Reviewerly/exHarmony huggingface dataset:
Resource Recommender for Cloud-Edge Engineering

The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).
On the Integer Polynomial Learning with Errors Problem

Several recent proposals of efficient public-key encryption are based on variants of the polynomial learning with errors problem (PLWE\(^f\)) in which the underlying polynomial ring \(\mathbb {Z}_q[x]/f\) is replaced with the (related) modular integer ring \(\mathbb {Z}_{f(q)}\); the corresponding problem is known as Integer Polynomial Learning with Errors (I-PLWE\(^f\)). Cryptosystems based on I-PLWE\(^f\) and its variants can exploit optimised big-integer arithmetic to achieve good practical performance, as exhibited by the ThreeBears cryptosystem. Unfortunately, the average-case hardness of I-PLWE\(^f\) and its relation to more established lattice problems have to date remained unclear.We describe the first polynomial-time average-case reductions for the search variant of I-PLWE\(^f\), proving its computational equivalence with the search variant of its counterpart problem PLWE\(^f\). Our reductions apply to a large class of defining polynomials f. To obtain our results, we employ a careful adaptation of Rényi divergence analysis techniques to bound the impact of the integer ring arithmetic carries on the error distributions. As an application, we present a deterministic public-key cryptosystem over integer rings. Our cryptosystem, which resembles ThreeBears, enjoys one-way (OW-CPA) security provably based on the search variant of I-PLWE\(^f\).
An RVML extension for modeling fuzzy rule bases

Rules are still the most widespread way to represent expert knowledge despite the popularity of semantic technologies. The effective use of rules in decision-making in the case of inaccurate or uncertain information requires the development of specialized means and software for visual and generative programming. This paper considers an extension of the Rule Visual Modeling Language called FuzzyRVML designed for modeling fuzzy rule bases. FuzzyRVML supports a fuzzy datatype, concepts of a linguistic variable, terms, and certainty factors. The descriptions of FuzzyRVML basic elements, main constructions, and an illustrative example containing FuzzyCLIPS source code generation are presented. The evaluation and implementation of this notation are made based on the Personal Knowledge Base Designer software.
Cryptographic Solution for Security Problem in Cloud Computing Storage During Global Pandemics

Cloud computing has emerged as a potential substitute over traditional computing systems during the time of the COVID-19 pandemic. Almost all organizations shift their working from conventional ways to the online form of working. Most of the organizations are planning to permanently change some % of their work to online WFH (Work from Home) mode. There are numerous benefits of using cloud services in terms of cost, portability, platform independence, accessibility, elasticity, etc. But security is the biggest barrier when one wants to move towards cloud computing services, especially the cloud storage service. To overcome the problem of security in cloud storage systems, we have presented an approach for data security in cloud storage. The proposed approach uses the cryptographic methods and provides security and monitoring features to the user data stored in cloud storage systems. The proposed approach continuously monitors user’s data for any kind of modification by attackers. Thus, approach not only provides data security but also improves user’s trust on cloud based storage services.
Automatic Mobile Warning System against People with Elevated Body Temperature

The paper proposes a system that allows for the automatic detection of people with elevated body temperature and estimates distance from them using a smartphone-type device and a single mobile thermal camera. The algorithm automatically finds and selects humans with the highest temperature, and tracks changes in their position in an image sequence. On the basis of the change in the position of the human head in the image, in subsequent frames, the algorithm estimates the distance between camera and human. Owing to the use of fast machine-learning methods, the proposed system can immediately alert the user about the presence of a people with an elevated temperature at a distance of 1–3 m as soon as it appears in the field of view of the camera. The effectiveness of the algorithm was assessed as the ratio of correct distance classifications in the test image set to the total number of test images. Values ranging from 73% to 100% were obtained for over 4000 images of humans at different distances. The proposed method allows for the quick and completely automatic warning aboutt people with elevated temperature, and can be used in popular Android mobile devices.
Radiação global e difusa diária na região de transição Cerrado-Amazônia brasileira

This study aimed to analyze the seasonal variations in atmospheric transmissivity and solar radiation (global and diffuse) on the horizontal surface in Sinop, Mato Grosso (MT) (11.865°S, 55.485°W, and altitude of 371 m) from 06/02/2011 to 12/31/2014. The values of diffuse radiation were measured using the Melo-Escobedo-Oliveira (MEO) shadow ring, with application of astronomical, geometric, and anisotropic correction factors. The analysis of atmospheric transmissivity was based on the classification of sky cover as cloudy, partly cloudy, partially clear, or clear. The diffuse radiation showed similar behavior to the radiation at the top of the atmosphere, reaching a maximum between October and April (rainy season), while the global radiation displayed higher levels during the dry season (May to September). The average daily global radiation ranged from 22.75±0.61 MJ m−2 d−1 in August to 16.44±1.45 MJ m−2 d−1 in January. In Sinop, cloudy and partly cloudy skies occurred on 45.6% of days and atmospheric transmissivity of global radiation was greater than 55% on 54.6% of days. The variations in diffuse radiation in the region were influenced by cloudiness and the concentration of biomass burning aerosol particles. The diffuse radiation can represent 8.02%–99.12% of the global radiation and 5.33%–29.01% of solar energy incident at the top of the atmosphere.
Probabilistic Non-Negative Matrix Factorization with Binary Components

Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data. However, its performance largely depends on the assumption of a fixed number of features. This work proposes a new probabilistic non-negative matrix factorization which factorizes a non-negative matrix into a low-rank factor matrix with constraints and a non-negative weight matrix. In order to automatically learn the potential binary features and feature number, a deterministic Indian buffet process variational inference is introduced to obtain the binary factor matrix. Further, the weight matrix is set to satisfy the exponential prior. To obtain the real posterior distribution of the two factor matrices, a variational Bayesian exponential Gaussian inference model is established. The comparative experiments on the synthetic and real-world datasets show the efficacy of the proposed method.
Fine-grained Interpretation and Causation Analysis in Deep NLP Models

Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi, Nadir Durrani. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials. 2021.
An Adaptive Mechanism With Cooperative Coevolution and Covariance for Differential Evolution

Differential evolution (DE) is an evolutionary algorithm widely used to solve optimization problems with different characteristics in fields where actions and decisions depend on numerical data such as engineering, economics, and logistics.In this paper, an adaptive differential evolution mechanism with cooperative co-evolution and covariance (A-CC/COV-DE) is proposed to overcome the low efficiency of differential evolution when solving large-scale numerical optimization problems, especially when the correlation between the variables of the problem is unknown.An unknown correlation of variables hinders DE from achieving an optimal search process since different types of correlations ideally require distinct optimization strategies.According to the separability of variables, the appropriate evolutionary strategy is selected adaptively.For separable functions, cooperative coevolution is adopted.After using extended differential grouping to split the problem, the sub-components are optimized by differential evolution.This reduces the dimensionality and complexity of the problem, improving its convergence speed and global search ability.For non-separable functions, a covariance matrix is calculated, and then the eigenvector is used to rotate the coordinate system.This leads to eliminate the correlation between variables and improve the search efficiency of differential evolution.We evaluated the performance of A-CC/COV-DE on the CEC 2014 test suite and compared it with state-of-the-art differential evolution algorithms.The experimental results show that our proposal is quite competitive with recent algorithms.
Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.
Fooling Partial Dependence via Data Poisoning

Abstract Many methods have been developed to understand complex predictive models and high expectations are placed on post-hoc model explainability. It turns out that such explanations are not robust nor trustworthy, and they can be fooled. This paper presents techniques for attacking Partial Dependence (plots, profiles, PDP), which are among the most popular methods of explaining any predictive model trained on tabular data. We showcase that PD can be manipulated in an adversarial manner, which is alarming, especially in financial or medical applications where auditability became a must-have trait supporting black-box machine learning. The fooling is performed via poisoning the data to bend and shift explanations in the desired direction using genetic and gradient algorithms. We believe this to be the first work using a genetic algorithm for manipulating explanations, which is transferable as it generalizes both ways: in a model-agnostic and an explanation-agnostic manner.
Syntactic Annotation in the I3rab Dependency Treebank

Arabic dependency parsers have a poor performance compared to parsers of other languages. Recently the impact of annotation at lexical level of dependency treebank on the overall performance of the dependency parses has been extensively investigated. This paper focuses on the impact of coarse-grained and fine-grained dependency relations on the performance of Arabic dependency parsers. Moreover, this paper introduces the annotation rules for I3rab dependency treebank. Experimentally, the obtained results showed that having an appropriate set of dependency relations improves the performance of an Arabic dependency parser up to 27.55%.
A Conversation about LibGuides and SubjectsPlus

Monica Ruane Rogers: Today we’re going to talk about library CMS software, and specifically LibGuides and SubjectsPlus. We’ll also dip into a discussion about proprietary, commercial products versu...
Una aplicación basada en ontologías del perfil básico del estándar ISO/IEC 29110 empleando Protégé

Objetive: provide a scalable environment to represent the concepts of the basic profile of the ISO / IEC 29110 standard for Small and Medium-sized Enterprises (SMEs) and their relationships. Methodology: In this paper we propose a novel approach for generating an ontology related to the ISO/IEC 29110 basic profile. We follow some steps: (i) modeling the domain by using executable pre-conceptual schemas; (ii) creating equivalences between pre-conceptual schemas and ontology elements; (iii) translating such equivalences into Protégé, an ontology-based environment; and (iv) creating rules for inferring knowledge from the ontology in order to overcome the aforementioned problems. Results: We create an ontology related to concepts of the ISO/IEC 29110 and their relations in its basic profile. We also answer questions in order to ease the implementation process of the ISO/IEC 29110 basic profile. Conclusions: The resulting ontology serves as support for VSEs and academics when implementing or teaching the basic profile of the ISO/IEC 29110.&#x0D;
Fine-Grained Simplification of Medical Documents

Easy access to medical and health information for children, foreigners and patients is an important issue for the modern society and research. Indeed, misunderstanding of medical and health information by patients may have a negative impact on their healthcare process and health. Even if several simplification guidelines exist, they are difficult to use by medical experts (i.e. lack of time, difficulty to respect the criteria). Existing simplification systems mainly address some lexical or syntactic transformations. We propose to combine lexical and syntactic simplifications within one rule-based system and to make the process fine-grained thanks to a better control of the grammaticality of sentences.
NIST Lightweight Cryptography Standardization Process: Classification of Second Round Candidates, Open Challenges, and Recommendations

In January 2013, the National Institute of Standards and Technology (NIST) announced the CAESAR (Competition for Authenticated Encryption: Security, Applicability, and Robustness) contest to identify authenticated ciphers that are suitable for a wide range of applications. A total of 57 submissions made it into the first round of the competition out of which 6 were announced as winners in March 2019. In the process of the competition, NIST realized that most of the authenticated ciphers submitted were not suitable for resource-constrained devices used as end nodes in the Internet-of-Things (IoT) platform. For that matter, the NIST Lightweight Cryptography Standardization Process was set up to identify authenticated encryption and hashing algorithms for IoT devices. The call for submissions was initiated in 2018 and in April 2019, 56 submissions made it into the first round of the competition. In August 2019, 32 out of the 56 submissions were selected for the second round which is due to end in the year 2021. This work surveys the 32 authenticated encryption schemes that made it into the second round of the NIST lightweight cryptography standardization process. The paper presents an easy-to-understand comparative overview of the recommended parameters, primitives, mode of operation, features, security parameter, and hardware/software performance of the 32 candidate algorithms. The paper goes further by discussing the challenges of the Lightweight Cryptography Standardization Process and provides some suitable recommendations.
Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Abstract Estimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across different image regions can be used to localize splicing in double JPEG tampered images. Traditional model-based approaches work under specific assumptions on the relationship between the first and second compression qualities and on the alignment of the JPEG grid. Recently, a deep learning-based estimator capable to work under a wide variety of conditions has been proposed that outperforms tailored existing methods in most of the cases. The method is based on a convolutional neural network (CNN) that is trained to solve the estimation as a standard regression problem. By exploiting the integer nature of the quantization coefficients, in this paper, we propose a deep learning technique that performs the estimation by resorting to a simil-classification architecture. The CNN is trained with a loss function that takes into account both the accuracy and the mean square error (MSE) of the estimation. Results confirm the superior performance of the proposed technique, compared to the state-of-the art methods based on statistical analysis and, in particular, deep learning regression. Moreover, the capability of the method to work under general operative conditions, regarding the alignment of the second compression grid with the one of first compression and the combinations of the JPEG qualities of former and second compression, is very relevant in practical applications, where these information are unknown a priori.
Philosophical understanding of the concept of success and its modern interpretation by students of a technical university

Introduction. The globalization of social and economic processes, the digitalization of all spheres of human activity, the increase in competition leads to a rethinking of the phenomenon of success. The purpose of the study is to analyze the attitude and motivation to success of students. Materials and methods. The survey involved students of 1-3 courses of the Moscow Aviation Institute (MAI) (95 people). To diagnose the motivation for success of students, the method of T. Ehlers was used. Methods of descriptive statistics (arithmetic mean) were used. The results of the study. The results obtained after the survey and this methodology allow us to conclude that, in general, students of 1-3 courses are motivated to succeed. Thus, the preferred success criteria for 54% of 1st year respondents are recognition in a peer group and in social networks. For 63 % of the respondents, the 2nd year – financial well-being, for 68% of the respondents, the 3rd year-professional implementation. The overall indicator of the respondents ' motivation for success corresponds to the average level of education. The study showed that in the course of forming their own attitude to success, the individual is based on the value orientations that have developed in the context of the current situation of the development of society, and also allows them to identify problem points in the training of future specialists. Conclusion. Learning a foreign language for students in assessing their own success is a prerequisite for both educational and future professional activities. The effectiveness was determined using numerical meters that allow accurate diagnosis of the success of the survey participants.