Reviewerly/exHarmony huggingface dataset:
BARRIERS IN PROFESSIONAL DEVELOPMENT OF WOMEN

Purpose: The current situation shows that women, in comparison with men, are a more vulnerable social group. Gender stereotypes lead to an underestimation of the abilities of women and their labor input.&#x0D; Methodology: The paper analyzes two groups of gender stereotypes, which in fact represent barriers that impede the professional development of women in the Russian labor market. The authors established a level of agreement with the influence of barriers on female professional activity in two groups of respondents - women with or without professional experience. &#x0D; Result: The authors identified and compared the leading barriers to female professional activities. In general, in the structure of the female professional mentality, there are certain personal stereotypes that, together with socially determined gender stereotypes, have a complex impact on unlocking the female employment potential.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of barriers in the professional development of women is presented in a comprehensive and complete manner.
Deep Siamese Networks with Bayesian Non-parametrics for Video Object Tracking

We present a novel algorithm utilizing a deep Siamese neural network as a general object similarity function in combination with a Bayesian optimization (BO) framework to encode spatio-temporal information for efficient object tracking in video. In particular, we treat the video tracking problem as a dynamic (i.e. temporally-evolving) optimization problem. Using Gaussian Process priors, we model a dynamic objective function representing the location of a tracked object in each frame. By exploiting temporal correlations, the proposed method queries the search space in a statistically principled and efficient way, offering several benefits over current state of the art video tracking methods.
Collaborative governance against corruption

The purpose of this paper is to study the benefits of the Blockchain, if properly applied in the near future in Lebanon. It sheds light on why collaborative governance against corruption is needed. The research is built on an analysis of reports and on face to face interviews with key managers in all 14 Lebanese alpha banks. Due to the sovereign debt composition, we found out that the Blockchain technology can represent a vital tool to disrupt corruption, but the decision of implementing it, is up to the Central Bank governor. The research offers an added value as it is the first to describe the positive effects of implementing the Blockchain technology in a country that is ranked 180 out of 209 on WB’s control of corruption index
Evaluating PIT-OSN 1 in inspecting the privacy levels of an online social network

In the last few years, Online Social Networks (OSNs) have experienced a growth in their number of users, becoming an increasingly embedded part of people's daily lives. Privacy expectations in OSNs are getting smaller as more members begin to face potential privacy problems when interacting with these systems. Inspections methods can be an effective alternative for addressing privacy problems because they allow detecting a possible defect that could be causing the system to behave in an undesirable way. Based on that, a set of privacy inspection techniques called PIT-OSN (Privacy Inspection Techniques for Online Social Network) was proposed. This paper focuses on one of these techniques (the PIT-OSN 1), which supports the inspection of the privacy levels in an OSN. The goal of this paper is to present the evaluation of PIT-OSN 1, through an empirical study, which collected quantitative and qualitative data. Results obtained from the analysis indicate that the technique assists nonexpert inspectors detecting privacy problems effectively and that it was considered easy to use and useful by the participants of the study. Finally, the qualitative analysis points out relevant improvement opportunities in PIT-OSN 1.
Tren Kepengarangan Berkala Ilmu Perpustakaan dan Informasi Periode 2014-2018: Sebuah Analisis Bibliometrik

This article discusses Bibliometrics which was used to analyse 80 articles published in journal Berkala Ilmu Perpustakaan dan Informasi (BIP) from 2014 to 2018 published by the Library of Universitas Gadjah Mada. This study aims to determine the distribution of articles per year as well as per number, authorship patterns, type of references used, institutional contribution and author productivity. The data was downloaded from the https://jurnal.ugm.ac.id/bip , and was processed using Microsof Excel 2013, as well as the degree of collaboration calculated in the Subramanyam formula. The results showed that BIP has published a total of 80 articles involving 120 people authors, with a single author larger than the multiple author; collaboration degrees value at 0.67. The majority of the references used were textbooks (65.15%) and scientific journals (31.04%). Affiliate authors come from educational institutions is the biggest contributor article, and Gadjah Mada University is in the first order (36 times). Female authors are more productive than male authors: Pergola Irianti served as the first one (4 articles). Some suggestions include that a journal manager should increase the number of collaborative authors and to require that published articles should have 80% of the journals and the proceedings as the mai n reference.
Entity Embedding Analogy for Implicit Link Discovery

In this work we are interested in the problem of knowledge graph (KG) incompleteness, which we propose to solve by discovering implicit triples using observed ones in the incomplete graph leveraging analogy structures deducted from a KG embedding model. We use a language modelling approach that we adapt to entities and relations. The first results show that analogical inferences in the projected vector space is relevant to a link prediction task.
Towards a Logical Formalisation of Theory of Mind: A Study on False Belief Tasks

Theory of Mind, the cognitive capacity to attribute internal mental states to oneself and others, is a crucial component of social skills. Its formal study has become important, witness recent research on reasoning and information update by intelligent agents, and some proposals for its formal modelling have put forward settings based on Epistemic Logic (EL). Still, due to intrinsic idealisations, it is questionable whether EL can be used to model the high-order cognition of ‘real’ agents. This manuscript proposes a mental attribution modelling logical framework that is more in-line with findings in cognitive science. We introduce the setting and some of its technical features, and argue why it does justice to empirical observations, using it for modelling well-known False-Belief Tasks.
Services of Harvard Library and the Enlightenments for Other Academic Libraries

As the largest university library in the world, Harvard Library is an important indicator of Harvard University as a world-class university.Harvard Library possesses extremely rich resources, advanced facilities and distinctive features, supplying first-class services.Harvard Library not only serves teaching, learning and scientific research, but also provides distinctive and refined services, humanized services for disadvantaged groups, public services for the society, as well as many international exchanges with academic libraries in other countries.The service systems of Harvard Library provide constructive references for other academic universities especially those in China.Academic libraries could actively learn from Harvard Library and substantially improve their construction and service levels, providing high-quality services for their students, teachers, researchers, and even the public.
Multimedia information retrieval in big data using OpenCV python

The popularization of systems, applications and devices to produce, view and share multimedia, saw the need to treat a large volume of data arise. In related areas (such as Multimedia Big Data, Data Science and Multimedia Information Retrieval) a key step is commonly referred as Multimedia Indexing or Multimedia Big Data Analysis, where the aim is to represent multimedia content into smaller, more manageable units, allowing the extraction of data features and information essential to the proper performance of the associated services. This mini-course discusses current tools and techniques for indexing, extracting and processing of multimodal multimedia content. The techniques are exemplified in Python OpenCV over different content (like images, audio, text and video), leading to the interest of services like Netflix, Google and YouTube on this subject, attracting the interest of researchers and developers.
NotaryPedia: A Knowledge Graph of Historical Notarial Manuscripts

The Notarial Archives in Valletta, the capital city of Malta, houses a rich and valuable collection of around twenty thousand notarial manuscripts dating back to the 15th century. The Archive wants to make the contents of this collection easily accessible and searchable to researchers and the general public. Knowledge Graphs have been successfully used to represent similar historical content. Nevertheless, building a Knowledge Graph for the archives is challenging as these documents are written in medieval Latin and currently there is a lack of information extraction tools that recognise this language. This is, furthermore, compounded with a lack of medieval Latin corpora to train and evaluate machine learning algorithms, as well as a lack of an ontological representation for the contents of notarial manuscripts. In this paper, we present NotaryPedia, a Knowledge Graph for the Notarial Archives. We extend our previous work on entity and keyphrase extraction with relation extraction to populate the Knowledge Graph using an ontological vocabulary for notarial deeds. Furthermore, we perform Knowledge Graph completeness using link-prediction and inference. Our work was evaluated using different translational distance and semantic matching models to predict relations amongst literals by promoting them to entities and to infer new knowledge from existing entities. A 49% relation prediction accuracy using TransE was achieved.
A New Approach to Improve Learning in Fuzzy Cognitive Maps Using Reinforcement Learning

Fuzzy Cognitive Maps (FCM) are dedicated to modeling complex dynamic systems and has been widely studied. One of those studies probed that Computing with Words (CWW) is very effective to improve the interpretability and transparency of FCM. Learning methods to calculate the weight matrix in a map are the target of hundreds of studies in various parts of the world. These methods allow the map to learn or evolve towards a better state, but always taking into account that the output must be evaluated and compared using a real scenario. Reinforcement Learning has, within its performance, one of the possible answers to this problem, aimed at improving the classification capacity of the map and thus improving the learning of its weights. This paper presents a new learning method for Fuzzy Cognitive Maps. The proposal was evaluated using international databases and the experimental results show a satisfactory performance.
Splitting a PRE-scheme on Private Blockchain

Splitting a PRE-scheme on the Private Block Chain (is a crucial decentralized management service and cryptographic access control mechanism for the decentralized applications. By transporting private data distribution and design to the public block chain, proposed scheme enables everything from encrypted content marketplaces to secret credentials management to the file sharing system. In this paper, we propose splitting of Proxy re-encryption (PRE) scheme on the private block chain for flexibility, efficiency, extensibility, and privacy. We also experiment with the new use of proxy re-encrypting (PRE) scheme for the delegation of permission from one block to the other block. As per our knowledge, we have not seen any work which uses layered block chain technology with PRE-scheme.
Using the Principle of Problematicity for the Development Socialization of Schoolchildren

The article discusses the features of the implementation of problem-based learning, as the most eff ective for the development of schoolchildren. The features of creating problem situations, tasks for them are noted. It indicates the diff erences between traditional and problem-based learning.
Dual Stream Spatio-Temporal Motion Fusion With Self-Attention For Action Recognition

Human action recognition in diverse and realistic environments is a challenging task. Automatic classification of action and gestures has a significant impact on human-robot interaction and human-machine interaction technologies. Due to the prevalence of complex real-world problems, it is non-trivial to produce a rich representation of actions and to produce an effective categorical distribution of large action classes. Deep convolutional neural networks have obtained great success in this area. Many researchers have proposed deep neural architectures for action recognition while considering the spatial and temporal aspects of the action. This research proposes a dual stream spatiotemporal fusion architecture for human action classification. The spatial and temporal data is fused using an attention mechanism. We investigate two fusion techniques and show that the proposed architecture achieves accurate results with much fewer parameters as compared to the traditional deep neural networks. We achieved 99.1 &#x0025; absolute accuracy on the UCF-101 test set.
XE in Australia and New Zealand, Latin America, the Middle East, Africa, and the world: c.1991–2017

The nations included in this chapter are Australia, Mexico and nine other nations of Latin America, Pakistan, Lebanon, Egypt, Turkey, the continent of Africa, the Gulf Cooperation Countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates), and 17 of the 35 Middle East and North Africa (MENA) nations. Many of these nations and the African continent have undergone market liberalization and, faced the effects of globalization, including the “urge to merge.” The studies in this chapter reflect this.
A Real-time Hand Gesture Recognition System using 24 GHz Radar Array

This paper presents a description of a real-time hand gesture recognition system. This system consists of three commercial modules perpendicular mounted in an three-dimensional array to provide six-channel baseband I/Q signals. The I/Q signals are pre-processed by the doppler signal amplitude threshold detection and spectral analysis. A convolutional neural network consisting in two convolutional layers and two fully connected layers is constructed as the recognition classifier with less dependence of feature extraction. The network is trained with 1000 groups of datasets and verified by testing recognized results as the customized shortcut keys. Results show that this system could achieve a high recognition accuracy rate higher than 95% in the real-time test.
Contourlet Transform based Multi-scale Fusion Network for Face Hallucination

Abstract Recently, Convolutional neural network (CNN) has achieved great success in the field of face hallucination. However, such approaches usually generate blurry and over-smoothed Super Resolution (SR) results, and the performance suffers from degradation when super-resolve a very Low Resolution (LR) face image. To solve these problems, this paper proposes an contourlet transform based accurate CNN architecture, namely Multi-scale Fusion CNN (MSFC), which are able to reconstruct a High Resolution (HR) face image from a very low resolution input. First of all, we present multi-scale fusion CNN (MSFC) to fully detect and exploit features from LR inputs. And then, we formulate the SR problem as the prediction of contourlet transform coefficients, which is able to make MSFC further capture the texture details for super-resolve face images. Extensive qualitative and quantitative experiments show that the proposed method is capable of preserving details and achieves superior SR performance compared to the state-of-the-art methods.
Systematizing IT Risks

IT risks-risks associated with the operation or use of information technology-have taken on great importance in business, and IT risk management is accordingly important in the science and practice of information management.Therefore, it is necessary to systematize IT risks in order to plan, manage and control for different risk-specific measures.In order to choose and implement suitable measures for managing IT risks, effect-based and causebased procedures are necessary.These procedures are explained in detail for IT security risks because of their special importance.
Doctorate in Innovation in Curriculum and Instruction Application Requirements

Information about the application requirements for the Doctorate in Innovation in Curriculum and Instruction.
Piece-wise max-margin-based discriminative feature learning

Max-margin-based discriminative feature learning method (MMLDF) learns a low-dimensional feature representation such that the global margin of the data is maximised and samples of the same class become as close as possible. Non-linear version of MMLDF uses kernel method. In this paper, we first prove that the kernel MMLDF cannot be used for discriminative feature learning. Strictly speaking, the kernel MMLDF maps input vectors into zero vectors which cannot then be classified. Therefore, to overcome this problem, we propose a novel non-linear MMLDF which uses piece-wise learning technique. Then, by using alternating optimisation, an algorithm is proposed to solve our proposed model. Experimental results on real datasets confirm that our proposed piece-wise MMLDF outperforms linear MMLDF and piece-wise SVM.
Image Segmentation Based on Cumulative Residual Entropy

Cumulative residual entropy (CRE) is an essential concept in information theory and have more general mathematical properties in contrast to entropy. However, it is observed that research on CRE has relatively little consideration in image processing. Image thresholding technique plays a crucial role in several of the tasks needed for pattern recognition and computer vision. In this paper, we study, implement, and apply the CRE measure for image thresholding. Firstly, we have defined a thresholding criterion, which is based on the CRE measure that related and based on the image. Secondly, the optimal solution of CRE function found. Finally, the proposed method is applied over data set of image such as nondestructive testing (NDT) images. Moreover, we compare this with several classic segmentation techniques on the same data set.
Design and implementing securely hiding system using Neighborhoods of the pixels

Embedding techniques play an important role in concealing secret messages inside images; in this research a hide security of confidential message(text or image) system in the digital image has been proposed using a new way to hide data depending on the neighborhoods of pixels.Alsothe access has been restricted by a password requested from the user to add more security to the proposed system,the extraction algorithm that requires another password known only to thereliable recipient that was also concealed within the image.Hiding the secret message inside the cover image is using the eight neighborhoods of the pixels, and the system will determine this pixel which will be concealment in its neighborhoods is accordance with an agreed formula between the sender and the recipient.Data embedding has been in a different manner from pixel to another depending on their location in the image, If it is in the individual sites, the embedding will be in anticlockwise else it will be clockwise as well as the use of passwords given higher security and resistance against extraction by the attackers, thus proved the ability of the proposed algorithm to embed and extract the confidential messages without errors.
Performance Analysis of Illumination Invariant Change Detection Method for Detecting Image Change in Night Vision Camera

At present, the use of video cameras is not only limited to documenting events but is also used for surveillance systems. Changes in lighting that occur in the surveillance area is one of the problems that result in a false alarm on the surveillance system. Illumination Invariant Change Detection is a method for detecting image changes on images. This study aims to determine the performance of the Illumination Invariant Change Detection method to detect image changes in night vision surveillance cameras. The Illumination Invariant Change Detection method does not work well for detecting image changes on a night vision camera under dark lighting conditions at an average value of Lux 0 with an infrared lamp on. The accuracy of the application of the method to detect image changes on night vision cameras is 80% with the selection of the threshold value of the detection of image changes that is 75000 pixels.
Know the Tools

Building Flutter apps cannot succeed without the help of various tools. During the development, we may need to use tools from Dart SDK, Flutter SDK, and IDEs. Making good use of these tools can increase your productivity. This chapter covers usage of tools from Dart SDK, Flutter SDK, Android Studio, and VS Code.
A Statistical Random Variable Approach to Fuzzy Cognitive Map Modeling

Fuzzy cognitive maps are studied from the quantitative human-scientific standpoint. The concepts and weights of these maps are thus examined as statistical random variables based on probability distributions in order to make more feasible interpretations on these entities. First, the available fuzzy cognitive maps are considered. Second, our statistical approach is introduced which assumes that the concepts and weights are uniformly or normally distributed random variables. Third, the dynamics and application possibilities of these fuzzy cognitive maps are studied.
Unboxing timbre in the Behringer model D synthesiser

On 29 June 2017, the artist Stephen Fitzgerald (or “Noir Et Blanc Vie”) posted a video on Youtube titled “Thoughts on Behringer Model D.”1 During the five-minute video, the artist delivers an evenl...
Quantitative Trading Machine Learning Using Differential Evolution Algorithm

Technical analysis, which is commonly used world-wide, employs trading price data to calculate technical indicators. These indicators assist investors in making decision on buy/sell timing for the highest profit. However, there are no such golden rules to guarantee the highest profit from using technical indicators despite hundreds of research in this field. In this research, a quantitative trading machine learning (QTML) was proposed to suggest buying or selling time for a stock. QTML employs Differential Evolution algorithm to analyze trading signals from a set of widely-used technical indicators. Daily stock trading information from different industries during 2014 - 2019 from Stock Exchange of Thailand (SET) are used for evaluating its performance compared with the buy-and-hold strategy. The experiment is with 0.2% commission fee for all buying and selling transactions. The result shows that the proposed QTML model can assist investors in making a higher profit, particularly in stocks with fluctuated or downward trends.