Reviewerly/exHarmony huggingface dataset:
Pre-Compensated Proportional Derivative Sliding Mode Controller Design for Image Based Visual Servoing

This study deals with image based visual servoing (IBVS) of robot manipulators in the presence of uncertainties. The proposed control technique is composed of a pre-compensated term, proportional-derivative (PD) controller and a sliding mode controller (SMC) to reach fast, accurate and robust motions. Practical stability of the closed-loop system is guaranteed by using Lyapunov method. Simulation results are used to demonstrate the effectiveness of the proposed controller in the normal and uncertain conditions.
Macrocanonical Models for Texture Synthesis

In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates between sampling and minimizing. We present experiments with neural network features and study the drawbacks and advantages of using this sampling scheme.
Victoria Vesna: Inviting Meaningful Organic Art–Science Collaboration

We asked Victoria Vesna for an interview upon investigating her recent collaborative project, Noise Aquarium, through which, together with an interdisciplinary group, she brings awareness to a general audience of underwater noise pollution in relation to the plankton and their plight of coexistence with microplastics in the ocean. The discussion that ensued resonated as to fruitful artistic process, contribution to science, and the organic nature by which many of her most meaningful collaborations evolve to produce art.
Text mining as a facilitating tool for deploying blockchain technology in the intellectual property rights system

The aim of the study is to introduce a new application of machine-learning techniques (text mining, clustering and classification) and the blockchain technology within the intellectual property rights (IPRs) management system. Using such machine-learning techniques facilitates the management process of intellectual properties (IPs) and makes it more efficient. Additionally, using the blockchain technology for IPRs management purposes enables all stakeholders to utilise the extracted data of the IP objects from the blockchain network. In this study, a text-mining technique was used to identify the two types of IP documents based on specific categories, namely, patent and trademark. In order to achieve this objective, a range of machine-learning techniques was used for 5,500 patent documents and 400 trademark documents. The results of the logistic regression model showed a high level of prediction accuracy of document type at the pre-registration stage on the blockchain network. This high level of prediction accuracy demonstrates that using machine-learning and text-mining techniques will facilitate the IPRs management system. This new application of specific machine-learning techniques in the IPRs management process contributes essentially to solving the problem in a conventional IPRs system associated with rights protection and data availability.
TEKNIK PENGUJIAN BOUNDARY VALUE ANALYSIS PADA  APLIKASI LEARNING MANAGEMENT SYSTEM POLINELA

This study discusses the testing process of the Lampung State Polytechnic e-learning application called the Polinela Learning Management System (LMS). The testing phase is one of the stages in the development cycle of a software. This stage is a series of activities carried out to find errors or shortcomings of a software or computer application. There are several techniques used to test software including black box testing techniques. In this study using black box testing with a boundary value analysis technique to test e-learning applications that have been applied to the Informatics Management program of the Lampung State Polytechnic. The boundary value analysis technique works by determining the lower and upper limit values of the data to be tested, then the data is inputted into the LMS Polinela application. The stages of research carried out begin by determining the functionality to be tested, designing test scenarios, determining the data to be tested, determining the upper and lower limit values according to the database structure that has been created, conducting testing experiments, documenting the results of research, and drawing conclusions. The test results show that there were no errors in the LMS Polinela application when validating the data to be processed.
Foreword to the M4 Competition

Forecasts are always wrong, otherwise, they are merely deterministic calculations. Besides leveraging advanced forecasting methods, post-processing has become a standard practice for solar forecasters to improve the initial forecasts. In this review, the post-processing task is divided into four categories: (1) deterministic-to-deterministic (D2D) post-processing, (2) probabilistic-to-deterministic (P2D) post-processing, (3) deterministic-to-probabilistic (D2P) post-processing, and (4) probabilistic-to-probabilistic (P2P) post-processing. Additionally, a total of ten overarching thinking tools, namely, (1) regression (D2D), (2) filtering (D2D), (3) resolution change (D2D), (4) summarizing predictive distribution (P2D), (5) combining deterministic forecasts (P2D), (6) analog ensemble (D2P), (7) method of dressing (D2P), (8) probabilistic regression (D2P), (9) calibrating ensemble forecasts (P2P), and (10) combining probabilistic forecasts (P2P), are proposed. These thinking tools can be thought of as the “style” or “mechanism” of post-processing. In that, the utilization of thinking tools circumvents the common pitfalls of classifying the literature by methods (e.g., statistics, machine-learning, or numerical weather prediction), which often leads to a “who used what method” type of roster review that is clearly ineffective, non-exhaustive, and dull. When myriads of post-processing methods are mapped to countable few thinking tools, it allows solar forecasters to enumerate the styles of adjustment that could be performed on a set of initial forecasts, which makes a post-processing task clearly goal-driven. Besides the thinking tools, this paper also emphasizes on the value of post-processing, and provides an outlook for future research. Although this paper is revolved around solar, the materials herein discussed can also be applied to wind and other forecasting areas.
Some Specific Examples of Attacks on Information Systems and Smart Cities Applications

In this chapter, are enlisted some specific examples of attacks in information systems and smart cities applications. The chapter consists of five parts. In the first and second part, the basic introductory considerations and some of our previous research in the field of security of information systems and security management in the cyberspace are listed. In the third part, are given specific examples of XSS and CSRF attacks. In addition, is given a specific implementation of the executive script, which has a task for the collection of confidential information in smart environments. In the fourth part, is listed the procedure for attacking hash digest data as well as the procedure for hiding information using the Python programming language and the Kali Linux environment. Finally, lists the final considerations and gives suggestions for further research in the field of cybersecurity attacks on smart cities.
Azure Security Center

Azure Security Center is Microsoft’s centralized dashboard solution for all things security, whether in Azure or in a hybrid topology.
Investigating Engineering Data by Probabilistic Measures

A critical issue for data-based engineering is a lack of descriptive labels for the measured data. For many engineering systems, these labels are costly/impractical to obtain, and as a result, conventional supervised learning is not feasible. This article outlines a probabilistic framework for the investigation and labelling of engineering datasets. Two alternative probabilistic measures are suggested to define the most informative observations to investigate and annotate, in order to maximise the classification performance of a statistical model.
Revolutionizing Education through Artificial Intelligence: Fuzzy Multiple Attribute Decision Making Approach for Determining the Best Vocational High School

Vocational High School has its own charm for prospective students. Graduates have ready-to-work capability based on the skills needed in the workplace. Decision Making System using Fuzzy Multiple Attribute Decision Making with Weighted Product method as the best vocational high school determinant method is an interactive computer-based system that can help decision makers to find the best alternative based on predetermined criteria. In this case the intended alternative is to determine the best vocational high school based on the criteria by finding the weight value for each attribute. The criteria needed in decision making namely facility, accreditation, quality, student (non-academic achievement), cost, human resources teacher.
The application of fractional wavelet transform in image enhancement

The fractional wavelet transform is a generalized extension of the wavelet multi-resolution analysis in time-fractional frequency domain. It can characterize signal features in time domain and fractional frequency domain. Based on the multi-resolution analysis of fractional wavelet, the process of decomposition and reconstruction of discrete fractional wavelet transform is derived, and it is extended to two dimensions. According to the characteristic that the subband energy of fractional wavelet transform varies with the order P, a novel image enhancement method is proposed. All the fractional wavelet coefficients at large scale and low resolution are retained, and a nonlinear adaptive gain method is used for the high-resolution subband coefficients. The experimental results show that the performance of this method is better than the traditional wavelet enhancement method both in vision effect and contrast improvement index.
The New Trends in Research on Social Responsibility of the University

The idea of Social Responsibility of the University has evolved during the last three decades. Due to rapid social and economical changes, relations between higher education institutions and their stakeholders have also changed drastically. Awareness of society and its expectations towards HEIs grew worldwide and a new and deep research project should be established to develop universities’ response to societal needs.
Library Provision of Intellectual Access to Open Access Journal Articles

Academic libraries provide intellectual access (discovery and procurement) to the full text of electronic journal articles through traditional library technologies like discovery layers, link resolver software and knowledge bases. These technologies mainly rely on accurate title-level metadata to successfully deliver journal articles to library users. Open Access articles pose a difficulty for many participants in the e-journal supply chain, including libraries and publishers, as Open Access status is a property of the article, not the title. A review of the literature examines: the impact of Open Access on intellectual access through traditional library technologies, current proposed solutions, and emerging technologies.
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization

Colours are everywhere. They embody a significant part of human visual perception. In this paper, we explore the paradigm of hallucinating colours from a given gray-scale image. The problem of colourization has been dealt in previous literature but mostly in a supervised manner involving user-interference. With the emergence of Deep Learning methods numerous tasks related to computer vision and pattern recognition have been automatized and carried in an end-to-end fashion due to the availability of large data-sets and high-power computing systems. We investigate and build upon the recent success of Conditional Generative Adversarial Networks (cGANs) for Image-to-Image translations. In addition to using the training scheme in the basic cGAN, we propose an encoder-decoder generator network which utilizes the class-specific cross-entropy loss as well as the perceptual loss in addition to the original objective function of cGAN. We train our model on a large-scale dataset and present illustrative qualitative and quantitative analysis of our results. Our results vividly display the versatility and the proficiency of our methods through life-like colourization outcomes.
Fingerprint Protected Password Authentication Protocol

With the rapid development of industrial Internet of things (IIOT), a variety of cloud services have been deployed to store and process the big data of IIOT. The traditional password only authentication is unable to meet the needs of security situation in IIOT. Therefore, a lot of mobile phone assisted password authentication schemes have been proposed. However, in existing schemes, the secret information is required to be stored in the user’s mobile phone. Once the phone is lost, the secret information may be obtained by the opponent, which will bring irreparable loss to the user. To address the above problems, we propose a fingerprint protected password authentication scheme which has no need to store the secret parameter in the mobile phone. When a user logs in, he uses his mobile phone to generate the private key which is used to decrypt the encrypted text generated during the registration phase. The process of generating the private key needs to enter the password and the fingerprint. When the computer interacts with the mobile phone, the user’s password will be blinded so that it can protect the user’s password from adversary’s attacks. Theoretical analysis and experimental results show that our scheme improves the security of the user’s secret. Meanwhile, our scheme can resist the opponent’s dictionary attacks, replay attacks, and phishing attack. Our scheme can reduce the storage pressure of the mobile phone and is easy to deploy.
Dolev-Yao Theory with Associative Blindpair Operators

In the context of modeling cryptographic tools like blind signatures and homomorphic encryption, the Dolev-Yao model is typically extended with an operator over which encryption is distributive. The intruder deduction problem has a non-elementary upper bound when the extended operator is an Abelian group operator. Here we show that the intruder deduction problem is DEXPTIME-complete when we restrict the operator to satisfy only the associative property. We propose an automata-based analysis for the upper bound and use the reachability problem for alternating pushdown systems to show the lower bound.
Using Established UX Design Techniques and Visual Enhancements to Redesign an Enterprise Mobile App and Improve Employee Productivity and Engagement

This paper describes our work to improve how IBM employees install IBM mobile apps through the design and development of a native IBM App Store for iOS. Using established UX design techniques such as usability testing and user surveys, we redesigned a legacy hybrid app to have a clean, modern interface leveraging Apples’ latest iOS design patterns, and introduced a visual hierarchy that keeps the experience focused on the core user tasks. The improved app allows users to find and install apps more quickly, through an engaging UX that users prefer.
Sufficient Conditions for Efficient Indexing Under Different Matchings

The most important task derived from the massive digital data accumulation in the world, is efficient access to this data, hence the importance of indexing. In the last decade, many different types of matching relations were defined, each requiring an efficient indexing scheme. Cole and Hariharan in a ground breaking paper [Cole and Hariharan, SIAM J. Comput., 33(1):26-42, 2003], formulate sufficient conditions for building an efficient indexing for quasi-suffix collections, collections that behave as suffixes. It was shown that known matchings, including parameterized, 2-D array and order preserving matchings, fit their indexing settings. In this paper, we formulate more basic sufficient conditions based on the order relation derived from the matching relation itself, our conditions are more general than the previously known conditions.
An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach

With around 330 million people around the globe tweet 6000 times per second to express their feelings about a product, policy, service, or an event. Twitter message majorly consists of thoughts. Thoughts are mostly expressed as a text and it is an open challenge to extract some insight from free text. The scope of this work is to build an effective tweet level sentiment classification framework that may use these thoughts to know collective sentiment of the folk on a particular subject. Furthermore, this work also analyses the impact of proposed tweet level recursive text pre-processing approach on overall classification results. This work achieved up to 4 points accuracy improvement over baseline approach besides mitigating feature vector space.
Some Effective Factors on the Webometric Status of Selected Universities of Medical Sciences: Lessons Learned from Iran

Web is considered an important tool for formal and informal communication and cooperation among members of the community including researchers. Universities' websites played a significant role in the development of social structure through the creation of cyberspace. We aimed to evaluate the growth and Webometrics rankings of the country's medical universities web sites base on interventional approach.This interventional study assessed Iranian universities of medical sciences' websites from the periods of Jan 2015, Jul 2015 and Jan 2016. Medical universities websites were ranked based on four Webometrics indicators; PRESENCE, VISIBILITY, OPENNESS, and EXCELLENCE. To enhance the ranking of websites, 4 strategies were introduced in 3 seminars conducted from Jan to Jul 2015. Information needed from these 30 medical universities were obtained from Webometrics Ranking of World Universities based on 4 indicators and were collected in 2 steps (before and after introduction of strategies).About 21% to 70% of the country's medical universities have increased ranking after the interventions while 9% to 30% obtained a downward trend in their rankings, Tehran University of Medical Sciences obtained the highest rank followed by Shahid Beheshti University of Medical Sciences and the third rank was obtained by Shiraz University of Medical Sciences.The presence of websites play a vital role in the academic community. Doubtlessly, the idea of designing and launching a website without any knowledge of the principles and standards would be problematic and impossible.
Material flow analysis in indentation process by 3D Digital Image Correlation

Focusing on the application of the 3D Digital Image Correlation technique, this work proposes a material flow analysis of an indentation process. The study establishes the methodology for the calibration and implementation of the 3D image sensing technology for deformation measurements. The purpose is to continue with the validation of DIC application on the indentation processes, where a deep penetration is achieved and extensive material flow is produced. With the 3D DIC technique is possible to perform accurate deformation measurements in non planar specimens and study the material emerging towards the exterior of the tested specimen, which is not possible with the 2D DIC technique. Although previous 2D studies were efficient detecting the flow field and von Mises strains on the specimens tested, the bulge emerging under the punch on the front surface (dead zone) could not be studied due to its predominantly 3D character. Therefore, present work implements a 3D methodology that carries out a complete deformation study, including the material flow that occurs on the Z axis, towards the exterior of the tested specimen, optimizing previous analyses.
Learning 3D joint constraints from vision-based motion capture datasets

Abstract Realistic estimation and synthesis of articulated human motion must satisfy anatomical constraints on joint angles. A data-driven approach is used to learn human joint limits from 3D motion capture datasets. We represent joint constraints with a new formulation ( s 1 , s 2 , τ ) using swing-twist representation in exponential maps form. Our parameterization is applied on Human3.6M dataset to create the lookup-map for each joint. These maps enable us to generate ‘synthetic’ datasets in entire joint rotation space of a given joint. A set of neural network discriminators is then trained with synthetic datasets to learn valid/invalid joint rotations. The discriminators achieve accuracy of [94.4−99.4 % ] for different joints. We validate precision-accuracy trade-off of discriminators and qualitatively evaluate classified poses with an interactive tool. The learned discriminators can be used as ‘priors’ for human pose estimation and motion synthesis.
Use of Information and Communication Technologies in the Classroom: An Exploratory Study in Professional Military Education

The study focuses on the use of information and communication technologies in professional military education, delimited to the present moment and to the current professors and students of the Portuguese Armed Forces Military University Institute. This study aims to analyse the relationship between ICT and teaching–learning methodologies in the context of professional military education, with emphasis on the concept of innovation in the classroom, on teaching–learning styles and on the role of ICTs in learning. The study is based on a mixed research strategy combining deductive and inductive approaches, materialized in a case study, with data collected through questionnaires, interviews and documentary analysis. This article focuses on the conceptual framework and its interconnection with the methodological strategy for the empirical study. The results presented refer to the pilot validation test of the data collection instruments and indicate that, in general, students and teachers share the same teaching–learning styles, both students and teachers show a good propensity to use ICT in the classroom and an almost complete substitution of traditional lessons, lectures and conferences by distance learning with a face-to-face approach in the discussion of case studies is possible.
An Efficient Algorithm for Deriving Frequent Itemsets from Lossless Condensed Representation

Mining frequent itemsets (abbr. FIs) from dense databases usually generates a large amount of itemsets, causing the mining algorithms to suffer from long execution time and high memory usage. Frequent closed itemset (abbr. FCI) is a lossless condensed representation of FI. Mining only the FCIs allows to reducing the execution time and memory usage. Moreover, with correct methods, the complete information of FIs can be derived from FCIs. Although many studies have presented various efficient approaches for mining FCIs, few of them have developed efficient algorithms for deriving FIs from FCIs. In view of this, we propose a novel algorithm called DFI-Growth for efficiently deriving FIs from FCIs. Moreover, we propose two strategies, named maximum support selection and maximum support replacement to guarantee that all the FIs and their supports can be correctly derived by DFI-Growth. To the best of our knowledge, the proposed DFI-Growth is the first kind of tree-based and pattern growth algorithm for deriving FIs from FCIs. Experiments show that DFI-Growth is superior to the most advanced deriving algorithm [12] in terms of both execution time and memory consumption.