Reviewerly/exHarmony huggingface dataset:
Towards Efficient Human Action Retrieval Based on Triplet-Loss Metric Learning

Recent pose-estimation methods enable digitization of human motion by extracting 3D skeleton sequences from ordinary video recordings. Such spatio-temporal skeleton representation offers attractive possibilities for a wide range of applications but, at the same time, requires effective and efficient content-based access to make the extracted data reusable. In this paper, we focus on content-based retrieval of pre-segmented skeleton sequences of human actions to identify the most similar ones to a query action. We mainly deal with the extraction of content-preserving action features, which are learned using the triplet-loss approach in an unsupervised way. Such features are (1) effective as they achieve a similar retrieval quality as the features learned in a supervised way, and (2) of a fixed size which enables the application of indexing structures for efficient retrieval.
Recommender System Expedited Quantum Control Optimization

Quantum control optimization algorithms are routinely used to synthesize optimal quantum gates or to realize efficient state transfers. Computational resource required for optimization is an essential consideration in order to scale toward control of larger registers. Here, we propose and demonstrate use of a machine learning method, specifically recommender system (RS), to deal with challenge of enhancing computational efficiency. Given a sparse database of a set of products and their customer ratings, RS is used to efficiently predict unknown ratings. In the quantum control problem, each iteration of numerical optimization algorithm typically involves evaluating large number of parameters, such as gradients or fidelities, which can be tabulated as rating matrix. We establish that RS can rapidly and accurately predict elements of such sparse rating matrix. Using this approach, we expedite a gradient ascent algorithm namely GRAPE, and demonstrate the faster construction of two-qubit CNOT gate with up to 8 qubits.
BPF: An Effective Cluster Boundary Points Detection Technique

In a dataset, boundary points are located at the extremes of the clusters. Detecting such boundary points may provide useful information about the process and it can have many real-world applications. Existing methods are sensitive to outliers, clusters of varying densities and require tuning more than one parameter. This paper proposes a boundary point detection method called Boundary Point Factor (BPF) based on the outlier detection algorithm known as Local Outlier Factor (LOF). BPF calculates Gravity values and BPF scores by combining original LOF scores of all points in the dataset. Boundary points can be effectively detected by using BPF scores of all points where boundary points tend to have larger BPF scores than other points. BPF requires tuning of one parameter and it can be used with LOF to output outliers and boundary points separately. Experimental evaluation on synthetic and real datasets showed the effectiveness of our method in comparison with existing boundary points detection methods.
Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

The advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring (NILM). The development of robust AI and machine learning algorithms based on deep learning architecture has enabled accurate extraction of individual appliance load signature from aggregated energy data. However, the success rate of NILM algorithm in disaggregating individual appliance load signature in smart grid data violates the privacy of the individual household lifestyle. This paper investigates the performance of Sequence-to-Sequence (Seq2Seq) deep learning NILM algorithm in predicting the load signature of appliances. Furthermore, we define a new notion of disclosure risk to understand the risk associated with individual appliances in aggregated signals. Two publicly available energy disaggregation datasets have been considered. We simulate three inference attack scenarios to better ascertain the risk of publishing raw energy data. In addition, we investigate three activation extraction methods for appliance event detection. The results show that the disclosure risk associated with releasing smart grid data in their original form is on the high side. Therefore, future privacy protection mechanisms should devise efficient methods to reduce this risk.
Detectionof Fake Profiles in Online Social Networks – A Survey

In the today’s world, almost everybody from a child to an old-age person are spending a good amount of time on Online Social Networks (OSNs) by interacting and exchanging their information with the other people in the world. Because of the massive interconnectivity and the large amount of information sharing provided by OSN, many people also tend to misuse the platform of social networks. One of the ways in which people misuse the social networks is by creating several fake accounts for taking various types of immoral benefits such as targeting a particular user, or attempting any other cybercrime. This motivates the need fordeveloping a system that is able to identify fake accounts on the social networks. Researchers have made several attemptsfor detecting the fake/real accounts on social networkingsites by using various classification algorithms with various account’s features such as timebased, graphbased,contentbased, and user-based features. In this work,we have provided an overview of major studies done in this field along with the suggestions regarding what can be done in future in this field. We have summarized the recent advancements done in this field using machine learning and deep learning algorithms, which could help the future researcher to develop a foundation in this field.
Active Authentication Protocol for IoV Environment with Distributed Servers

The Internet of Vehicles (IoV) has evolved as an advancement over the conventional Vehicular Ad-hoc Networks (VANETs) in pursuing a more optimal intelligent transportation system that can provide various intelligent solutions and enable a variety of applications for vehicular traffic.Massive volumes of data are produced and communicated wirelessly among the different relayed entities in these vehicular networks, which might entice adversaries and endanger the system with a wide range of security attacks.To ensure the security of such a sensitive network, we proposed a distributed authentication mechanism for IoV based on blockchain technology as a distributed ledger with an ouroboros algorithm.Using timestamp and challenge-response mechanisms, the proposed authentication model can withstand several security attacks such as Man-in-Middle (MiM) attacks, Distributed Denial of Service (DDoS) attacks, server spoofing attacks and more.The proposed method also provides a solution for single-point failure, forward secrecy, revocability, etc.We exhibit the security of our proposed model by using formal (mathematical) analysis and informal analysis.We used Random Oracle Model to perform the mathematical analysis.In addition, we compared the communication cost, computation cost, and security of the proposed model with the related existing studies.We have verified the security of the model by using AVISPA tool simulation.The security analysis and computation analysis show that the proposed protocol is viable.
Object Relational Mapping Vs. Event-Sourcing: Systematic Review

Storing information in a software system is challenging, especially in the cloud computing era. Traditional, battle-tested methods, like Object Relational Mapping, seem inappropriate for all cases, but other alternatives feel too complicated to implement. Moreover, new software design methodologies, like Domain-Driven Design, provide alternative modeling tools for software systems, which in contrast to Object-Oriented Design, focus more on Domain-Events and their processing. Additionally, there is an increasing interest in causality when implementing software systems, mainly as a matter of accountability, tracing, and other similar properties, especially in the context of e-Government, in order to support transparency. We are now interested in a system's state history and its current state, and that is no longer the exception. Therefore, this paper focuses on Object Relational Mapping and Event-Sourcing trends for the past years as two alternatives for storing applications data through a systematic literature review. We evaluate how these two alternatives have evolved over the years under the prism of academic literature and discuss our findings, according to modern application development requirements.
Accurately Predicting User Registration in Highly Unbalanced Real-World Datasets from Online News Portals

Getting visitors to register is a crucial factor in marketing for online news portals. Current approaches are rule-based by awarding points for specific actions [3]. Finding efficient rules can be challenging and depends on the specific task. Registration is generally rare compared to regular visitors, leading to highly imbalanced data. We analyze different supervised learning classification algorithms under consideration of the data imbalance. As case study, we use anonymized real-world data from an Austrian newspaper outlet containing the visitor's session behavior with around 0.1% registrations over all visits. We identify an ensemble approach combining the Balanced Random Forest Classifier and the RUSBoost Classifier correctly identifying 76% of registrations over five independent data sets.
Digital Inequities and Societal Context: Digital Transformation as a Conduit to Achieve Social and Epistemic Justice

In society, digital technology has often been viewed as a solution to systemic inequalities. However, approaching digital technologies from a utility perspective places these tools at crossroads with broader social and contextual issues. This results in epistemological tensions rather than achieving the intended goal of institutional equity. South Africa is confronted with an unprecedented unequal access to quality education, particularly in rural and under-resourced communities. With this backdrop, the authors offer a theoretical critique of epistemological and ontological paradigms on the problematic complexities of digital transformation. They posit that digital transformation is critical to achieving social and epistemic justice, more so in the increasingly dynamic virtual spaces in education. Hence, this chapter offers constructive suggestions to further a paradigm shift on the myopic epistemic that imposes dichotomy in the higher education sector, thereby encumbering quality learning and development. Framing justice as equity and access allows for education to be viewed as a public good, hence the importance of creating commonplaces for knowledge development through constructivist-oriented practices. These commonplaces afford students unprecedented connections to widen their context of learning so to expand their perspectives, bypass gatekeepers to knowledge acquisition and create reciprocal relationships with their educators.
KGSR-GG: A Noval Scheme for Dynamic Recommendation

Recommendation algorithms regard user-item interaction as a sequence to capture the user's short-term preferences, but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts.In these years, combining the knowledge graph with sequential recommendation has gained momentum.The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations, rich association facts can increase the diversity of recommendations, and complex relational paths can hence the interpretability of recommendations.But the information in the knowledge graph, such as entities and relations, often fails to be fully utilized and high-order connectivity is unattainable in graph modelling in knowledge graph-based sequential recommender systems.To address the above problems, a knowledge graph-based sequential recommendation algorithm that combines the gated recurrent unit and the graph neural network (KGSR-GG) is proposed in the present work.Specifically, entity disambiguation in the knowledge graph is performed on the preprocessing layer; on the embedding layer, the TransR embedding technique is employed to process the user information, item information and the entities and relations in the knowledge graph; on the aggregation layer, the information is aggregated by graph convolutional neural networks and residual connections; and at last, on the sequence layer, a bi-directional gated recurrent unit (Bi-GRU) is utilized to model the user's sequential preferences.The research results showed that this new algorithm performed better than existing sequential recommendation algorithms on the MovieLens-1M and Book-Crossing datasets, as measured by five evaluation indicators.
Physical Layer Authentication Using Ensemble Learning Technique in燱ireless Communications

Cyber-physical wireless systems have surfaced as an important data communication and networking research area.It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies.Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation.Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks.Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target.These methods perform well but only in stationary scenarios.We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios.The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data.The authentication of the received signal is considered a binary classification problem.The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information.Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks.It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile.The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.
Sign Language Recognition using Convolutional Neural Network with Customization

Sign language is the communication language used by individuals with speech or hearing impairments. Systems that recognize sign language are used to automatically translate visual signs in video feeds to text. Even though sign language has become ubiquitous recently, it is still difficult for non-sign language speakers to connect with sign language users or signers. Normal speakers generally can not perceive sign language fluently, thus recognition systems can bridge that gap. This paper provides a system that is based on Computer vision and Convolutional neural networks. An end-to-end application is created to provide a customizable solution for each user with a graphic user interface.
A Novel Optimized Language-Independent Text Summarization Technique

A substantial amount of textual data is present electronically in several languages.These texts directed the gear to information redundancy.It is essential to remove this redundancy and decrease the reading time of these data.Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects.This paper proposes a language-independent extractive summarization technique.The proposed technique presents a clustering-based optimization technique.The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance.Experiments are devised and evaluated using BillSum dataset for the English language, MLSUM for German and Russian and Mawdoo3 for the Arabic language.The experiments are evaluated using ROUGE metrics.The results showed the effectiveness of the proposed technique compared to other language-dependent and languageindependent summarization techniques.Our technique achieved better ROUGE metrics for all the utilized datasets.The technique accomplished an F-measure of 41.9% for Rouge-1, 18.7% for Rouge-2, 39.4% for Rouge-3, and 16.8% for Rouge-4 on average for all the dataset using all three objectives.Our system also exhibited an improvement of 26.6%, 35.5%, 34.65%, and 31.54%w.r.t.The recent model contributed in the summarization of BillSum in terms of ROUGE metric evaluation.Our model's performance is higher than the compared models, especially in the metric results of ROUGE_2 which is bi-gram matching.
Anonymisation of Heterogeneous Graphs with Multiple Edge Types

Anonymisation is a strategy often employed when sharing and exchanging data that contains personal and sensitive information, to avoid possible record identification or inference. Besides the actual attributes contained within a dataset, also certain other aspects might reveal information on the data subjects. One example of this is the structure within a graph, i.e. the connection between nodes. These might allow to re-identify a specific person, e.g. by knowledge of the number of connections for some individuals within the dataset. Thus, anonymisation of the structure is an important aspect of achieving privacy. In this paper, we therefore present an algorithm that extends upon the current state of the art by considering multiple types of connections (relations) between nodes.
Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

Recently, smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources.Despite the advantages of smart cities, security remains a huge challenge to be overcome.Simultaneously, Intrusion Detection System (IDS) is the most proficient tool to accomplish security in this scenario.Besides, blockchain exhibits significance in promoting smart city designing, due to its effective characteristics like immutability, transparency, and decentralization.In order to address the security problems in smart cities, the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning (PPSF-BODL) model.The proposed PPSF-BODL model includes the collection of primary data using sensing tools.Besides, z-score normalization is also utilized to transform the actual data into useful format.Besides, Chameleon Swarm Optimization (CSO) with Attention Based Bidirectional Long Short Term Memory (ABiLSTM) model is employed for detection and classification of intrusions.CSO is employed for optimal hyperparameter tuning of ABiLSTM model.At the same time, Blockchain (BC) is utilized for secure transmission of the data to cloud server.This cloud server is a decentralized, distributed, and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSF-BODL model over recent approaches with a maximum accuracy of 97.46%.5300
Analytical Algebra: Extension of Relational Algebra

In the context of contemporary data, the processing of information is crucial. This paper proposes an extension to the traditional database relational algebra, which enriches the data model and provides additional complex-data operations. Specifically, we focus on analytical operators from the areas of data mining and similarity search, such as frequent pattern mining or similarity search queries. The proposed approach can be easily extended by additional algebraic operators. To demonstrate the capabilities of our analytical algebra, we show three practical use cases with different levels of the expression complexity.
Impulsivity — Reflexivity and Neuroefficiency of Intellectual Activity

A brief review of current research into the problem of cognitive neuroefficiency revealed a large array of conflicting data. Some studies provide evidence of lower energy consumption and faster cognitive activity in respondents with high IQ, while others show higher energy consumption and slower speed. Other datasets indicate that respondents with high intelligence are more reflexive than those with low intelligence. The heterogeneity of the samples is indicated as a key reason for the inconsistency of the data. This study aimed to compare behavioral and electroencephalographic data in groups of respondents homogeneous in terms of (a) sex, (b) age, and (c) manifestations of the cognitive style Impulsivity-Reflexivity. The study sample consisted of three age groups: 92 people aged 13.73 ± 0.45 years (44.5% of men), 123 people aged 15.46 ± 0.66 years (52% of men), 36 people aged 21.11±2.87 years (44.4% of men). The diagnostic procedure included the collection of behavioral data (Cognitive Personality Style Questionnaire CPS-Q, Raven’s SPM, Amtrhauer’s Intelligence Structure Test, Rusalov’s Personality Formal Dynamic Properties Questionnaire) and data on the bioelectrical activity of the cerebral cortex at rest and during the detection of similar figures J. Kagan. Comparative data analysis (U-test; H-test) showed that women demonstrate significantly higher intelligence indicators, detect similar figures with greater accuracy, but at the same time expend more energy in terms of the EEG power spectrum, compared with men. Significant differences in intelligence and Impulsivity-Reflexivity among age groups were not found. However, a significant decrease in the EEG power spectrum was revealed under electrodes O1, C3, Oz, Pz, CP4 while solving Kagan’s tasks from the younger to the older age group. A paradoxical result was obtained when comparing groups of respondents significantly different in terms of the Impulsivity-Reflexivity of the Kagan test. Significant differences in the EEG power spectrum, CPS-Q Impulsivity and Reflexivity scales, and intelligence were not found. A hypothesis was put forward about the false discriminant validity of the Kagan test for respondents with low IQ.