Reviewerly/exHarmony huggingface dataset:
Identity Access Management via ECC Stateless Derived Key Based Hierarchical Blockchain for the Industrial Internet of Things

Recently, the adoption of the industrial Internet of things (IIoT) has optimized many industrial sectors and promoted industry “smartization.” Smart factories and smart industries connect the real and virtual worlds through cyber-physical systems (CPS). However, these linkages will increase the cyber security danger surface to new levels, putting millions of dollars' worth of assets at risk if communications in big network systems like IIoT settings are left unsecured. To solve these problems, the fundamental method is security, such as authentication and confidentiality, and it should require the encryption key. However, it is challenging the security performance with the limited performance of the sensor. Blockchain-based identity management is emerging for lightweight, integrity and persistence. However, the key generation and management issues of blockchain face the same security performance issues. First, through blockchain smart contracts and hierarchical deterministic (HD) wallets, hierarchical key derivation efficiently distributes and manages keys by line and group in the IIoT environment. Second, the pairing verification value based on an elliptic curve single point called Root Signature performs efficient public key certificate registration and verification and improves the key storage space. Third, the identity log recorded through the blockchain is the global transparency of the key lifecycle, providing system reliability from various security attacks. Keyless Signature Infrastructure (KSI) is adopted to perform efficiently via hash-based scheme (hash calendar, hash tree etc.). We analyze our framework compared to hash-based state commitment methods. Accordingly, our method achieves a calculation efficiency of O(nlog N) and a storage space saving of 60% compared to the existing schemes.
Simulating Software Evolution to Evaluate the Reliability of Early Decision-making among Design Alternatives toward Maintainability

Critical decisions among design altern seventh atives with regards to maintainability arise early in the software design cycle. Existing comparison models relayed on the structural evolution of the used design patterns are suitable to support such decisions. However, their effectiveness on predicting maintenance effort is usually verified on a limited number of case studies under heterogeneous metrics. In this article, a multi-variable simulation model for validating the decision-making reliability of the derived formal comparison models for the significant designing problem of recursive hierarchies of part-whole aggregations, proposed in our prior work, is introduced. In the absence of a strict validation, the simulation model has been thoroughly calibrated concerning its decision-making precision based on empirical distributions from time-series analysis, approximating the highly uncertain nature of actual maintenance process. The decision reliability of the formal models has been statistically validated on a sample of 1,000 instances of design attributes representing the entire design space of the problem. Despite the limited accuracy of measurements, the results show that the models demonstrate an increasing reliability in a long-term perspective, even under assumptions of high variability. Thus, the modeling theory discussed in our prior work delivers reliable models that significantly reduce decision-risk and relevant maintenance cost.
Incremental type-checking for free: using scope graphs to derive incremental type-checkers

Fast analysis response times in IDEs are essential for a good editor experience. Incremental type-checking can provide that in a scalable fashion. However, existing techniques are not reusable between languages. Moreover, mutual and dynamic dependencies preclude traditional approaches to incrementality. This makes finding automatic approaches to incremental type-checking a challenging but important open question. In this paper, we present a technique that automatically derives incremental type-checkers from type system specifications written in the Statix meta-DSL. We use name resolution queries in scope graphs (a generic model of name binding embedded in Statix) to derive dependencies between compilation units. A novel query confirmation algorithm finds queries for which the answer changed due to an edit in the program. Only units with such queries require reanalysis. The effectiveness of this algorithm is improved by (1) splitting the type-checking task into a context-free and a context-sensitive part, and (2) reusing a generic mechanism to resolve mutual dependencies. This automatically yields incremental type-checkers for any Statix specification. Compared to non-incremental parallel execution, we achieve speedups up to 147x on synthetic benchmarks, and up to 21x on real-world projects, with initial overheads below 10%. This suggests that our framework can provide efficient incremental type-checking to the wide range of languages supported by Statix.
Provenance research today: Principles, practice, problemsBy ArthurTompkins (Ed.), Lund Humphries.2020. 224 pages. £29.99

Curator: The Museum JournalEarly View Book Review Provenance research today: Principles, practice, problems edited by Arthur Tompkins. Lund Humphries, 2020. 224 pages. £29.99 Heather Saunders, Corresponding Author Heather Saunders [email protected] orcid.org/0000-0002-4393-1660 Dean of Libraries and Archives, Acadia University, Wolfville, Nova Scotia, CanadaSearch for more papers by this author Heather Saunders, Corresponding Author Heather Saunders [email protected] orcid.org/0000-0002-4393-1660 Dean of Libraries and Archives, Acadia University, Wolfville, Nova Scotia, CanadaSearch for more papers by this author First published: 31 October 2022 https://doi.org/10.1111/cura.12528Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
The essence of online data processing

Data processing systems are a fundamental component of the modern computing stack. These systems are routinely deployed online: they continuously receive the requests of data processing operations, and continuously return the results to end users or client applications. Online data processing systems have unique features beyond conventional data processing, and the optimizations designed for them are complex, especially when data themselves are structured and dynamic. This paper describes DON Calculus, the first rigorous foundation for online data processing. It captures the essential behavior of both the backend data processing engine and the frontend application, with the focus on two design dimensions essential yet unique to online data processing systems: incremental operation processing (IOP) and temporal locality optimization (TLO). A novel design insight is that the operations continuously applied to the data can be defined as an operation stream flowing through the data structure, and this abstraction unifies diverse designs of IOP and TLO in one calculus. DON Calculus is endowed with a mechanized metatheory centering around a key observable equivalence property: despite the significant non-deterministic executions introduced by IOP and TLO, the observable result of DON Calculus data processing is identical to that of conventional data processing without IOP and TLO. Broadly, DON Calculus is a novel instance in the active pursuit of providing rigorous guarantees to the software system stack. The specification and mechanization of DON Calculus provide a sound base for the designers of future data processing systems to build upon, helping them embrace rigorous semantic engineering without the need of developing from scratch.
Quality assurance expectations for online higher education: stepping stones to support post-pandemic decisions in Georgia

This article considers post-pandemic implications for higher education institutions and explores the steps required to continue with online delivery, where desirable. Strategic considerations are outlined and priorities for development analysed to ensure higher education institutions can align, in a post-pandemic future, with quality assurance expectations for online education, especially where they are new to the online environment. An analysis of the Georgian higher education context exhibited highly transferable findings as it identified the specificities of the emergency response, highlighted experiences relevant to the transition phase and recommended criteria and guidelines to support the quality assurance of post-pandemic online education. While the Georgian pandemic experience has been as challenging as in other parts of the world, the fact that the whole sector was legally denied the opportunity to offer online education before COVID-19 makes for interesting analysis and confirms that post-pandemic online higher education development is as much an institutional decision as it is a systemic leap.
RafterNet: Probabilistic Predictions in Multi-Response Regression

A fully nonparametric approach for making probabilistic predictions in multi-response regression problems is introduced. Random forests are used as marginal models for each response variable and, as novel contribution of the present work, the dependence between the multiple response variables is modeled by a generative neural network. This combined modeling approach of random forests, corresponding empirical marginal residual distributions and a generative neural network is referred to as RafterNet. Multiple datasets serve as examples to demonstrate the flexibility of the approach and its impact for making probabilistic forecasts.
Quantifying China’s financial reach up through the pandemic: The African experience

This study seeks to quantify the financial connections between China and Africa. China’s increasing investments in Africa have inevitably strengthened the relationship between China and the majority of African countries over the past decade. We find consistent effects of the Shanghai Industrial Index on African stock markets together with some evidence that these relationships strengthened following the onset of the coronavirus pandemic. Markov-Switching analysis affirms these connections while also identifying intensifying effects as we move from periods of low market volatility to periods of high volatility. The African stock markets included in the sample encompass Egypt, Kenya, Morocco, Nigeria, South Africa, Tanzania, Uganda, and Zambia.
Hydrology research articles are becoming more topically diverse

We used Natural Language Processing (NLP) to assess topic diversity in all research articles (∼75,000) from eighteen water science and hydrology journals published between 1991 and 2019. We found that individual water science and hydrology research articles are becoming increasingly diverse in the sense that, on average, the number of topics represented in individual articles is increasing, which may be a sign of increasing interdisciplinarity. This is true even though the body of water science and hydrology literature as a whole is not becoming more topically diverse. Topics with the largest increases in popularity were Climate Change Impacts, Water Policy & Planning, and Pollutant Removal. Topics with the largest decreases in popularity were Stochastic Models and Numerical Models. At a journal level, Water Resources Research, Journal of Hydrology, and Hydrological Processes are the three most topically diverse journals among the corpus that we studied.
Two-phase fuzzy feature-filter based hybrid model for spam classification

A spam message is forced data in a public, private or commercial network that occupies resources and affects the reliability of the network. This paper investigates a two-level filter-based hybrid model to identify spam content or messages accurately. At level-1 of this model, a high-level filter is incorporated for removing the non-relevant and non-significant features and contents. At level-2, a fuzzy-based composite evaluator is integrated for low-level filtration and to identify the most contributing and effective features. In this composite filter, ChiSquare and ReliefF rankers are computed on each significant feature. Two-phase fuzzy is applied to these ranking methods for generating a reduced and relevant featureset. In the final stage, the Naive Bayes and random forest classifiers are combined using the majority voting method to generate a probabilistic score and detect the spam messages. The proposed model is implemented on CSDMC2010 SPAM, spambase, and SMS Spam Collection datasets. The analytical evaluation is conducted on the error and accuracy-based performance measures. The comparative analysis is done against various conventional filters, classifiers, and stage-of-art methods. The comparative results identified that the proposed model achieved an average accuracy of 98.80% on CSDMC2010, 97.79% on spambase, and 98.84% on SMS Spam collection datasets and outperformed the existing conventional and recent algorithms and models.
Image restoration of luminous objects in dense fog

Images captured in dense fog are always severely degraded. For luminous objects, the luminance and contrast of the image are severely reduced due to the strong absorption of suspended particles. In this case, owing to the influence of forward scattered light and detector noise, the signal-to-noise ratio of the image is greatly reduced. Traditional image dehazing algorithms will enhance the noise while improving the image contrast. Here a dynamic equation-based filter is proposed, which can strongly suppress speckle and noise while improving target contrast. Based on this filter, we design an image restoration method for luminous targets in dense fog. The effectiveness of the proposed method is verified by experiments. The experimental results show that the proposed method has a good recovery effect on the luminous targets in dense fog with an optical thickness of 4.51. The proposed method has better performance than the traditional dehazing algorithms and conventional filtering method.
MSTN: A Multi-granular Spatial–Temporal Network for video-based person re-identification

With the popularization of surveillance cameras, public-safety related applications requiring the functionality of video-based person re-identification (Re-ID) thrive. Re-ID aims at accurately identifying a person-of-interest across video sequences from multiple cameras. Existing methods usually focus on either spatially salient regions, or temporal features among frames of fixed intervals (i.e., either short- or long-term temporal features), resulting in the under-utilization of neglected features and hence moderate identification accuracy. To achieve high Re-ID accuracy, we propose a novel framework termed Multi-granular Spatial–Temporal Network (MSTN) , that facilitates full utilization of spatial–temporal features for video-based person Re-ID. Within MSTN, a Temporal Kernel Attention (TKA) module is proposed to adaptively capture both short- and long-term temporal relationships; a Feature Disentanglement Spatial Attention (FDSA) module is further proposed to mine spatially salient and subtle features. Extensive experiments on the MARS dataset demonstrate that MSTN can achieve high identification accuracy, exhibiting 86.1% in terms of mAP and 91.0% in terms of Rank-1, notably higher than state-of-the-art comparison schemes.
Mixture models with decreasing weights

Decreasing weight prior distributions for mixture models play an important role in nonparametric Bayesian inference. Various random probability measures with decreasing weights have been previously explored and it has been shown that they provide an efficient alternative to the more traditional Dirichlet process mixture model. This ordering of the weights implicitly alleviates the so-called label switching problem, as larger weights correspond to larger groups. A general procedure to define any decreasing weights model based on a characterization of a discrete random variable which also allows for an easy and generic sampling algorithm for estimating the model is provided. An exact representation for the number of expected components is given. Finally, the performance of the mixture model on simulated data sets is investigated numerically.
Micro-Calcification Classification Analysis in Mammogram Images with Aid of Hybrid Technique Analysis

Breast cancer is the leading cause of death in women. Early identification can contribute significantly to improving the survival rate. For diagnosis and accurate therapy automatic detection of micro-calcification is therefore essential. In the paper, an automated technique is utilized in the mammogram images according to their micro-calcification classification. The automated technique is working with the combination of Deep Belief Neural Network (DBNN) and Chimp Optimization Algorithm (COA). The proposed method is working with three phases such as pre-processing phase, feature extraction, and classification phase. In the pre-processing phase, a median filter is utilized to remove unwanted information from the images. In the feature extraction phase, Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), and Hu moments are utilized to extract essential features from the mammogram images. After that, the detection and classification are performed on the mammogram images according to their micro-calcifications with the utilization of the proposed advanced deep learning method. From the classification stage, the normal and abnormal images are identified from the images. The proposed method is implemented in the MATLAB platform and analyzed their statistical performances like accuracy, sensitivity, specificity, precision, recall, and F-measure. To evaluate the effectiveness of the proposed method this is compared with the existing method such as Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).
Citation Recommendation Using Deep Learning Approach

Citation recommendation is a technique that assists an academic publisher or scholar in identifying a selection of relevant works that can be cited whilst writing a paper. In comparison with prior years, it is clear that there is currently a deluge of articles being published every year. As a result, the traditional way to identify valid citations appears to be a very difficult process, as there appears to be massive flow of data. In this research, we will discuss an innovative way to solve the citation recommendation task. To begin, we employed a probabilistic model known as the Gaussian mixture model, to minimise the search space, which we subsequently integrated with other neural architectures such as ANN and a hybrid model, CNN-LSTM. Finally, we performed a thorough experimental investigation employing a variety of evaluation metrics.
Compile Time Analyzer to Detect Violations of Programming Guidelines

The maintenance of software is the biggest challenge in the software industry today. The quality of code affects maintainability of software which can be improved by following well-defined programming guidelines. In academia as well, we are required to check the quality of students’ code and if the students have used the expected approach. We propose a method to find violations of guidelines like length of identifiers, use of global variables and depth of control structures. We also intend to check if a particular programming approach has been used such as dynamic programming or recursion. This paper discusses a method through which the given code can be analyzed statically to detect for the violations of selected guidelines. We have a library of guidelines to choose from and provide a novel mechanism for the user to specify a new guideline in a structured form developed by us. This in turn results in the generation of code which performs the checks.
Blockchain-Based Incentive System for Enhancing Student’s Participation in Co-Curricular and Extracurricular Activities

An enriching university experience not only consists of demonstrating excellence in the academic curriculum but also applying that knowledge to solve challenging problems in society using technology. Students in a university are exposed to several such opportunities in the form of coding competitions, hackathons, research paper presentations, etc. that are beyond their academic curriculum. Several students perform exceptionally well in these extracurricular. We propose a use of blockchain for universities to reward students for their co-curricular and extracurricular accomplishments, thus incentivizing and acknowledging their achievement. This, in turn, creates a holistic environment for students to participate and achieve more. Our solution involves faculties, to transparently verify the authenticity of the accomplishment and then reward students. Rewards are in the form of VJCoins, the native cryptocurrency of the blockchain implemented in VJTI Mumbai that can be consumed in on-campus activities such as cafeteria, library, Xerox store and gifting others, thus creating a circular economy within campus.
Artificial Intelligence in Game Programming

As a child we have Queryplayed a lot of video games and we are constantly seeing the evolution of video games. What has been the most interesting aspect? Animations of-course but what has made the game more interactive is development of NPC characters. AI has played a bigger part in this. And what has made the game even more realistic is game physics. It has brought even more interesting and interactive environments to the game. Even if it is a virtual world, it is well defined and logical to the game and the creator. What we are trying to explain in this paper is to show how intelligent the preset NPC looks like thinking more strategically and logically navigating through the environment while pursuing the target with more efficient path changing all the time and avoiding the obstacles in the environment. While being more logical in the gameplay the NPC should have physics and animation systems connected together with the behavior of the NPC to make it blend in the environment.
Readability Enhancement for Kannada Text Documents Through OCR

Text documents are progressively being scanned in order to make them easier to acquire and maintain in today’s world. Although scanned papers have a better texture, they can be maintained for the generations. An optical character recognition (OCR) system converts scanned text into machine-editable characters. Professional optimization techniques for publications available in English, Japanese, Chinese, Arabic, and a few Indian characters are now widely available. Karnataka, one of India’s southern states, has Kannada as its official language. The implementation of OCR for Kannada language is presently a subject of great interest. The Kannada language has a significant set of characters, some of which are conceptually related. As a result, designing an OCR for this language is much more complex than implementing one for a language such as English. The fact that progress into building OCRs for Kannada should still be in its initial phases necessitates the creation of this research study. The objectives of this paper are to go beyond the Kannada script’s peculiarity, the challenges they provide to the recognizing, techniques mentioned in the literature, identification prediction accuracy, and a comparative with several other algorithms in depth.