Reviewerly/exHarmony huggingface dataset:
Identification of Heavy Metal Compounds in Technogenically Transformed Soils Using Sequential Fractionation, XAFS Spectroscopy, and XRD Powder Diffraction

Abstract The transformation of technogenic Cu and Zn compounds in technogenically transformed soils (Spolic Technosols) with high and very high concentrations of metals formed at the site of a natural tailings pond in the floodplain of the Seversky Donets River, the main tributary of the Don River (Rostov oblast, Russia) has been studied. The Technosols are compared to an unpolluted meadow-chernozemic soil (Fluvisol) outside the impact zone. The state of Cu and Zn is assessed using three sequential extraction schemes—Miller’s, Tessier’s, and BCR, as well as synchrotron X-ray powder diffraction (XRD) and analysis of synchrotron X-ray absorption spectrometry (XAFS) spectra. It is shown that the distribution of metals in soil is largely related to their properties, such as electronegativity, hydrolyzability, and softness parameter. As is observed, Cu mainly concentrates in the residual fraction (to 42%) and in the fraction associated with organic matter (up to 27%). The mobility of Zn in the studied soils is higher than that of Cu. Its main part (up to 56%) is in the residual fraction and the fraction associated with Fe and Mn oxides (up to 48%), especially with Fe(III) crystalline compounds. The combination of a three-stage BCR scheme with XAFS and XRD techniques is used for the first time. Most of the peaks in diffraction patterns of soil samples after the first and second extraction stages correspond to the authigenic sulfur-containing minerals, namely, wurtzite (ZnS with a hexagonal structure), sphalerite (cubic ZnS), covellite (CuS), and bornite (Cu 5 FeS 4 ). Wurtzite is present in the exchangeable and reducible fractions. These fractions also contain chalcocite (Cu 2 S). Sulfides are most abundant in soil sample after extraction of the oxidizable fraction, while phyllosilicates are prevalent in the sample after extraction of the reducible fraction. X-ray absorption spectroscopy demonstrates molecular structural changes in the Zn and Cu compounds in heavily polluted soils, suggesting the transformation of metals under different environmental conditions, which is important for assessment of the soil protective function.
Novel Exploit Feature-Map-Based Detection of Adversarial Attacks

In machine learning (ML), adversarial attack (targeted or untargeted) in the presence of noise disturbs the model prediction. This research suggests that adversarial perturbations on pictures lead to noise in the features constructed by any networks. As a result, adversarial assaults against image categorization systems may present obstacles and possibilities for studying convolutional neural networks (CNNs). According to this research, adversarial perturbations on pictures cause noise in the features created by neural networks. Motivated by adversarial perturbation on image pixel attacks observation, we developed a novel exploit feature map that describes adversarial attacks by performing individual object feature-map visual description. Specifically, a novel detection algorithm calculates each object’s class activation map weight and makes a combined activation map. When checked with different networks like VGGNet19 and ResNet50, in both white-box and black-box attack situations, the unique exploit feature-map significantly improves the state-of-the-art in adversarial resilience. Further, it will clearly exploit attacks on ImageNet under various algorithms like Fast Gradient Sign Method (FGSM), DeepFool, Projected Gradient Descent (PGD), and Backward Pass Differentiable Approximation (BPDA).
Influence of Language Proficiency on the Readability of Review Text and Transformer-based Models for Determining Language Proficiency

In this study, we analyze the influence of the English language proficiency of non-native speakers on the readability of the text written by them. In addition, we present multiple approaches for automatically determining the language proficiency levels of non-native English speakers from the review data. To accomplish the above-mentioned tasks, we first introduce an annotated social media corpus of around 1000 reviews written by non-native English speakers of the following five English language proficiency (ELP) groups: very high proficiency (VHP), high proficiency (HP), moderate proficiency (MP), low proficiency (LP), and very low proficiency (VLP). We employ the Flesch Reading Ease (FLE) and Flesch-Kincaid Grade (FKG) tests to compute the readability scores of the reviews written by various ELP groups. We leverage both the classical machine learning (ML) classifiers and transformer-based language models for deciding the language proficiency groups of the reviewers. We observe that distinct ELP groups do not exhibit any noticeable differences in the mean FRE scores, although slight differences are observed in the FKG test. The results imply that the readability measures do not possess high discriminating capabilities to distinguish various ELP groups. In the language proficiency determination task, we notice fine-tuned transformer-based approaches yield slightly better efficacy than the traditional ML classifiers.
Formal verification of modular multipliers using symbolic computer algebra and boolean satisfiability

Modular multipliers are the essential components in cryptography and Residue Number System (RNS) designs. Especially, 2n - 1 and 2n + 1 modular multipliers have gained more attention due to their regular structures and a wide variety of applications. However, there is no automated formal verification method to prove the correctness of these multipliers. As a result, bugs might remain undetected after the design phase.
Equivalence checking paradigms in quantum circuit design

As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for automated methods to design and test potential applications rises. Equivalence checking of quantum circuits is an important, yet hardly automated, task in the development of the quantum software stack. Recently, new methods have been proposed that tackle this problem from widely different perspectives. However, there is no established baseline on which to judge current and future progress in equivalence checking of quantum circuits. In order to close this gap, we conduct a detailed case study of two of the most promising equivalence checking methodologies---one based on decision diagrams and one based on the ZX-calculus---and compare their strengths and weaknesses.
Personal Knowledge Graphs: Use Cases in e-learning Platforms

Personal Knowledge Graphs (PKGs) are introduced by the semantic web community as small-sized user-centric knowledge graphs (KGs). PKGs fill the gap of personalised representation of user data and interests on the top of big, well-established encyclopedic KGs, such as DBpedia [21]. Inspired by the widely recent usage of PKGs in the medical domain to represent patient data, this PhD proposal aims to adopt a similar technique in the educational domain in e-learning platforms by deploying PKGs to represent users and learners. We propose a novel PKG development that relies on ontology and interlinks to Linked Open Data. Hence, adding the dimension of personalisation and explainability in users' featured data while respecting privacy. This research design is developed in two use cases: a collaborative search learning platform and an e-learning platform. Our preliminary results show that e-learning platforms can get benefited from our approach by providing personalised recommendations and more user and group-specific data.
Personal Research Knowledge Graphs

Maintaining research-related information in an organized manner can be challenging for a researcher. In this paper, we envision personal research knowledge graphs (PRKGs) as a means to represent structured information about the research activities of a researcher. PRKGs can be used to power intelligent personal assistants, and personalize various applications. We explore what entities and relations could be potentially included in a PRKG, how to extract them from various sources, and how to share a PRKG within a research group.
Inference attacks based on GAN in federated learning

Purpose In the digital age, organizations want to build a more powerful machine learning model that can serve the increasing needs of people. However, enhancing privacy and data security is one of the challenges for machine learning models, especially in federated learning. Parties want to collaborate with each other to build a better model, but they do not want to reveal their own data. This study aims to introduce threats and defenses to privacy leaks in the collaborative learning model. Design/methodology/approach In the collaborative model, the attacker was the central server or a participant. In this study, the attacker is on the side of the participant, who is “honest but curious.” Attack experiments are on the participant’s side, who performs two tasks: one is to train the collaborative learning model; the second task is to build a generative adversarial networks (GANs) model, which will perform the attack to infer more information received from the central server. There are three typical types of attacks: white box, black box without auxiliary information and black box with auxiliary information. The experimental environment is set up by PyTorch on Google Colab platform running on graphics processing unit with labeled faces in the wild and Canadian Institute For Advanced Research-10 data sets. Findings The paper assumes that the privacy leakage attack resides on the participant’s side, and the information in the parameter server contains too much knowledge to train a collaborative machine learning model. This study compares the success level of inference attack from model parameters based on GAN models. There are three GAN models, which are used in this method: condition GAN, control GAN and Wasserstein generative adversarial networks (WGAN). Of these three models, the WGAN model has proven to obtain the highest stability. Originality/value The concern about privacy and security for machine learning models are more important, especially for collaborative learning. The paper has contributed experimentally to private attack on the participant side in the collaborative learning model.
Combinatorial Algorithms for Subsequence Matching: A Survey

In this paper we provide an overview of a series of recent results regarding algorithms for searching for subsequences in words or for the analysis of the sets of subsequences occurring in a word.
Designing a rating system based on competencies for the analysis of the university teachers' research activities

One of the indicators of the influence and competitiveness of university is participation in international and local rankings. In the methodologies of the most authoritative rankings, the quality and transparency of Universities is assessed by indicators of the university teachers’ publishing activity. The article analyzes the experience of designing systems that analyses university teachers research activities, tools and components of evaluating the effectiveness of research, designed a structural and functional model of rating system for the analysis the university teachers’ research activities taking into account research and digital competencies. The developed model provides performance of the basic functions and allows for systematic monitoring of openness, transparency, efficiency of the research component of university teachers' professional activity. The model was based on key indicators for evaluating the effectiveness of research -- citation indicators of the three most important scientometric databases -- Scopus, Web of Science, Google Scholar. The connection between the indicators presented in the model forms a portrait of the university teachers’ scientific activity, gives an overall assessment of productivity, influence and contribution to the research direction of the university as a whole. The article describes 1 stage of implementation of the presented model by developing a “Rating of Transparency of Structural Units”, the effectiveness of experimental ranking. The study has established the positive impact of the implementation of the rating system, identified the main activities to increase the visibility, presence, dissemination of research results, the systematic implementation of which contributes to the optimal representation of the scientist in the rating evaluation of the research component of the university teachers professional activities, improving the digital competence of teachers and positively affects the quality indicators of the university scientific work in local and international rankings, as the existence of the system and the formation of ratings is an incentive for university teachers to present and disseminate their own publishing activities in the international online scientific community.
Selecting cloud computing software for a virtual online laboratory supporting the Operating Systems course

The article provides a survey on cloud platforms suitable for a virtual online laboratory, which contains Linux online environments and is intended to support the Operating Systems course.The study justifies the choice of utilizing private cloud as a deployment model and IaaS as a service model and substantiates the decision to create specially tailored cloud environments adapted for educational needs in contrast to applying ready-made IaaS (Infrastructure as a Service) cloud services given by providers. The related works on cloud platforms for teaching operating systems are analyzed. The study also makes a review of the authors' previous research on virtualization tools and environments for the Operating Systems course and Cisco CyberSecurity Operations course. The basic and additional requirements for cloud computing software for virtual online laboratory supporting Operating Systems course have been elaborated. Finally, the work makes the comparison of Eucalyptus, OpenStack, CloudStack and OpenNebula cloud platforms and substantiates the selection among these cloud computing software the platforms of the first and the second choice.
Enabling privacy and leakage resistance for dynamic blockchain-based access control systems

With rise of 5G/6G network, low-storage devices usually outsource data for higher rate and less latency. Non-controlled outsourcing and complex communications incur security issues for IoT applications. Specially, user privacy and access reliability pose technical challenges for sensitive data outsourcing and sharing. In this paper, we harmonize functional encryption and blockchain to propose a reliable and privacy-aware access control system named R-PAC. It allows a result-form access without learning raw information and fair interaction against malicious users. With a combination of all-or-nothing encapsulation technology, R-PAC supports users’ dynamic joining and key leakage resistance. We design R-PAC from Boneh–Franklin identity-based encryption, with forward-coverable encryption and reverse-discoverable decryption, and formally prove its indistinguishability security. We implement a R-PAC prototype and deploy it to a simulated Ethereum network to evaluate its performance. Experiments from both data access and transaction overhead show that R-PAC is with reasonable cost and has a trade-off between efficiency and strong security/functionality.
The unified image cryptography algorithm based on finite group

Image information security is an essential component of the network information security field. The main task of image cryptography is exploring the image cryptosystem with high processing speed and strong security. This paper studied the confusion and diffusion of images based on the finite group algorithm and then proposed a new uniform image cryptosystem which shared the encryption and decryption processes. The paper also suggested a keystream extension algorithm based on any length key. The speed of this image cryptosystem reached 5.54Mbps in the simulation environment. The absolute relative error of each performance index between its tested value and the theoretical value is less than 0.1%. The simulation experiments showed that the proposed image cryptography algorithm could be a candidate algorithm for the network image security technology.
A comprehensive construction of the domain ontology for stratigraphy

Stratigraphic knowledge, the cornerstone of geoscience, needs to be represented by the Knowledge Graph based upon ontology, in order to apply the state-of-the-art big-data techniques. This study aims to comprehensively construct the ontologies for the stratigraphic domain. This has been achieved by a federated, crowd intelligence-based collaboration among domain experts of major stratigraphic subdisciplines. The initial step is to enumerate key terms from authoritative references and incorporate them into the Geoscience Professional Knowledge Graphs (GPKGs) of Deep-time Digital Earth Project. During this process, semantic heterogeneities were meticulously addressed by professional judgement aided by an automatic detection of Homonyms at the GPKGs platform. Afterwards, these terms were further differentiated as either classes or properties and arranged in a hierarchical framework in a top-down process. Consequently, seven ontologies are constructed for major stratigraphic branches, i.e., Lithostratigraphy, Biostratigraphy, Chronostratigraphy, Chemostratigraphy, Magnetostratigraphy, Cyclostratigraphy and Sequence Stratigraphy. The ontology of Biostratigraphy, among them, is elaborated here, as no biostratigraphic ontology has been attempted before to our knowledge. The constructed biostratigraphic ontology comprises following major root classes: Fossil, Biostratigraphic unit, Biostratigraphic horizon. Altogether, they contribute to the eventual dating and correlating of strata in another root class: Biostratigraphic correlation. In summary, the achievements of this study are probably heretofore the most comprehensive ontologies for the stratigraphic domain. Moreover, a proto model of semantic search engine was conceived to discuss potential application of our work for better querying stratigraphic references, utilizing the semantic liaison of the classes in the constructed ontologies.
Optimized GAN for Text-to-Image Synthesis: Hybrid Whale Optimization Algorithm and Dragonfly Algorithm

• This paper aims to design a new text-to-image synthesis scheme. • Initially, throughout text to image encoding, cross modal feature grouping is done. • BI-LSTM is employed for converting the text embeddings into text feature vectors. • In the subsequent stage, the images are synthesized depending upon the encoding. • The GAN is trained with optimization strategy by tuning optimal weights using DC-WO. Text to image synthesizes is a motivating and valuable task to explore in these days. The objective is to produce images from texts or captions, i.e. considering textual descriptions as input and deploying them to produce related images. For carrying this task, two challenging elements of image generation and language modeling should be combined that are said to be more complex than caption generation. This research aims to develop a new text-to-image synthesis method that includes the two crucial steps of (i) text to picture encoding and (ii) optimized GAN. At first, cross-modal feature grouping is done throughout the text to image encoding. Consequently, text embeddings are transformed into textual feature vectors using BI-LSTM. The images are created based on the encoding in the following stage. Thus, optimized GAN receives input from text feature groups and outputs the end synthesis images. Here, a research plans to train the GAN with optimization strategy via tuning the optimal weights using the Dragon Customized Whale Optimization (DC-WO) model. Finally, the superiority of the created approach is evaluated by comparing it to existing techniques. Additionally, the FID value of the proposed model using the cub dataset is 29.04%, 47.43%, 49.09%, 37.64% and 11.35%, superior to traditional FF, WOA, GWO, DA and GAN + CMAF models.
Correlation between Acoustic Analysis and Psycho-Acoustic Evaluation of Violins

This paper presents the results of an experimental study performed on seven violins obtained from a top plate made of resonance spruce and a back plate made of curly maple. Each pair of plates had a different modification to its thickness profile. Some were thickened and others were thinned compared to the classical thickness profile. Then, a soloist played a musical sequence on each violin and the acoustic signals were recorded. The sound quality of the signals was evaluated with a psycho-acoustic evaluation based on a blind questionnaire completed by listeners. It turned out that: (1) respondents with more musical experience (especially those with over 26 years of experience) were more demanding in assessing sound clarity and offered the widest range of scores in assessing this quality; (2) the musical experience of the respondents influenced to the highest degree the appreciation of the warm sound quality; (3) the scores for the violins with thinned plates were weaker, especially according to the psycho-acoustic analysis; and (4) the highest score was obtained by the violin with the thickest plates, which can be correlated with the two dominant frequencies extracted from the FFT analysis, whose values coincide with the frequencies of the B1− and B1+ modes.
Fairness and privacy preservation for facial images: GAN-based methods

Facial images are widely adopted for computer vision tasks such as face recognition or attribute classifications. Consequently, the adoption of mass real facial images leads to significant identification privacy leakage concerns. Meanwhile, the model classification results suffer unfair predictions towards features such as genders due to biased training data distributions. Although methods have been proposed to resolve the privacy and fairness issues separately, simultaneous protection methods are merely studied. In this study, for facial attributes classifications, we propose one unified framework with GAN models to generate synthetic images for privacy protections and contrastive learning based loss designs to enforce fairness protections simultaneously. Meanwhile, unlike other privacy or fairness protection methods, the proposed methods can maintain high data and model utilities. We evaluate our approaches with the high image resolution dataset CelebA-HD, and the results show our methods meet both privacy and fairness requirements.
An adaptive neighborhood-based speciation differential evolution for multimodal optimization

Multimodal optimization problems (MMOPs) are single-objective optimization problems with multiple optimal solutions. It is essential to find as many optimal solutions as possible to make a robust decision. However, it is a very challenging task. In this paper, an enhanced adaptive neighborhood-based speciation differential evolution (EANSDE) is proposed to solve the MMOPs effectively, which can be featured as: (i) The parameters are adaptively controlled to alleviate the fine-tuning of parameters by the users. (ii) An external archive is introduced to store the inferior solutions discarded in each generation. When the population size is reduced to a preset threshold, the solutions in the archive are merged with the current population in the following search. (iii) The crowding relieving mechanism is proposed to remove extremely similar individuals from population. Compared with related methods, experimental results indicate the superiority of EANSDE on the 20 benchmark MMOPs in CEC-2013.