Reviewerly/exHarmony huggingface dataset:
Ontological Model for Scientific Institutions Information Representation

The paper presents an approach to ontological model development for representing information accumulated by scientific institutions. The ontology allows to structure and organize such in-formation for later use in the process of evaluating the performance of an institution. The general ontology is represented by a system of interconnected components, each allowing to de-scribe the relevant subprocess within both the activity of the institution itself and the process of its evaluation by external organizations and ministries. The elements of all the compo-nents of the ontology system are described. The practical devel-opment of the proposed ontological model was performed using the ontological solutions development platform TEDAOS.
Performance of Statistical Stegdetectors in Case of Small Number of Stego Images in Training Set

Today special interest is taken to early detection and counteraction to hidden (steganographic) communication between intruders. Such communication based on message embedding into files, for instance digital images, that are processed and transmitted in a communication system. The wide range of steganalysis methods for revealing of formed stego images was developed. Proposed stegdetectors allows achieving high detection accuracy (more than 95%) for most steganographic methods when steganalyst has access to the embedding algorithm. In case of limited a priori information about used steganographic method, when steganalyst can use only a small amount of stego images during stegdetector tuning, detection accuracy may decreases drastically. The paper is devoted to the performance analysis of state-of-the-art stegdetector based on maxSRMd2 statistical model of cover image by limited quantity of available stego images. The case of adaptive embedding of stegodata in cover image according to modern HUGO and S-UNIWARD method is considered. The obtained results indicate a strong dependence of stegdetector performance on number of cover-stego images pairs at detector setup stage, which puts forward additional requirements for training set during detectors tuning.
Significant of Gradient Boosting Algorithm in Data Management System

Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.
Explainable AI for Classifying Devices on the Internet

Devices reachable on the Internet pose varying levels of risk to their owners and the wider public, depending on their role and functionality, which can be considered their class. Discussing the security implications of these devices without knowing their classes is impractical. There are multiple AI methods to solve the challenge of classifying devices. Since the number of significant features in device HTTP response was determined to be low in the existing word-embedding neural network, we elected to employ an alternative method of Naive Bayes classification. The Naive Bayes method demonstrated high accuracy, but we recognise the need to explain classification results to improve classification accuracy. The black-box implementation of Artificial Neural Networks has been a serious concern when evaluating the classification results produced in most fields. While devices on the Internet have historically been classified manually or using trivial fingerprinting to match major vendors, these are not feasible anymore because of an ever-increasing variety of devices on the Internet. In the last few years, device classification using Neural Networks has emerged as a new research direction. These research results often claim high accuracy through the validation employed, but through random sampling there always occur devices that cannot be easily classified, that an expert intuitively would classify differently. Addressing this issue is critical for establishing trust in classification results and can be achieved by employing explainable AI. To better understand the models for classifying devices reachable on the Internet and to improve classification accuracy, we developed a novel explainable AI method, which returns the features that are most significant for classification decisions. We employed a Local Interpretable Model-Agnostic Explanations (LIME) framework toexplain Naive Bayes model classification results, and using this method were able to further improve accuracy with a better understanding of the results.
Detecting Semantic-based Similarity Between Verses of The Quran with Doc2vec

Semantic similarity analysis in natural language texts is getting great attention recently. Semantic analysis of the Quran is especially challenging because it is not simply factual but encodes subtle religious meanings. Investigating similarity and relatedness between the Quranic verses is a hot topic and can promote the acquisition of the underlying knowledge. Therefore, we use an NPL method to detect the semantic-based similarity between the verses of the Quran. The idea is to exploit the distributed representation of text, to learn an informative representation of the Quran's passages. We map the Arabic Quranic verses to numerical vectors that encode the semantic properties of the text. We then measure similarity among those vectors. The performance of our model is judged through cosine similarity between our assigned semantic similarity scores and annotated textual similarity datasets. Our model scored 76% accuracy on detecting the similarity, and it can act as a basis for potential experiments and research.
Analytical Modeling and Prediction of Cloud Workload

Cloud workload prediction is a very critical task for elastic scaling, because cloud manager decides what configuration sequence is to be considered for resource provisioning. Matching the demand guarantees Service Level Agreement condition (SLA) and Quality of Service (QoS) performance. Many techniques have been used in literature for workload prediction that show accepted accuracy but all of them have some limitation and challenges, like modeling architecture, training, validation and tuning. In this work we propose a subjective analytical model that studies the cloud workload, and formulates a statistical/mathematical model to describe the workload behavior. Workloads are generated from different sources, such as regular applications running on cloud, or data log from Internet of Things (IoT) systems to monitor and control physical systems. Real time cyber-physical system requires high accuracy provisioning for resources to prevent any performance degradation. Predicting and modeling different types of cloud workloads enhances system performance and increases its accuracy. This work considers cloud workload modeling with excellent prediction values and less training cost compared with other methods.
BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine

Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames.We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks.This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.
A provably secure content distribution framework for portable DRM systems

The growing demand for digital content using smart devices has taken the attenuation towards the enforcement of rights in digital content distribution. Digital rights management (DRM) systems support copyrights and try to control the access of digital content in a user-friendly way. However, user-friendly systems should also support user’s rights along with system requirements. The portable DRM architecture has the potential to support user’s rights. One of the critical challenges in portable DRM systems is the anonymous and secure delivery of digital content. To address this issue, a smart card-based content distribution framework is presented, which supports mutual authentication and secure session establishment. The security proof of the proposed scheme is presented in a random oracle model with rigorous informal security analysis, which signals that the proposed scheme has desirable attributes of security. Moreover, the security analysis is also performed using a widely adopted simulation tool, namely, “Automated Validation of Internet Security Protocol and Application (AVISPA)”. The study of performance has been conducted, which signals that the proposed scheme is also addressing the requirements of efficiency.
Universal Graph-Based Scheduling for Quantum Systems

High fidelity operation of a quantum system requires precise tuning of control parameters. Calibration of a quantum system is often achieved by running complex series of dependent experiments and a full system calibration can require tens of calibration experiments to complete. Optimal control parameters drift over time, and components of experimental quantum systems are susceptible to failure. Hence, continuous operation of a quantum system requires automated background processes such as frequent recalibration and monitoring. In this article, we present a scheduling toolkit that schedules experiments based on a directed acyclic graph using a configurable traversal algorithm. Our scheduler can be triggered from any process, enabling universal feedback between the scheduler and the quantum control system. To demonstrate the capabilities of our system, we implemented a complex system calibration algorithm based on our scheduling toolkit.
Compressed image restoration via deep deblocker driven unified framework

As a typical ill-posed problem, JPEG compressed image restoration (CIR) aims to recover a high-quality (HQ) image from the compressed version. Although many model-based and learning-based methods have been proposed for conventional image restoration (IR), proposing a general and effective framework for various CIR tasks is still a challenging work. The model-based methods are flexible for handling different IR tasks, but they suffer from high complexity and the difficulty in designing sophisticated priors. The learning-based methods have shown promising results in various IR tasks. However, most of them need to retrain their models for each IR task separately, which sacrifices methods’ flexibility. In this paper, we propose a novel and high-performance deep deblocker driven unified framework to flexibly address various CIR tasks without retraining. First, a novel fidelity (NF) is introduced into CIR, and then the CIR problem is divided into inversion and deblocking subproblems by our improved split Bregman iteration (ISBI) algorithm. Next, we design a set of compact yet effective deep deblockers. Since simultaneously modeling the data fidelity term and implicit priors via deblockers is necessary, these deblockers are used as implicit priors and also used for NF in the CIR problem. The convergence of our method is proved as well. To the best of our knowledge, our method is the first work to use deblockers as implicit priors, and it could also contribute to other deblocking methods to obtain better flexibility. The effectiveness of the proposed method is demonstrated both visually and quantitatively.
A New Automatic Charging System for Electric Vehicles

With the continuous advancement of new energy technologies, although the market share of electric vehicles has gradually increased in recent years, due to the problem of charging time, it is difficult to charge electric vehicles. So far, there are no robots on the market that can automatically complete the charging process for different models, because a single robotic arm can only insert and remove the charging gun at a fixed position, and the charging port position of different models is different. This paper proposes a mobile robot charging system based on computer vision, which consists of a rail and a mobile robot equipped with a mechanical arm. First, the vehicle model is judged by the camera mounted on the mobile robot, the corresponding position of the charging port is found on the track, and then the robot is moved to this position, and the insertion and removal of the charging gun is completed through the camera and the mechanical arm. This system has verified the feasibility and the robustness of the algorithm through visual recognition and prototype testing.
Cybersecurity Risk Assessment of Information Systems of Critical Infrastructure Objects

The concept of complex cybersecurity risk of information systems of critical infrastructure objects is substantiated in the paper, the vector model of risk and model of complex risk is offered, the method of calculation of total risk, complex risk is offered, the structural decision of computer system for calculation of cybersecurity risk of information systems of critical infrastructure objects is developed. Using the proposed method, it is possible to solve the issue related to the possibility of calculating the amount of risks, which allows to assess the risk as a whole, taking into account the human factor in risk assessment, which is extremely important for critical infrastructure, especially in the energy sector. The proposed computing system can be used as part of a decision support system for assessing cybersecurity risks of information systems of critical infrastructure facilities. The results obtained can be used to assess the cybersecurity risk of information systems of critical infrastructure objects in the construction and implementation of information security management systems, integrated information protection systems in automated systems in the development of a threat model, security policy, and protection plan. Despite a significant number of approaches to solving this problem, it remains relevant for the entire world community.
Visual object tracking using Fourier domain phase information

In this article, phase of the Fourier transform (FT), which has observed to be a crucial component in image representation, is utilized for visual target tracking. The main aim of the proposed scheme is to reduce the computational complexity of cross-correlation-based matching frameworks. Normalized cross-correlation (NCC) function-based object tracker is converted to a phase minimization problem under the following assumption: In visual object tracking applications, if the frame rate is high, the moving object can be considered to have translational shifts in image domain in a small time window. Since the proposed tracking framework works in the Fourier domain, the translational shifts in the image space are converted to phase variations in the Fourier domain due to the "translational invariance" property of the FT. The proposed algorithm estimates the spatial target position based on the phase information of the target region. The proposed framework uses the $$\ell _1$$ -norm and provides a computationally efficient solution for the tracking problem. Experimental studies indicate that the proposed phase-based technique obtain comparable results with baseline tracking algorithms which are computationally more complex.
A study on the Effect of Occlusion in Human Activity Recognition

The problem of occlusion plays a crucial role in real-life human activity recognition applications. However, most research works either underestimate it, or base their training solely on datasets shot under laboratory conditions, i.e., without any partly or full occlusion. In this work we perform a study on the effect of occlusion in the task of human activity recognition and the domains of the recognition of a) activities of daily living; and b) medical conditions. Throughout our experiments we use a convolutional neural network that has been trained using a 2D representation of skeleton motion for all available joints, i.e., without using any occluded samples. We evaluate our approach using two challenging, publicly available human motion datasets upon removing one or more body parts.
Instance elimination strategy for non-convex multiple-instance learning using sparse positive bags

In some multiple instance learning (MIL) applications, positive bags are sparse (i.e. containing only a small fraction of positive instances). To deal with the imbalanced data caused by these situations, we present a novel MIL method based on a small sphere and large margin approach (SSLM-MIL). Due to the introduction of a large margin, SSLM-MIL enforces the desired constraint that for all positive bags, there is at least one positive instance in each bag. Moreover, our framework is flexible to incorporate the non-convex optimization problem. Therefore, we can solve it using the concave–convex procedure (CCCP). Still, CCCP may be computationally inefficient for the number of external iterations. Inspired by the existing safe screening rules, which can effectively reduce computational time by discarding some inactive instances. In this paper, we propose a strategy to reduce the scale of the optimization problem. Specifically, we construct a screening rule in the inner solver and another rule for propagating screened instances between iterations of CCCP. To the best of our knowledge, this is the first attempt to introduce safe instance screening to a non-convex hypersphere support vector machine. Experiments on thirty-one benchmark datasets demonstrate the safety and effectiveness of our approach.
A Novel approach towards Implicit Authentication System by using Multi-share visual key Cryptography Mechanism

Abstract Currently huge amount of data used to stored, extracted and transacted via various stand alone and internet based applications. These applications are extended towards the verticals like huge databases, data warehouses, cloud computing services and various client-server applications. In all these applications important data used to float day in day out. Therefore preserving user authentication &amp; access control is extremely important aspect of information security. Here an attempt is made to generate an implicit authentication system using multi-share visual key cryptography which will generate strong password keys by using images. Initially various images will be fused to form a resultant image than on this fused visual key cryptography will be performed which will provide multiple shares, out of these one of the share is selected to generate strong password strings/keys to accomplish the task of access control or user authentications.
Application of NLP for Information Extraction from Unstructured Documents

Pudasaini, Shushanta Shakya, Subarna Lamichhane, Sagar Adhikari, Sajjan Tamang, Aakash Adhikari, SujanThe world is intrigued by data. In fact, huge capitals are invested to devise means that implements statistics and extract analytics from these sources. However, when we examine the studies performed on applicant tracking systems that retrieve valuable information from candidates’ CVs and job descriptions, they are mostly rule-based and hardly manage to employ contemporary techniques. Even though these documents vary in contents, the structure is almost identical. Accordingly, in this paper, we implement an NLP pipeline for the extraction of such structured information from a wide variety of textual documents. As a reference, textual documents which are used in applicant tracking systems like CV (Curriculum Vitae) and job vacancy information have been considered. The proposed NLP pipeline is built with several NLP techniques like document classification, document segmentation and text extraction. Initially for the classification of textual documents, support vector machines (SVM) and XGBoost algorithms have been implemented. Different segments of the identified document are categorized using NLP techniques such as chunking, regex matching and POS tagging. Relevant information from every segment is further extracted using techniques like Named Entity Recognition (NER), regex matching and pool parsing. Extraction of such structured information from textual documents can help to gain insights and use those insights in document maintenance, document scoring, matching and auto-filling forms.
Enhancement of OPTICS’ time complexity by using fuzzy clusters

Density-Based clustering are the main clustering algorithms because they can cluster data with different shapes and densities, but some of these algorithms have high time complexity like OPTICS (Ordering Points to Identify Clustering Structure) and DBSCAN (Density-based spatial clustering of applications with noise) where the time complexity up to O(n2). In this paper, we use an approach to reduce this complexity by providing fuzzy clusters to OPTICS, which make the process of finding neighbours within an only narrow region (fuzzy group) instead of searching all the state space making the algorithm faster than the original one with keeping almost the same accuracy of the innovative algorithm. The results show that there is an improvement in execution time using some synthetic and real datasets with high dimensions.