Reviewerly/exHarmony huggingface dataset:
From Things into Clouds – and back

The concept of the cloud-to-things continuum addresses advancements made possible by the widespread adoption of cloud, edge, and IoT resources. It opens the possibility to combine classical symbolic AI and advanced machine learning approaches in a meaningful way. In this paper we present a thing registry and an agent-based orchestration framework which we combine to support semantic orchestration of IoT use cases across several federated cloud environments. We illustrate the approach with a use case from an assisted living scenario.
ARTS AND MACHINE CIVILIZATION INTERNATIONAL SCIENTIFIC CONFERENCE

The text reviews the Arts and Machine Civilization International Scientific Conference. The conference took place on March 30—April 2, 2021, and was organized by the State Institute for Art Studies, GITR Film and Television School, and the Saint Petersburg State University. SIAS has been hosting conferences on contemporary culture, screen art and television for 17 years. This year, for the first time in the history of such forums, the researchers were tasked with analyzing the new things that machines have brought to the arts and, in general, to human life. The conference took its special place among the forums held over the past year in Russia and abroad in the following areas: artificial intelligence (Artificial Intelligence Journey, Moscow,Russia); machine learning (International Conferenceon Machine Learning, Vienna, Austria; 3rd International Conference on Machine Learning and Machine Intelligence, Hangzhou, China); civilization of knowledge (Civilization of Knowledge: Russian Realities, Moscow, Russia), etc. The novelty of the conference lies in the unification of the seemingly incompatible phenomena: art and machine civilization. As is commonly known, art was traditionally opposed to technology as something alien, sometimes hostile, although the both were born in human mind and created by human hands. Until now, the expression “machine civilization” in art has been used mainly in the genre of fantasy and with an emphasis on its negative connotations. The purpose of the conference was to comprehend the artistic practices in the era of machine civilization, get acquainted with current hypotheses, publish new facts and discuss modern terminologies (law of spontaneity, law of semantic uncertainty, algorithmic apophenia, post-opera, artificial life and new vitality). Along with the study of new challenges, old issues were raised, which became in demand in the machine civilization: originals and copies of artworks, the boundaries of conventionality and overcoming distrust in new media, narratives and poetics in serious and entertaining screen genres. The conference reports were divided into six blocks: Theoretical Models, ScreenArts—Cinema, Fine Arts, Music, PC Games, and Digitalization.
Budget and SLA Aware Dynamic Workflow Scheduling in Cloud Computing with Heterogeneous Resources

Workflow with different patterns and sizes arrive at a cloud data center dynamically to be processed at virtual machines in the data center, with the aim to minimize overall cost and makespan while satisfying Service Level Agreement (SLA) requirement. To efficiently schedule workflows, manually designed heuristics are proposed in the literature. However, it is time consuming to manually design heuristics. The designed heuristics may not work effectively for heterogeneous workflow since only simple problem related factors are considered in the heuristics. Further, most of the existing approaches ignore the deadline constraints set in SLAs. Genetic Programming Hyper Heuristic (GPHH) can be used to automatically design heuristics for scheduling problems. In this paper, we propose a GPHH approach to automatically generate heuristics for the dynamic workflow scheduling problem, with the goal of minimizing the VM rental fees and SLA penalties. Experiments have been conducted to evaluate the performance of the proposed approach. Compared with several existing heuristics and conventional Genetic Programming (GP) approaches, the proposed Dynamic Workflow Scheduling Genetic Programming (DWSGP) has better performance and is highly adaptable to variations in cloud environment.
Analysis and Implementation of Threat Agents Profiles in Semi-Automated Manner for a Network Traffic in Real-Time Information Environment

Threat assessment is the continuous process of monitoring the threats identified in the network of the real-time informational environment of an organisation and the business of the companies. The sagacity and security assurance for the system of an organisation and company’s business seem to need that information security exercise to unambiguously and effectively handle the threat agent’s attacks. How is this unambiguous and effective way in the present-day state of information security practice working? Given the prevalence of threats in the modern information environment, it is essential to guarantee the security of national information infrastructure. However, the existing models and methodology are not addressing the attributes of threats like motivation, opportunity, and capability (C, M, O), and the critical threat intelligence (CTI) feed to the threat agents during the penetration process is ineffective, due to which security assurance arises for an organisation and the business of companies. This paper proposes a semi-automatic information security model, which can deal with situational awareness data, strategies prevailing information security activities, and protocols monitoring specific types of the network next to the real-time information environment. This paper looks over analyses and implements the threat assessment of network traffic in one particular real-time informational environment. To achieve this, we determined various unique attributes of threat agents from the Packet Capture Application Programming Interface (PCAP files/DataStream) collected from the network between the years 2012 and 2019. We used hypothetical and real-world examples of a threat agent to evaluate the three different factors of threat agents, i.e., Motivation, Opportunity, and Capability (M, O, C). Based on this, we also designed and determined the threat profiles, critical threat intelligence (CTI), and complexity of threat agents that are not addressed or covered in the existing threat agent taxonomies models and methodologies.
Email Spam Filtering Using Machine Learning Based Xgboost Classifier Method

E-mail spam, known as undesirable Bulk E-mail (UBE), junk mail, or undesirable commercial e-mail (UCE), is transferring undesirable e-mail information, usually with business data, in large amounts to a confused set of recipients. Spam is standard on the Internet because automated communications' transaction costs are lower than other alternative forms of communication. Many spam filters use various approaches to recognize the incoming message as spam, varying from white list/blacklist, Bayesian review, keyword matching, postage, mail header analysis, enactment, etc. Even though we are still involved in spam e-mails every day, this paper proposed an enhanced spam exposure design based on Extreme Gradient Boosting (XGBoost) model. It is studied for increased accuracy in spam detection. To the best of our experience, it has expected minor considerations spam e-mail detection difficulties.  We explore the proposed system's performance using a more comprehensive range of experimental metrics behind the accuracy, which has managed the existed investigations. The proposed algorithm comparing with existed classifiers of SVM, CNSA-FFO, Rotation forest, MLP, J48, and Naive Bayes. The proposed model gets better accuracy with 95% when compared with previous classifiers
Optimal Extraction of Photovoltaic Cell Parameters for the Maximization of Photovoltaic Power Output Using a Hybrid Particle Swarm Grey Wolf Optimization Algorithm

Avoiding over-dependency on the oil-fired energy supply systems motivates many countries to integrate renewable energy into the existing energy supply systems. Solar Photovoltaic technology forms the most promising option for developing such a cost-effective and sustainable energy supply system. Generally, the current-voltage curve is used in the performance assessment and analysis of the Photovoltaic module. The accuracy of the equations for the curve depends on accurate cell parameters. However, the extraction of these parameters remains a complex stochastic nonlinear optimization problem. Many studies have been carried out to deal with such problem but still more researches need to be carried out to achieve a minimum error and a high accuracy. The existing researches ignored the variation in the meteorological data though it has a significant impact on the problem design. In this study, the Sample Average Approximation was employed to deal with the uncertainty and the hybrid optimization method was used to get the optimal parameters. The results showed that the Hybrid PSO-GWO produced the most optimal solution: Series resistance (1.4623), Shunt resistance(215.0000), Ideal diode factors (n1 = 0.9500, n2 = 1.6500) with a maximum PV power of 59.850W. The methodology produced realistic results since the variability is dealt with and the Hybrid PSO-GWO finds the optimal solution at a higher convergence rate.
Multi-criteria assessment of user trust in Social Reviewing Systems with subjective logic fusion

By now people’s opinions and actions are more and more strongly influenced by what is posted and shared on the various social networks. Thus, malicious users can purposely manipulate other users posting fake news/reviews. In order to face this challenge, modern online social networks are beginning to adopt tool for user trustworthiness assessment. Current assessment solutions mainly adopt multi-criteria frameworks for user trustworthiness assessment but fail at properly dealing with uncertainty and vagueness in computed/collected scores and aggregating them in a robust manner. In this paper, we propose a larger set of criteria than existing related works, and the use of subjective logic to represent and combine subjective and objective scores. Specifically, several of assessment criteria are introduced for verifying user trust from different point of views (usefulness and quality of user reviews, users’ influence/importance in terms of activities and centrality within the social network, time dependent crown consensus investigating aspect-based sentiments and opinions of reviews w.r.t. the majority), aiming at improving accuracy and precision in trust estimation. The available fusion operators in the literature of subjective logic have been compared so as to find the best one fitting the needs of trust estimation. The proposed solution has been implemented and evaluated against public Yelp data-sets so as to prove its effectiveness and efficiency w.r.t. existing related works within the literature.
AgeFlow: Conditional Age Progression and Regression with Normalizing Flows

Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively. Existing generative adversarial networks (GANs) based methods suffer from the following three major issues: 1) unstable training introducing strong ghost artifacts in the generated faces, 2) unpaired training leading to unexpected changes in facial attributes such as genders and races, and 3) non-bijective age mappings increasing the uncertainty in the face transformation. To overcome these issues, this paper proposes a novel framework, termed AgeFlow, to integrate the advantages of both flow-based models and GANs. The proposed AgeFlow contains three parts: an encoder that maps a given face to a latent space through an invertible neural network, a novel invertible conditional translation module (ICTM) that translates the source latent vector to target one, and a decoder that reconstructs the generated face from the target latent vector using the same encoder network; all parts are invertible achieving bijective age mappings. The novelties of ICTM are two-fold. First, we propose an attribute-aware knowledge distillation to learn the manipulation direction of age progression while keeping other unrelated attributes unchanged, alleviating unexpected changes in facial attributes. Second, we propose to use GANs in the latent space to ensure the learned latent vector indistinguishable from the real ones, which is much easier than traditional use of GANs in the image domain. Experimental results demonstrate superior performance over existing GANs-based methods on two benchmarked datasets. The source code is available at https://github.com/Hzzone/AgeFlow.
A Model for Trend Analysis in the Online Shopping Scenario Using Multilevel Hesitation Pattern Mining

The present paper proposes a new model for the exploration of hesitated patterns from multiple levels of conceptual hierarchy in the transactional dataset. The usual practice of mining patterns has focused on identifying frequent patterns (i.e., which occur together) in the transactional dataset but uncovers the vital information about the patterns which are almost frequent (but not exactly frequent) called “hesitated patterns.” The proposed model uses the reduced minimum support threshold (contains two values: attractiveness and hesitation) and constant minimum confidence threshold with the top-down progressive deepening approach for generating patterns and utilizing the apriori property. To validate the model, an online purchasing scenario of books through e-commerce-based online shopping platforms such as Amazon has been considered and shown that how the various factors contributed towards building hesitation to purchase a book at the time of purchasing. The present work suggests a novel way for deriving hesitated patterns from multiple levels in the conceptual hierarchy with respect to the target dataset. Moreover, it is observed that the concepts and theories available in the existing related work Lu and Ng (2007) are only focusing on the introductory aspect of vague set theory-based hesitation association rule mining, which is not useful for handling the patterns from multiple levels of granularity, while the proposed model is complete in nature and addresses the very significant and untouched problem of mining “multilevel hesitated patterns” and is certainly useful for exploring the hesitated patterns from multiple levels of granularity based on the considered hesitation status in a transactional dataset. These hesitated patterns can be further utilized by decision makers and business analysts to build the strategy on how to increase the attraction level of such hesitated items (appeared in a particular transaction/set of transactions in a given dataset) to convert their state from hesitated to preferred items.
Vehicle Detection in Remote Sensing Image Based on Machine Vision

Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.
A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification

Particle swarm optimization (PSO) has been widely used for feature selection (FS) in classification. However, FS is still a challenging optimization task for PSO when the dimensionality of data is high. In this paper, we propose a forward search inspired PSO (FSIPSO) algorithm to build a wrapper-based FS method. In FSIPSO, the search space dynamically changes during the evolutionary process. Specifically, we rank the features according to their single-feature classification performance and divide the search space into several sub-spaces. A forward search scheme is proposed to sequentially select the sub-spaces. The selected sub-spaces construct the search space for FSIPSO. With this scheme, FSIPSO first searches in a small space to quickly find candidate solutions (feature subsets) with relatively good performance. Then, the search space expands with the selection of more sub-spaces, and FSIPSO can further select informative features in the expanded search space. Moreover, mutation operations are used in FSIPSO to avoid the premature problem. The experimental results on 8 UCI datasets have shown that FSIPSO obtains better FS results with less computation time compared with benchmark PSO-based FS methods. FSIPSO also obtains better convergence performance than these methods.
MailTrout: A Machine Learning Browser Extension for Detecting Phishing Emails

The onset of the COVID-19 pandemic has given rise to an increase in cyberattacks and cybercrime, particularly with respect to phishing attempts.Cybercrime associated with phishing emails can significantly impact victims, who may be subjected to monetary loss and identity theft.Existing anti-phishing tools do not always catch all phishing emails, leaving the user to decide the legitimacy of an email.The ability of machine learning technology to identify reoccurring patterns yet cope with overall changes complements the nature of anti-phishing techniques, as phishing attacks may vary in wording but often follow similar patterns.This paper presents a browser extension called MailTrout, which incorporates machine learning within a usable security tool to assist users in detecting phishing emails.MailTrout demonstrated high levels of accuracy when detecting phishing emails and high levels of usability for end-users.Phishing.
A COMPARATIVE ANALYSIS OF TRANSLATION SOFTWARE USING DATA SCIENCE APPROACH TO ARABIC STATEMENTS

Readers who find difficulty in Understanding any language take the help of translator. In this era human translators are now replaced by translation software.  Many students are using translation software for translating English texts into Arabic. However often these software Applications do not convey the real sense of the original text. It can be misleading several times. In this research an attempt is made to evaluate the reliability of different translation software while translating English into Arabic statements. The Machine translating is Compared with human Translation. Set of statements with three categories, named as simple, moderate and difficult levels. The comparative study has been conducted and the results have been discussed.
A Blockchain-Based Microgrid Data Disaster Backup Scheme in Edge Computing

In view of the problems of low security, poor reliability, inability to backup automatically, and overreliance on the third party in traditional microgrid data disaster backup schemes based on cloud backup, the edge computing is used to preprocess power big data, and a microgrid data disaster backup scheme based on blockchain in edge computing environment is proposed in this paper. First, the honey encryption (HE) technology and advanced encryption standard (AES) are combined to propose a new encryption algorithm HE-AES, which is used to encrypt the preprocessed data. Second, the Kademlia algorithm is embedded in the edge server to realize the distributed storage and automatic recovery of microgrid data. Finally, the traditional proof of authority (PoA) consensus mechanism is improved partially, and the improved PoA is used to make each node reach consensus and pack blocks on the chain. The scheme can not only realize the data disaster backup automatically but also has high efficiency of data processing, which can provide a new idea for improving the current data disaster backup schemes.
Learning Graph Convolutional Network for Blind Mesh Visual Quality Assessment

This paper proposes a new method for blind mesh visual quality assessment (MVQA) based on a graph convolutional network. For that, we address the node classification problem to predict the perceived visual quality. First, two matrices representing the 3D mesh are considered: a graph adjacency matrix and a feature matrix. Both matrices are used as input to a shallow graph convolutional network. The network consists of two convolutional layers followed by a max-pooling layer to provide the final feature representation. With this structure, the Softmax classifier predicts the quality score category without the reference mesh's availability. Experiments are conducted on four publicly available databases constructed explicitly for the mesh quality assessment task. We investigate several perceptual and visual features to select the most effective combination. Comparisons with the state-of-the-art alternative methods show the effectiveness of the proposed framework.
Progressive Convolutional Neural Network for Incremental Learning

In this paper, we present a novel incremental learning technique to solve the catastrophic forgetting problem observed in the CNN architectures. We used a progressive deep neural network to incrementally learn new classes while keeping the performance of the network unchanged on old classes. The incremental training requires us to train the network only for new classes and fine-tune the final fully connected layer, without needing to train the entire network again, which significantly reduces the training time. We evaluate the proposed architecture extensively on image classification task using Fashion MNIST, CIFAR-100 and ImageNet-1000 datasets. Experimental results show that the proposed network architecture not only alleviates catastrophic forgetting but can also leverages prior knowledge via lateral connections to previously learned classes and their features. In addition, the proposed scheme is easily scalable and does not require structural changes on the network trained on the old task, which are highly required properties in embedded systems.
Joint Universal Syntactic and Semantic Parsing

While numerous attempts have been made to jointly parse syntax and semantics, high performance in one domain typically comes at the price of performance in the other. This trade-off contradicts the large body of research focusing on the rich interactions at the syntax–semantics interface. We explore multiple model architectures that allow us to exploit the rich syntactic and semantic annotations contained in the Universal Decompositional Semantics (UDS) dataset, jointly parsing Universal Dependencies and UDS to obtain state-of-the-art results in both formalisms. We analyze the behavior of a joint model of syntax and semantics, finding patterns supported by linguistic theory at the syntax–semantics interface. We then investigate to what degree joint modeling generalizes to a multilingual setting, where we find similar trends across 8 languages.