Reviewerly/exHarmony huggingface dataset:
PASTA: An Efficient Proactive Adaptation Approach Based on Statistical Model Checking for Self-Adaptive Systems

Abstract Proactive adaptation, in which the adaptation for a system’s reliable goal achievement is performed by predicting changes in the environment, is considered as an effective alternative to reactive adaptation, in which adaptation is performed after observing changes. When predicting the environmental changes, the prediction may be uncertain, so it is necessary to verify and confirm an adaptation’s consequences before execution. To resolve the uncertainty, probabilistic model checking (PMC) has been utilized for verification of adaptation tactics’ effects on the goal of a self-adaptive system (SAS). However, PMC-based approaches have limitations on the state-explosion problem of complex SAS model verification and the modeling languages supported by the model checkers. In this paper, to overcome the limitations of the PMC-based approaches, we propose an efficient Proactive Adaptation approach based on STAtistical model checking (PASTA). Our approach allows SASs to mitigate the uncertainty of the future environment, faster than the PMC-based approach, by producing statistically sufficient samples for verification of adaptation tactics based on statistical model checking (SMC) algorithms. We provide algorithmic processes, a reference architecture, and an open-source implementation skeleton of PASTA for engineers to apply it for SAS development. We evaluate PASTA on two SASs using actual data and show that PASTA is efficient comparing to the PMC-based approach. We also provide a comparative analysis of the advantages and disadvantages of PMC- and SMC-based proactive adaptation to guide engineers’ decision-making for SAS development.
Simulation of adiabatic quantum computing for molecular ground states

Quantum computation promises to provide substantial speedups in many practical applications with a particularly exciting one being the simulation of quantum many-body systems. Adiabatic state preparation (ASP) is one way that quantum computers could recreate and simulate the ground state of a physical system. In this paper, we explore a novel approach for classically simulating the time dynamics of ASP with high accuracy and with only modest computational resources via an adaptive sampling configuration interaction scheme for truncating the Hilbert space to only the most important determinants. We verify that this truncation introduces negligible error and use this new approach to simulate ASP for sets of small molecular systems and Hubbard models. Furthermore, we examine two approaches to speeding up ASP when performed on quantum hardware: (i) using the complete active space configuration interaction (CASCI) wave function instead of the Hartree-Fock initial state and (ii) a nonlinear interpolation between the initial and target Hamiltonians. We find that starting with a CASCI wave function with a limited active space yields substantial speedups for many of the systems examined, while nonlinear interpolation does not. In additional, we observe interesting trends in the minimum gap location (based on the initial state) as well as how state preparation time can depend on certain molecular properties, such as the number of valence electrons. Importantly, we find that the required state preparation times do not show an immediate exponential wall that would preclude an efficient run of ASP on actual hardware.
An HEVC steganalytic approach against motion vector modification using local optimality in candidate list

Current research of video steganalysis is mainly oriented to H.264 or even earlier formats. As the H.265/HEVC standard is becoming more and more popular, there have been steganographic methods specifically designed for HEVC videos. Previous steganalytic methods are no longer effective due to the new features of HEVC. The advanced motion vector prediction technique employed by HEVC provides a new way for motion vector (MV) modification, utilizing the index of the candidate MV list. Such modification cannot be detected by previous steganalytic methods because the value of MV is not changed. To solve this problem, we proposed a new steganalytic strategy employing not only the local optimality of MV but also the local optimality in the candidate list. Combining the two types of local optimality, a 40-dimensional feature set is designed. Extensive experiments are carried out to evaluate the effectiveness of the proposed feature set. It is shown that compared with three current steganalyzers, our approach improves the performance against typical MV modification methods under various settings. In particular, our approach significantly boosts the detection accuracy of the index modification method that is exclusive to HEVC videos. To our best knowledge, this is the first work of video steganalytic method exploiting the new features of HEVC format.
Ontological Visualization of Knowledge Structures Based on the Operational Management of Information Objects

The properties of ontological models to reflect the conceptual view of a researcher on a certain subject area and uniquely determine its concept, structure, accumulate and reuse knowledge repeatedly and make it possible to widely use it in the educational process. However, the existing tools for constructing ontological models of ontology knowledge (Protégé in integration with OWLViz, OntoGraf, 3D Hyperbolyc, Tree, TODOS, etc.) only partially satisfy the needs of the educational process. The choice of a particular visualization method is specific and has its own characteristics depending on the task. When choosing visualization tools, it is important that they do not only help to effectively reflect all the information, but, at the same time, allow the user to perform easily various operations on ontologies. There is no single definite method that is universal. The article describes the author’s web resource https://ontos.xyz , designed to create and visualize the hierarchical structure of subject areas using an ontograph, is implemented as a combination of content-containing concepts (vertices, nodes) and the relationships between these concepts. The authors determine the possibilities of using the ontological approach in the pedagogical process by the example of working with the web resource they created, which provides convenient use of ontologies. The article discusses examples of using ontologies to provide semantic annotation for collections of images, sound and other non-text objects; for the development of documentation of arbitrary volume; for building directories with the ability to support the surjective connection of objects (nodes); to simplify the performance of search work, etc. The purpose of the article is to reveal the features of the use of ontological structures for visualizing knowledge schemes and components of the operational management of information objects.
Low-Resource Machine Translation for Low-Resource Languages: Leveraging Comparable Data, Code-Switching and Compute Resources.

We conduct an empirical study of unsupervised neural machine translation (NMT) for truly low resource languages, exploring the case when both parallel training data and compute resource are lacking, reflecting the reality of most of the world's languages and the researchers working on these languages. We propose a simple and scalable method to improve unsupervised NMT, showing how adding comparable data mined using a bilingual dictionary along with modest additional compute resource to train the model can significantly improve its performance. We also demonstrate how the use of the dictionary to code-switch monolingual data to create more comparable data can further improve performance. With this weak supervision, our best method achieves BLEU scores that improve over supervised results for English$\rightarrow$Gujarati (+18.88), English$\rightarrow$Kazakh (+5.84), and English$\rightarrow$Somali (+1.16), showing the promise of weakly-supervised NMT for many low resource languages with modest compute resource in the world. To the best of our knowledge, our work is the first to quantitatively showcase the impact of different modest compute resource in low resource NMT.
Parametric Fuzzy Implications Produced via Fuzzy Negations with a Case Study in Environmental Variables

In this paper, we present a new Fuzzy Implication Generator via Fuzzy Negations which was generated via conical sections, in combination with the well-known Fuzzy Conjunction. The new Fuzzy Implication Generator takes its final forms after being configured by the fuzzy strong negations and combined with the most well-known fuzzy conjunctions TM, TP, TLK, TD, and TnM. The final implications that emerge, given that they are configured with the appropriate code, select the best value of the parameter and the best combination of the fuzzy conjunctions. This choice is made after comparing them with the Empiristic implication, which was created with the help of real temperature and humidity data from the Hellenic Meteorological Service. The use of the Empiristic implication is based on real data, and it also reduces the volume of the data without canceling them. Finally, the MATLAB code, which was used in the programming part of the paper, uses the new Fuzzy Implication Generator and approaches the Empiristic implication satisfactorily which is our final goal.
High-accuracy deflectometric microscope system with a large slope range

We propose an off-axis deflectometric microscope system for microscopic surface testing with both high measurement accuracy and a large slope dynamic range. A high-luminance liquid crystal display directly illuminates the tested sample with coded fringes, and then the reflected fringes passing through a microscope objective are captured by a pinhole camera, from which the deflectometric microscopic testing with a large slope range can be achieved. The accuracy of the proposed system is validated numerically and experimentally, and a large measurable slope dynamic range is also demonstrated. The proposed system provides a feasible way with the slope range in the order of sub-radians and sag resolution better than 0.05 nm.
LaKSA: A Probabilistic Proof-of-Stake Protocol

We present Large-scale Known-committee Stakebased Agreement (LaKSA), a chain-based Proof-of-Stake protocol that is dedicated, but not limited, to cryptocurrencies.LaKSA minimizes interactions between nodes through lightweight committee voting, resulting in a simpler, more robust, and more scalable proposal than competing systems.It also mitigates other drawbacks of previous systems, such as high reward variance and long confirmation times.LaKSA can support large numbers of nodes by design, and provides probabilistic safety guarantees in which a client makes commit decisions by calculating the probability that a transaction is reverted based on its blockchain view.We present a thorough analysis of LaKSA and report on its implementation and evaluation.Furthermore, our new technique of proving safety can be applied more broadly to other Proof-of-Stake protocols.
Shifted Cubic Spline Wavelets with Two Vanishing Moments on the Interval and a Splitting Algorithm

This paper deals with the use of the first two vanishing moments for constructing cubic spline-wavelets orthogonal to polynomials of the first degree. A decrease in the supports of these wavelets is shown in comparison with the classical semiorthogonal wavelets. For splines with homogeneous Dirichlet boundary conditions of the second order, an algorithm of the shifted wavelet transform is obtained in the form of a solution of a tridiagonal system of linear equations with a strict diagonal dominance
A Systemic Study of Pattern Recognition System Using Feedback Neural Networks

The evolution of artificial intelligence has led to the developments of various smart applications such as the pattern recognition models. Pattern recognition techniques has as widely applied in many real life applications such character recognition, speech recognition, and bio-metric authentication as well person identification. In this paper, we report on the detailed design of pattern recognition system using Hopfield Feedback Neural Network (HFNN) with the least possible recognition error. As a case study, we have applied the proposed HFNN model to recognize the decimal digits 0 - 9 where each image digit comprises a 12x10 pixels. The developed HFNN model has been efficiently used in recognizing the patterns with 20% random bit noise at maximum recognition accuracy. However, to assure the the least possible recognition error, we have trained our HFNN through the digit patterns’ perdition phase of 0% noisy patterns and the system was able to correctly predict all the patterns without any bit error. Finally, we have plotted all output patterns including the desired patterns, the training patterns, the 20% noisy patterns and recognized patterns, for comparison purposes and to gain more insights about the accuracy achieved by applying the proposed HFNN.
Minimal Block Knight’s Tour and Edge with LSB Pixel Replacement Based Encrypted Image Steganography

The data security of an information is predominant in the digital world and gaining lot of importance. Cryptography and steganography are widely used in providing security to an information. In the proposed algorithm, the image encryption and steganography are performed using Knight’s move in the game of chess called Knight’s Tour Algorithm. Minimum block or square required for a knight’s tour to reach all the squares is 5 × 5 block. The 5 × 5 blocks’ pattern generated is used for image encryption. The encrypted image is then embedded into another image and block shuffling is performed to obtain a crypto-stego image. Proposed algorithm is robust and provides high data security with a good PSNR and SSIM.
Stemming Analysis Indonesian Language News Text with Porter Algorithm

Abstract Stemming is the process of classifying various morphological variations of a word or sentence into one and the same basic form. In Indonesian language stemming, there are two types of stemming methods that already exist, namely the dictionary-based stemming algorithm and the non-dictionary-based stemming algorithm. In this study the algorithm used is the Indonesian Porter algorithm for dictionary-based ones. The test was carried out using 100 predetermined Indonesian text documents. The results of tests conducted show that the highest accuracy value is found in the Porter algorithm, the least Overstemming and Understemming values are also found in the Porter algorithm.
Towards String Support in JayHorn (Competition Contribution)

Abstract is a Horn clause-based model checker for Java programs that has been competing at SV-COMP since 2019. An ongoing research and implementation effort is to add support for data-type to . Since current Horn solvers do not support strings natively, we consider a representation of (unbounded) strings using algebraic data-types, more precisely as lists. This paper discusses Horn clause encodings of different string operations, and presents preliminary results.
Theoretical and Legal Aspects of Cyberbulling

The article considers the theoretical and legal aspects of cyberbullying. The relevance of the research topic is connected with the lack of proper scientific study of this problem and the need to consider cyberbullying as an unlawfully socially dangerous act. Based on the study, the author's definition of cyberbullying is proposed. The results of a population survey on bullying on the Internet are presented. The main forms of cyberbullying are considered. One form of cyberbullying is doxing, which is the public disclosure of personal and confidential data via the Internet. The analysis of the motivation of cyber aggressors was carried out and two types of victims of cyberbullying were identified. The main short-term and long-term psychological and physical consequences of victims of cyberbullying are described. The differences of cyberbullying from the traditional forms of bullying are examined, the emphasis is placed on the presence of witnesses, who are divided into groups in the article, based on the roles they play in the bullying process. The current Russian legislation regarding liability for cyberbullying is analyzed. It is noted that in Russia this action is not considered an independent corpus delicti. Basic preventive measures are proposed that can eliminate or minimize the likelihood of becoming a victim of cyberbullying. Emphasis is placed on the need to complicate the registration procedure on social networks, on forums and other Internet resources by linking the profile to a mobile phone number, public services website account, and also specifying the real username or an officially registered nickname, which will eliminate online anonymity and malicious use of assumed names. The international experience of countering cyberbullying and the possibility of using it to improve domestic legislation in this area are considered.
Modeling Hierarchical System with Operads

This paper applies operads and functorial semantics to address the problem of failure diagnosis in complex systems.We start with a concrete example, developing a hierarchical interaction model for the Length Scale Interferometer, a high-precision measurement system operated by the US National Institute of Standards and Technology.The model is expressed in terms of combinatorial/diagrammatic structures called port-graphs, and we explain how to extract an operad LSI from a collection of these diagrams.Next we show how functors to the operad of probabilities organize and constrain the relative probabilities of component failure in the system.Finally, we show how to extend the analysis from general component failure to specific failure modes.
Hindi to English: Transformer-Based Neural Machine Translation

Machine Translation (MT) is one of the most prominent tasks in Natural Language Processing (NLP) which involves the automatic conversion of texts from one natural language to another while preserving its meaning and fluency. Although the research in machine translation has been going on since multiple decades, the newer approach of integrating deep learning techniques in natural language processing has led to significant improvements in the translation quality. This paper has developed a Neural Machine Translation (NMT) system by training the Transformer model to translate texts from Indian Language Hindi to English. Hindi being a low resource language has made it difficult for neural networks to understand the language thereby leading to a slow growth in the development of neural machine translators. Thus, to address this gap, back-translation is implemented to augment the training data and for creating the vocabulary, it has been experimented with both word and subword level tokenization using Byte Pair Encoding (BPE) thereby ending up training the Transformer in 10 different configurations. This led us to achieve a state-of-the-art BLEU score of 24.53 on the test set of IIT Bombay English-Hindi Corpus in one of the configurations.
ToAM: a task-oriented authentication model for UAVs based on blockchain

Abstract The pervasive cooperation of a group of UAVs has attracted increasing attention due to the reduced cost and widespread availability. When working in an untrusted or adversarial environment, the mutual authentication of UAVs in the cooperative process is imperative. However, there are some major challenges, including changes in the network environment before and during task performing, and the weak connection network state faced by UAVs. Therefore, a novel task-oriented authentication model for UAVs based on blockchain (ToAM) is proposed, which divides UAVs authentication into group building authentication and intra-group authentication with a two-stage authentication framework. And two lightweight authentication protocols are presented, respectively, corresponding to two stages. Finally, analyses demonstrate that our model realizes secure and lightweight authentication function for the whole process of UAVs requisition and task performing.
Can a Skill be Measured or Assessed?

This paper reviews historic and current definitions of skill as a concept, as well as frameworks that have been applied to analyse levels of skills development across the academic and professional spectrum. The author proposes a 6-level chart of skill development based on the “can-do descriptors” approach by CEFR, also known as the ability assessment approach, for further discussion, development, and application. The proposed chart is based on six levels. These are organized into two Entry levels, two Operational Use levels and two Strategic Vision levels. Further debate on the typology of skills by Deloitte, LinkedIn Research and World Economic Forum is presented as of 2020 documentation. Its dynamic manner of paradigms changing the importance of a wide array of skills (alternatively denominated as employability skills, soft skills and/or 21st Century skills) are analysed, both for educators and employers.
Answer Sentence Selection Using Local and Global Context in Transformer Models

An essential task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question. Previous neural models have experimented with using additional text together with the target sentence to learn a selection function but these methods were not powerful enough to effectively encode contextual information. In this paper, we analyze the role of contextual information for the sentence selection task in Transformer based architectures, leveraging two types of context, local and global. The former describes the paragraph containing the sentence, aiming at solving implicit references, whereas the latter describes the entire document containing the candidate sentence, providing content-based information. The results on three different benchmarks show that the combination of the local and global context in a Transformer model significantly improves the accuracy in Answer Sentence Selection.
SiT: Self-supervised vIsion Transformer

Self-supervised learning methods are gaining increasing traction in computer vision due to their recent success in reducing the gap with supervised learning. In natural language processing (NLP) self-supervised learning and transformers are already the methods of choice. The recent literature suggests that the transformers are becoming increasingly popular also in computer vision. So far, the vision transformers have been shown to work well when pretrained either using a large scale supervised data or with some kind of co-supervision, e.g. in terms of teacher network. These supervised pretrained vision transformers achieve very good results in downstream tasks with minimal changes. In this work we investigate the merits of self-supervised learning for pretraining image/vision transformers and then using them for downstream classification tasks. We propose Self-supervised vIsion Transformers (SiT) and discuss several self-supervised training mechanisms to obtain a pretext model. The architectural flexibility of SiT allows us to use it as an autoencoder and work with multiple self-supervised tasks seamlessly. We show that a pretrained SiT can be finetuned for a downstream classification task on small scale datasets, consisting of a few thousand images rather than several millions. The proposed approach is evaluated on standard datasets using common protocols. The results demonstrate the strength of the transformers and their suitability for self-supervised learning. We outperformed existing self-supervised learning methods by large margin. We also observed that SiT is good for few shot learning and also showed that it is learning useful representation by simply training a linear classifier on top of the learned features from SiT. Pretraining, finetuning, and evaluation codes will be available under: this https URL.