Reviewerly/exHarmony huggingface dataset:
Blockchain Open Issues, Research Gaps, and Road Map of Future Blockchain for 2030

The future is great for blockchain technology. Blockchain market size can grow from 3 billion USD to 39.7 billion USD by 2025. Thirty percent of blockchain projects might go live this year. There is another prediction that 90% of those projects might have a substitute solution. Twenty-five percent of Forbes Global 2000 might start implementing blockchain for improving digital trust. The implementations might not use tokenization, smart contracts, decentralized consensus, and other features. The latest solutions during COVID-19 lockdown are becoming the reference solutions for the blockchain initiatives. The number of enterprises that are part of the blockchain networks has significantly increased. On the other hand, long-term blockchain implementations are on hold. Future projects are focusing on creating a digital platform for post-pandemic scenarios. Private blockchains are becoming popular, and they will have a bigger market share compared to public blockchains. European countries are coming up with their cryptocurrency, and China is ready with its crypto yuan.
BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.&#x0D;
Nonlinear Differential Equations in the Teaching Model of Educational Informatisation

Abstract This article takes the nonlinear differential equation model as an example and uses information technology to create a new course teaching model. The computing software Maple is effectively applied to the precise solution of nonlinear differential equations through the analysis of examples. The overall goal and specific implementation of the teaching plan to integrate ordinary differential equations and information technology have been completed. The study found that the teaching plan proposed in the article is assisted by information technology, with teachers and students participating in an interactive and exploratory teaching method. This method is worthy of further promotion.
Modeling a multi-layered blockchain framework for digital services that governments can implement

The general population increasingly uses digital services, meaning services which are delivered over the internet or an electronic network, and events such as pandemics have accelerated the need of using new digital services. Governments have also increased their number of digital services, however, these digital services still lack of sufficient information security, particularly integrity. Blockchain uses cryptographic techniques that allow decentralization and increase the integrity of the information it handles, but it still has disadvantages in terms of efficiency, making it incapable of implementing some digital services where a high rate of transactions are required. In order to increase its efficient, a multi-layer proposal based on blockchain is presented. It has four layers, where each layer specializes in a different type of information and uses properties of public blockchain and private blockchain. An statistical analysis is performed and the proposal is modeled showing that it maintains and even increases the integrity of the information while preserving the efficiency of transactions. Besides, the proposal can be flexible and adapt to different types of digital services. It also considers that voluntary nodes participate in the decentralization of information making it more secure, verifiable, transparent and reliable.
Language Intelligence Development of English Multimedia Teaching considering Collaborative Filtering Topic Search Algorithm

With the opening up of our country, English has become one of the necessary skills for foreign communication. Traditional English teaching can no longer meet the needs of learning. It is necessary to introduce computer, multimedia, and other technologies to supplement students to improve students’ autonomous learning ability and learning interest. This is extremely important for the evaluation of English multimedia teaching. Based on the collaborative filtering topic search algorithm, this article tries to sort out the influence of English synthesis and English specialization on the development of language intelligence for English multimedia teaching and analyzes and promotes the improvement of students’ interest in learning English and the improvement of classroom self-learning awareness. The results of simulation experiments show that the collaborative filtering topic search algorithm is effective and can support the development of language intelligence in English multimedia teaching.
Modeling Deception: A Case Study of Email Phishing

Information manipulation for deception continues to evolve at a remarkable rate. Artificial intelligence has greatly reduced the burden of combing through documents for evidence of manipulation; but it has also enabled the development of clever modes of deception. In this study, we modeled deception attacks by examining phishing emails that successfully evaded detection by the Microsoft 365 filtering system. The sample population selected for this study was the University of North Texas students, faculty, staff, alumni and retirees who maintain their university email accounts. The model explains why certain individuals and organizations are selected as targets, and identifies potential counter measures and counter attacks. Over a one-year period, 432 phishing emails with different features, characters, length, context and semantics successfully passed through Microsoft Office 365 filtering system. The targeted population ranged from 18 years old up to those of retirement age; ranged across educational levels from undergraduate through doctoral levels; and ranged across races. The unstructured data was preprocessed by filtering out duplicates to avoid overemphasizing a single attack. The term frequency-inverse document frequency (TF-IDF) and distribution of words over documents (topic modeling) were analyzed. Results show that staff and students were the main target audience, and the phishing email volume spiked in the summer and holiday season. The TF-IDF analysis showed that the phishing emails could be categorized under six categories: reward, urgency, job, entertainment, fear, and curiosity. Analysis showed that attackers use information gap theory to bait email recipients to open phishing emails with no subject line or very attractive subject line in about thirty percent of cases. Ambiguity remains the main stimulus used by phishing attackers, while the reinforcements used to misinform the targets range from positive reinforcements (prize, reward) to negative reinforcements (blackmail, potential consequences).
Identifying the polarity of a text given the emotion of its author

The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author’s Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics.
An Approach to Integrate a Blockchain-Based Payment Model and Independent Secure Documentation for a Robot as a Service

Abstract Robots for hire instead of purchase are developing an increasing interest among customers. This article will present a concept developed at ZeMA for the integration of a blockchain-based payment module and manipulation-proof documentation of process-specific data. This should improve the business model of hiring robots not only in terms of technical components but also in terms of economic considerations. In this way, the billing process can be automated to a certain extent, and a legally secure basis with the manipulation-proof storage of process-specific data can be created. The advantages and disadvantages of blockchain, in relation to Robot as a Service, will be highlighted and it will be shown how the disadvantages can be negated. The current limits of blockchain will also be shown. The blockchain technology IOTA is used.
DataSifter II: Partially synthetic data sharing of sensitive information containing time-varying correlated observations

There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated ( sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics.
A Hierarchical Hadoop Framework to Process Geo-Distributed Big Data

In the past twenty years, we have witnessed an unprecedented production of data worldwide that has generated a growing demand for computing resources and has stimulated the design of computing paradigms and software tools to efficiently and quickly obtain insights on such a Big Data. State-of-the-art parallel computing techniques such as the MapReduce guarantee high performance in scenarios where involved computing nodes are equally sized and clustered via broadband network links, and the data are co-located with the cluster of nodes. Unfortunately, the mentioned techniques have proven ineffective in geographically distributed scenarios, i.e., computing contexts where nodes and data are geographically distributed across multiple distant data centers. In the literature, researchers have proposed variants of the MapReduce paradigm that obtain awareness of the constraints imposed in those scenarios (such as the imbalance of nodes computing power and of interconnecting links) to enforce smart task scheduling strategies. We have designed a hierarchical computing framework in which a context-aware scheduler orchestrates computing tasks that leverage the potential of the vanilla Hadoop framework within each data center taking part in the computation. In this work, after presenting the features of the developed framework, we advocate the opportunity of fragmenting the data in a smart way so that the scheduler produces a fairer distribution of the workload among the computing tasks. To prove the concept, we implemented a software prototype of the framework and ran several experiments on a small-scale testbed. Test results are discussed in the last part of the paper.
Coherence migration in high-dimensional bipartite systems

The conservation law for first-order coherence and mutual correlation of a bipartite qubit state was firstly proposed by Svozilík et al. , and their theories laid the foundation for the study of coherence migration under unitary transformations. In this paper, we generalize the framework of first-order coherence and mutual correlation to an arbitrary ( m ⊗ n )-dimensional bipartite composite state by introducing an extended Bloch decomposition form of the state. We also generalize two kinds of unitary operators in high-dimensional systems, which can bring about coherence migration and help to obtain the maximum or minimum first-order coherence. Meanwhile, the coherence migration in open quantum systems is investigated. We take depolarizing channels as examples and establish that the reduced first-order coherence of the principal system over time is completely transformed into mutual correlation of the (2 ⊗ 4)-dimensional system-environment bipartite composite state. It is expected that our results may provide a valuable idea or method for controlling the quantum resource such as coherence and quantum correlations.
VIP: verifying real-world C idioms with integer-pointer casts

Systems code often requires fine-grained control over memory layout and pointers, expressed using low-level ( e.g. , bitwise) operations on pointer values. Since these operations go beyond what basic pointer arithmetic in C allows, they are performed with the help of integer-pointer casts . Prior work has explored increasingly realistic memory object models for C that account for the desired semantics of integer-pointer casts while also being sound w.r.t. compiler optimisations, culminating in PNVI, the preferred memory object model in ongoing discussions within the ISO WG14 C standards committee. However, its complexity makes it an unappealing target for verification, and no tools currently exist to verify C programs under PNVI. In this paper, we introduce VIP, a new memory object model aimed at supporting C verification. VIP sidesteps the complexities of PNVI with a simple but effective idea: a new construct that lets programmers express the intended provenances of integer-pointer casts explicitly. At the same time, we prove VIP compatible with PNVI, thus enabling verification on top of VIP to benefit from PNVI’s validation with respect to practice. In particular, we build a verification tool, RefinedC-VIP, for verifying programs under VIP semantics. As the name suggests, RefinedC-VIP extends the recently developed RefinedC tool, which is automated yet also produces foundational proofs in Coq. We evaluate RefinedC-VIP on a range of systems-code idioms, and validate VIP’s expressiveness via an implementation in the Cerberus C semantics.
Spacecraft Trajectory Optimizations: Metrics for Fitness Landscape Analysis

This paper presents fitness landscape analysis (FLA) for investigating fitness landscape characteristics and problem difficulty for spacecraft trajectory optimization problems. The FLA is performed to understand/identify structural characteristics of the optimization problem. Five FLA metrics are used to quantify the problem features. They can measure not only the fitness landscape shape such as ruggedness and neutrality but also identify problem properties such as modality and funnel structure. We apply those metrics to spacecraft trajectory optimization problems and investigate the fitness landscape features in detail. In addition, the correlation between the algorithm performance and FLA metrics is analyzed, and appropriate FLA metrics for predicting problem difficulty are proposed.
Usefulness of Graphemes in Word-Level Language Identification in Code-Mixed Text

Language Identification (LI) is a crucial part of various text-processing pipelines, as most techniques presume that the language of input text is known. Document-level Language Identification has been seen as an almost solved problem in some application areas, but language detectors fail in the case of social media environment due to code-switching, word-borrowing from different languages, phonetic typing; which imply that LI in code-mixed text must be carried out at word-level. Hence, this work focuses on identifying languages at word-level in multilingual environments like social-media. One of the major concerns of these environments is phonetic typing which can be taken into consideration by inculcating graphemic features into our model. Character n-grams take all combination of character occurring together into account resulting in large model size, whereas graphemic features consider only those combinations of characters having some underlying linguistic significance. For example, ‘kh’ and ‘gh’ graphemes occur majorly in languages like Hindi and Urdu in comparison to English. According to our observations in dataset (Sarma et al. in Word level language identification in assamese-bengali-hindi-english code-mixed social media text, pp. 261–266), we have observed that more graphemes (53.46%) are exclusive to a particular language than bigrams (21.38%) or trigrams (39.43%) are. This work consists of detailed analysis and comparison on the basis of several metrics between the character n-gram and grapheme based features by performing experiments using grapheme based features in various popular methods (originally containing only character n-gram features) in place of character n-gram features. Through these set of experiments and our analysis, we show the usefulness of grapheme in the field of word-level LI.
A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples

Software systems tend to become more and more configurable to satisfy the demands of their increasingly varied customers.Exhaustively testing the correctness of highly configurable software is infeasible in most cases because the space of possible configurations is typically colossal.This paper proposes addressing this challenge by (i) working with a representative sample of the configurations, i.e., a "uniform" random sample, and (ii) processing the results of testing the sample with a rule induction system that extracts the faults that cause the tests to fail.The paper (i) gives a concrete implementation of the approach, (ii) compares the performance of the rule learning algorithms AQ, CN2, LEM2, PART, and RIPPER, and (iii) provides empirical evidence supporting our procedure.
Adaptability and Self-Organization of the Doctoral Training System

Training of highly qualified personnel for science, education and industry is an important factor in the formation of a high-tech economy. The modern system of doctoral training does not always cope with its tasks. Enhancing the efficiency of doctoral training can be associated with an increase in adaptability and self-organization. The adaptation of the pedagogical system to changing conditions should be considered in a broad sense, while its fitting to students’ features is a special manifestation of this property. It is necessary to work out two mechanisms of its adaptation – passive and active. Passive adaptation means adjusting to changing external conditions (including a new law), changing in the systems of an adjacent and higher levels. Active adaptation involves the influence on other subsystems of the university educational environment, such as master’s school, additional education (retraining and advanced training of professors), the scientific and pedagogical personnel attestation system (dissertation councils), research departments. Such adaptation can take place according to the model of expanding influence, simultaneously at two levels – personal (subject-subjective) and organizational and managerial. Self-organization is considered as one of the most important features of the system adaptability and a condition for its successful functioning.
Enhancing interactive graph representation learning for review-based item recommendation

Collaborative filtering has been successful in the recommendation systems of various scenarios, but it is also hampered by issues such as cold start and data sparsity. To alleviate the above problems, recent studies have attempted to integrate review information into models to improve accuracy of rating prediction. While most of the existing models respectively utilize independent module to extract the latent feature representation of user reviews and item reviews, ignoring the correlation between the latent features, which may fail to capture the similarity of user preferences and item attributes hidden in different review text. On the other hand, the graph neural network can realize the information interaction in high dimensional space through deep architecture, which has been extensively studied in many fields. Therefore, in order to explore the high dimensional relevance between users and items hidden in the review information, we propose a new recommendation model enhancing interactive graph representation learning for review-based item recommendation, named IGRec. Specifically, we construct the user-review-item graph with users/items as nodes and reviews as edges. We further add the connection of the user-user and the item-item to the graph by meta-path of user-item-user and item-user-item. Then we utilize the attention mechanism to fuse edges information into nodes and apply the multilayer graph convolutional network to learn the high-order interactive information of nodes. Finally, we obtain the final embedding of user/item and adopt the factorization machine to complete the rating prediction. Experiments on the five real-world datasets demonstrate that the proposed IGRec outperforms the state-of-the-art baselines.