Control Protocol

Academic Certificates are a social convention that offers a forum for new knowledge about a person or an entity to be transmitted. Academic certificates are one of the areas of education in which the short-term application of blockchain technology can be seen. The practice of certificate fraud is common, undermining investment and trust in higher education systems and bearing substantial economic and social. Falsified qualifications vary from diplomas in high school to doctoral degrees. In addition, the credentials of several famous universities have been forged and it is very difficult to detect such forgeries. Hence, blockchain was introduced to improve the verification process of academic certificates. Recently, blockchain technology has emerged as a potential way of authenticating the process of document authentication and as an effective tool for combating document fraud and misuse. Blockchain based applications have been developed by various Universities. However, privacy preserving was a challenge in most of the existing solutions. Privacy indicates that the certificate protects both identity and information. Therefore , a new Decentralized Certification Verification Privacy Control Protocol is proposed. The proposed protocol can be used for issuing and verifying educational certificates while maintaining privacy and confidentiality at the network level (Ministries level, Universities level, Students level) and data level. The decentralized solution was implemented using Hyperledger Fabric as the infrastructure and Javascript as the contract language.
A hybrid neural variational CF-NADE for collaborative filtering using abstraction and generation

Generative models in some way reflect probability distributions over multiple variables. For latent variable generative models, the density function is intractable and these models thus make some independent assumptions to minimize the number of factors and, in turn, the number of parameters in the model. Autoregressive models such as Neural Autoregressive Distribution Estimator (NADE) have a tractable density with no latent variables and the goal is to learn a joint distribution over input variables. Autoregressive models have several advantages over latent variable models based on samples (ratings in our case) produced, independent assumptions, and tractable density functions. NADE models as such have seen less scrutiny in recommendation literature. Few papers have utilized NADE for collaborative filtering tasks but have limited capability as they could not perform abstraction, which restricts the model from learning accurate latent representations and the sampling rate was also slow. Further, the rating prediction process is calculative and not generative and model retraining is also required whenever a new user or new item gets added to the underlying dataset, limiting the model performance. Existing neural network based methods are deterministic and they can’t account for the uncertainty in user-item latent representations. To overcome these issues, we propose a hybrid neural Variational CF-NADE generative model that extends NADE for collaborative filtering tasks using variational Bayesian autoencoder to characterize the uncertainty in ratings. We do not make any independence assumption for the users or items rating vectors but use the default factorization to learn a joint distribution over ratings. Therefore, the proposed model uses a tractable density distribution to have good quality ratings generated. The model learns comprehensive user-item interaction function using abstraction and generation phases. The variational autoencoder performs abstraction on user-item metadata information which acts as a regularizer to the rating generated. By reusing the shared weights for fast sampling among different predicted ratings, we speed up the computation with an increased performance by decomposing the rating vector into conditionals by the chain rule and thus reducing the number of parameters. Experiments on three real-world datasets show that the proposed model beats recent state-of-the-art approaches for collaborative filtering tasks.