Reviewerly/exHarmony huggingface dataset:
Workload Characterization of a Software-as-a-Service Web Application Implemented with a Microservices Architecture

We study the workload of an Online Invoicing application with clients in the Andean region in South America. The application is offered to clients with a Software-as-a-Service model, has a microservices architecture and runs on a containerized environment on a public cloud provider. The cloud application workload described in this paper can be used as part of a workload suite comprised of different application workloads, when evaluating microservices architectures. To the best of our knowledge, this is a novel workload in the web domain and it complements other workloads publicly available. Though we make no claim of the general applicability of this workload as a "microservices benchmark", its inclusion in evaluations could aid researchers and practitioners enrich their evaluations with tests based on a real microservices-based web application. Finally, we provide some insights regarding best-practices in microservice design, as a result of the observed workload.
Implicit quantification made explicit: How to interpret blank nodes and universal variables in Notation3 Logic

Since the invention of Notation3 Logic, several years have passed in which the theory has been refined and applied in different reasoning engines like Cwm, EYE, and FuXi. But despite these developments, a clear formal definition of Notation3’s semantics is still missing. This does not only form an obstacle for the formal investigation of that logic and its relations to other formalisms, it has also practical consequences: in many cases the interpretations of the same formula differ between reasoning engines. In this paper we tackle one of the main sources of that problem, namely the uncertainty about implicit quantification. This refers to Notation3’s ability to use bound variables for which the universal or existential quantifiers are not explicitly stated, but implicitly assumed. We provide a tool for clarification through the definition of a core logic for Notation3 that only supports explicit quantification. We specify an attribute grammar which maps Notation3 formulas to that logic according to the different interpretations and thereby define the semantics of Notation3. This grammar is then implemented and used to test the impact of the differences between interpretations on practical cases. Our dataset includes Notation3 implementations from former research projects and test cases developed for the reasoner EYE. We find that 31% of these files are understood differently by different reasoners. We further analyse these cases and categorise them in different classes of which we consider one most harmful: if a file is manually written by a user and no specific built-in predicates are used (13% of our critical files), it is unlikely that this user is aware of possible differences. We therefore argue the need to come to an agreement on implicit quantification, and discuss the different possibilities.
Interested Object Detection based on Gaze using Low-cost Remote Eye Tracker

Eye tracking technology provides many advantages in human-computer interaction and human-robot interaction research. However, most studies in human-computer interaction are based on prohibitively expensive professional eye-trackers, which cannot be affordable for daily life applications. Though consumer-level low-cost eye trackers have appeared in market in recent years, they can only output raw gaze recordings and lack the high-level measurements/features for further analyzing the user’s implicit visual intention in an image. To this end, a gaze-based intention detection approach using a low cost remote eye tracker is proposed. In this study, we form the gaze point into clusters and extract gaze-based features from the clusters. Then a support vector machine (SVM) classifier is used for detecting intentional gaze clusters. According to our experiment results obtained from 15 subjects, the accuracy for classifying the gaze clusters is 97.85%. Besides, the performance of our approach for detecting user’s interested objects from the visual image of the real-life scene is comparable with the state-of-the-art method that employs an expensive professional eye tracker. The overall results suggest that our approach based on a low-cost remote eye tracker is applicable for detecting user’s interested object.
KEY FACTORS IN THE DEVELOPMENT OF UNIVERSITY MANAGEMENT INFORMATION SYSTEM BASED ON 1C:ENTERPRISE 8 PLATFORM

The actual problems of the development and implementation of existing software products for solving he problems of automating the management of the educational activities of the university are considered on the example of Irkutsk State University. A twocircuit technological model for constructing a corporate system for accounting and management of the educational processes has been formed. The properties of the 1C: Enterprise 8 technological platform in the design of systems for the management of the educational process of the university has been considered. The necessary and sufficient system forming factors of the 1C: University for the creation of an integrated automated control system for the university in the context of the post-industrial development of the educational environment are indicated. Examples of the implementation of informatization projects in Irkutsk State University based on the specified properties and the formulated two-circuit model are given.
Application of Transfer Learning for Object Detection on Manually Collected Data

This paper investigates the usage of pre-trained deep learning neural networks for object detection on a manually collected dataset for real-life indoor objects. Availability of object-specific datasets is a great challenge and the unavoidable task of collecting, processing and annotating ground truth data is laborious and time-consuming. In this paper, two famous models (AlexNet and Vgg16) have been evaluated as feature extractors in a Faster R-CNN network. Network models have been trained end-to-end on the collected dataset. The study highlights the poor performance of state of art systems when dealing with small size objects. Modifying the detector design by redesigning systems’ anchor boxes might help to tackle this problem. Detector results on the proposed dataset have been collected and compared. In addition, limitations and future work have been discussed.
Size-aware visual object tracking via dynamic fusion of correlation filter-based part regressors

Although correlation filter (CF)-based trackers have shown promising results in addressing problematic challenges of visual tracking, common holistic-wise CF-based trackers mostly drift away from the target object when undergoing partial occlusion. On the other hand, part-based models provide a prosperous basis for handling occlusion problem, due to preserving local structure of the target object. Employing local-global appearance models of the object, we propose a robust tracking algorithm based on the weighted cumulative fusion of CF-based part regressors. Indeed, we dynamically learn importance weights of each part via a multilinear ridge regression optimization model aiming at enhancing discrimination power of our tracker. To alleviate tracking drift caused by the object size changes, we further present an accurate method that jointly estimates object scale and aspect ratio by analyzing relative deformation cost of importance pair-wise parts. Also, to reduce the computational complexity, we introduce a feature sharing strategy for all constituent parts. Extensive experiments on OTB-2013, OTB-50, OTB-100, and VOT2016 datasets demonstrate that our tracker not only impressively enhances the performance of target-wise KCF tracker as its baseline but also performs favorably against state-of-the-art trackers in terms of qualitative and quantitative measures while running about 30 fps using Matlab on 3.2 GHz core-i5.
Accelerating Local Binary Pattern Networks with Software-Programmable FPGAs

Fueled by the success of mobile devices, the computational demands on these platforms have been rising faster than the computational and storage capacities or energy availability to perform tasks ranging from recognizing speech, images to automated reasoning and cognition. While the success of convolutional neural networks (CNNs) have contributed to such a vision, these algorithms stay out of the reach of limited computing and storage capabilities of mobile platforms. It is clear to most researchers that such a transition can only be achieved by using dedicated hardware accelerators on these platforms. However, CNNs with arithmetic-intensive operations remain particularly unsuitable for such acceleration both computationally as well as for the high memory bandwidth needs of highly parallel processing required. In this paper, we implement and optimize an alternative genre of networks, local binary pattern network (LBPNet) which eliminates arithmetic operations by combinatorial operations thus substantially boosting the efficiency of hardware implementation. LBPNet is built upon a radically different view of the arithmetic operations sought by conventional neural networks to overcome limitations posed by compression and quantization methods used for hardware implementation of CNNs. This paper explores in depth the design and implementation of both an architecture and critical optimizations of LBPNet for realization in accelerator hardware and provides a comparison of results with the state-of-art CNN on multiple datasets.
A Lightweight Authentication Scheme for Cloud-Centric IoT Applications

Internet of Things (IoT) is an emerging paradigm that enables people to connect with sensors and devices. Meanwhile, security and privacy stands as a vital issue in field mission which connects thousands of devices, sends sensitive information to and from users, and offloads some information to the cloud. These connected devices are susceptible to vulnerabilities and security breaches. Authentication of IoT devices is crucial to control access in public networks. As traditional security mechanisms consume too much energy, researchers are attracted to lightweight security mechanisms for resource-constrained IoT system. In this paper, we proposed a lightweight authentication scheme for cloud-centric IoT applications. The formal security verification of the scheme has been carried out using widely accepted AVISPA tool. Also, the security analysis, computational and communicational cost demonstrates the proposed lightweight scheme is secure and efficient.
Contextual Generation of Word Embeddings for Out of Vocabulary Words in Downstream Tasks

Over the past few years, the use of pre-trained word embeddings to solve natural language processing tasks has considerably improved performances on every end. However, even though these embeddings are trained on gigantic corpora, the vocabulary is fixed and thus numerous out of vocabulary words appear in specific downstream tasks. Recent studies proposed models able to generate embeddings for out of vocabulary words given its morphology and its context. These models assume that we have sufficient textual data in hand to train them. In contrast, we specifically tackle the case where such data is not available anymore and we rely only on pre-trained embeddings. As a solution, we introduce a model that predicts meaningful embeddings from the spelling of a word as well as from the context in which it appears for a downstream task without the need of pre-training on a given corpus. We thoroughly test our model on a joint tagging task on three different languages. Results show that our model helps consistently on all languages, outperforms other ways of handling out of vocabulary words and can be integrated into any neural model to predict out of vocabulary words.
The Role of Data Mining Techniques in the Decision-Making Process in Jordanian Commercial Banks

Technology is one of the most essential factors that affect the business environment of modern banking organizations, allowing new capabilities to support the decision making process at all levels of the organizations to influence the immediate working environment. The use of data mining is one of the important recommendations of the study that will affect the efficiency and effectiveness efficiency of banking operations. The use of data mining technology strengthen the knowledge base and provide all the necessary data that will assist the Jordanian commercial banking industry in its decision-making process. The purpose of this study is to examine the role of applying data mining tools in the industry and decision making in Jordanian commercial banks. To achieve this objective, the researchers will use descriptive analytical methodology based on the questionnaire distributed to the members of the study community.
Research on breakthrough and innovation of UAV mission planning method based on cloud computing-based reinforcement learning algorithm

The UAV system has evolved in the direction of intelligence and autonomy. Mission planning is an important part of autonomous drone control. The issue of route planning and task assignment in drone mission planning is studied. For the drone path planning problem in three-dimensional static threat environment, two improved ant colony algorithms are proposed, and these prior knowledges are constructed as multiple heuristic information of ants, guiding the ant’s path search, and verifying the global convergence of the algorithm. The fuzzy inference system is used to dynamically adjust the parameters of the RRT algorithm according to the real-time information of the task environment and the growth status of the RRT random tree. The experimental results show that the two improved algorithms can obtain better planning results than the single artificial potential field method and ant colony algorithm, effectively shorten the route planning time, improve the planning accuracy, and obtain the optimal flight path.
An Efficient Video Editing Application for Android based Technologies

Objective: Smartphone industry is growing day by day.According to studies, maximum smartphone users watch videos on their smartphones.With the time, android operating system is becoming popular due to the applications which are based on android SDKsthat attract the attention of the mobile phone users. Methods: However, with the increase of its popularity video editing in android devices is becoming a complex task for many reasons as well like poor video quality after editing, slow editing process, editing tool crashes while editing etc. There is a need of an attractivetool and an efficient environmentto deal with such complex tasks. Findings: This study discusses the novel and easy-to-use interface of video editing in android devices. The developed tool can perform video encoding which generates number of frames per second. We have applied image processing techniques to add images or text to any frame of video and can apply different effects like covers, sepia, gamma, rotation, snow. Then the tool can regenerate video from the frames produced after editing and application of various effects. Application:The resultant video will be quality conscious and can be generated in multiple formats like MP4, 3GP, and MOV reliably. Keywords: Android Phones, Digital Video, Image Processing, Smartphones, Video Processing, Video Editing
ITIKI Plus: A Mobile Based Application for Integrating Indigenous Knowledge and Scientific Agro-Climate Decision Support for Africa’s Small-Scale Farmers

Information and communication technologies (ICTs), especially mobile phone technology, have great potential in improving livelihoods and alleviating poverty among Africa's small-scale farmers. In particular, an effective decision support system that can aid farmers' tactical and routine level decisions has been proven to lead to increased agricultural production. In this paper, we present such a tool - ITIKI Plus intelligently integrates indigenous and scientific data and information to provide contextualised micro-level drought forecast and cropping decision information to small-scale farmers in Kenya, Mozambique and South Africa. By providing the farmers, especially women, with both tactical-level and day-to-day decision support, the farmers have been able to make decisions that are up to 98% accurate. They have also been able to increase their food production by up to 10%.
Some food for thought on the theory and practice of internet pragmatics

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Extraction of RDF Statements from Text

The vision of the Semantic Web is to get information with a defined meaning in a way that computers and people can work collaboratively. In this sense, the RDF model provides such a definition by linking and representing resources and descriptions through defined schemes and vocabularies. However, much of the information able to be represented is contained within plain text, which results in an unfeasible task by humans to annotate large scale data sources such as the Web. Therefore, this paper presents a strategy for the extraction and representation of RDF statements from text. The idea is to provide an architecture that receives sentences and returns triples with elements linked to resources and vocabularies of the Semantic Web. The results demonstrate the feasibility of representing RDF statements from text through an implementation following the proposed strategy.
Research on Keyword Extraction Algorithm Using PMI and TextRank

Keyword extraction is a basic text retrieval technique in natural language processing, which can highly summarize text content and reflect the author's writing purposes. It plays an important role in document retrieval, text classification and data mining. In this paper, we propose a TextRank algorithm based on PMI (pointwise mutual information) weighting for extracting keywords from documents. The initial transition probability of the candidate words is constructed by calculating the PMI between vocabularies, which is used for iterative calculation of the vocabulary graph model within TextRank and keyword extraction. Taking into account the mutual information between the vocabulary in the document set, the word relationship in the single document is corrected, which is helpful to improve the accuracy of document keyword extraction. Experiments show that our method achieves better performance in extracting keywords in large-scale text data.
Inferring Advertiser Sentiment in Online Articles using Wikipedia Footnotes

Online advertising platforms in partnerships with media companies typically have access to an online user's history of viewed articles. If a concerned brand (advertiser) plans to run advertisement campaigns on users exposed to negative articles, it is essential to first identify articles with negative sentiment about the brand. For an advertising platform, scalable identification of such articles with little human-annotation effort is necessary for launching campaigns soon after an advertiser signs up. In this context, generic sentiment analysis tools suffer from the lack of contextual world knowledge associated with the advertiser. Human annotation of articles for supervised approaches is laborious and painstaking. To address these problems, we propose the use of publicly available Wikipedia footnote references for an advertiser, and propagate their sentiment to several articles related to the advertiser. In particular, our proposed approach has three components: (i) automatically find Wikipedia references which have negative sentiment about an advertiser, (ii) learn distributed representations (doc2vec) of article texts referred in footnotes and other unlabeled articles, and (iii) inferring sentiment in unlabeled articles using label propagation (from references) in the doc2vec space. Our experiments spanning three real brands, and data from a major advertising platform (Yahoo Gemini) show significant lifts in sentiment inference compared to existing baselines. In addition, we share valuable insights on how article sentiment influences the online activities of a user with respect to a brand.
A Code Complexity Model of Object Oriented Programming (OOP)

The Code Complexity and Object Oriented Programming (OOP) is an import topic due to the role of OOP playing in most of the software design and architectures nowadays. In OOP there are key design concepts like Encapsulation, Polymorphisms and Inheritance that affect the coding design, structure and style. The challenge is how to minimize the Complexity in OOP and complying with the key concepts of OOP design. This paper reviews the literature on current solutions for code complexity and proposes a new model for OOP code complexity. The new model has added into OOP complexity metrics the following characteristics: abstraction and class details complexity. The proposed model is based on the following attributes selection criteria: Readability, Understandability, Maintainability, Reusability, Extensibility and Consistency of the programming code.
Modeling Context-Dependent Latent Effect Heterogeneity

Classical generalized linear models assume that marginal effects are homogeneous in the population given the observed covariates. Researchers can never be sure a priori if that assumption is adequate. Recent literature in statistics and political science have proposed models that use Dirichlet process priors to deal with the possibility of latent heterogeneity in the covariate effects. In this paper, we extend and generalize those approaches and propose a hierarchical Dirichlet process of generalized linear models in which the latent heterogeneity can depend on context-level features. Such a model is important in comparative analyses when the data comes from different countries and the latent heterogeneity can be a function of country-level features. We provide a Gibbs sampler for the general model, a special Gibbs sampler for gaussian outcome variables, and a Hamiltonian Monte Carlo within Gibbs to handle discrete outcome variables. We demonstrate the importance of accounting for latent heterogeneity with a Monte Carlo exercise and with two applications that replicate recent scholarly work. We show how Simpson’s paradox can emerge in the empirical analysis if latent heterogeneity is ignored and how the proposed model can be used to estimate heterogeneity in the effect of covariates.
A novel quantum steganography scheme based on ASCII

Based on the novel enhanced quantum representation for quantum image (NEQR), a new blind quantum steganography scheme is proposed. In this scheme, an improved quantum representation of text utilizing American Standard Code for Information Interchange (ASCII) is provided that uses two qubit sequences to store the same quantum text message. The general embedding process of the scheme is as follows: First, the cover image of size [Formula: see text] will be divided into eight blocks of size [Formula: see text] and the secret quantum text of size [Formula: see text] is scrambled by Gray code transform method. Then, the disorder quantum text is embedded into the eight blocks of cover image employing the Gray code as a judgment condition. Meanwhile, the corresponding quantum circuits are drawn. Through the analysis of all quantum circuits, it can be concluded that the scheme has a lower complexity that is [Formula: see text]. And the performance of the proposed scheme is analyzed in terms of simulation results of three items: visual quality, capacity and robustness.