Reviewerly/exHarmony huggingface dataset:
The Role and Specificity of Human Capital in the Formation of a Digital Economy

The article analyzes the process of transformation of human capital in the digital economy and reveals the current specifics of human capital, formed under the influence of digital technologies. Using systemic and synergetic research methods allowed us to consider the modern digital economy as a dynamic, self-developing system in which human capital is the primary source of innovative transformations. The authors discuss the mechanism of the dynamics of current social processes that influenced the formation of new qualitative content of human capital. They highlight the following global trends in the development of modern human capital: the transformation of human capital into an independent factor of production, the increasing role of intellectual capital in the digital economy, the emergence of new requirements that modern employers place on human capital. It is proved that, at present, human capital takes the form of intellectual capital associated with the heuristic nature of new knowledge. The authors conclude that the young generation is becoming the bearer of modern intellectual capital, in which unique values, a new type of motivation, new forms of communication, and new skills (including digital) are actively being formed.
<i>SHARI’AH</i> ORIENTED PRECIOUS METAL BACKED CRYPTOCURRENCY: FROM <i>SHARI’AH</i> ADVISORS’ AND FINANCIAL EXPERTS’ PERCEPTIONS

The suitability of assets-backed money has been the subject of considerable debate, although hampered in part by lack of theoretical and empirical evidence. Therefore, the motivation of this research is to investigate the perceptions of Shari’ah scholars and financial experts on the concepts and salient features of Shari’ah-compliant precious metal backed crypto-currency (PMC). To achieve this, this study adopted a qualitative method using semi-structured interview based on saturation technique. The results from Shari’ah advisors and financial experts indicated that the informants have differences of views on the assets-backed money, but they agreed that it ensures stability of money and adding the cryptocurrency technology is found to be desirable and recommendable. It is also found that PMC would be subjected to financial regulation challenges and using blockchain technology will increase the transparency. The informants agreed that PMC is closer to Maqāsid al-Shari’ah and there is some form of justice and equality compared to the current interest-based financial system. Therefore, the informants recommended the implementation of PMC.
Balanced Image Data Based Ensemble of Convolutional Neural Networks

Convolutional Neural Networks (CNN) have been very successful in classifying image datasets predominantly because they can automatically extract features and do not scale in complexity with large images due to the presence of multiple convolutional layers. Though being effective they are constrained with the availability of large amounts of data. CNNs normally require large amounts of input data in order to effectively train a network that can achieve reasonable classification performance. However, training of CNNs with large and unbalanced image data is inefficient in terms of time and accuracy. Therefore in this paper, we propose a novel method to create balanced image data and an ensemble of small CNNs. This will help improving learning and accuracy in deep learning applications. One of the key concepts proposed in this paper is to cluster input images into class pure data clusters. A class pure data cluster is a data cluster that has patterns from a same input class. Further all class pure data clusters are balanced by adding samples from other classes based on a distance measurement generating a pool of balanced data clusters. CNNs are trained on all balanced data clusters and fused using majority voting. We have evaluated the proposed method on well-known benchmark image datasets and the results showed that the proposed technique was able to achieve higher accuracy than the standard CNN.
Recommended practices for supplemental data

Since various forms of supplemental data (SD) have been introduced in academic publications, it has become necessary to establish guidelines to systematically process, indicate, and distribute such data. This material aims to help the science journals establish rational SD policies and guidelines and to ensure compliance with such policies and to manage them consistently. Generally, SD can be approached in a literal way by categorizing ‘appendices’ as ‘additional or separately added complementary materials’ and ‘supplements’ as ‘materials supplemental to the research in a comprehensive sense,’ rather than by viewing SD as an independent component of an article. The recommended practices of the National Information Standards Organization of USA advise the classification of SD into either ‘integral content’ or ‘additional content’ according to the content’s functional relationship to the associated article. If a public depository is used for SD, the author can ensure the perpetuity of data accessibility by assigning a digital object identifier. Science journals should adopt appropriate SD policies and describe them in detail in the instructions for authors to ensure consistent compliance with those policies. Additionally, they should be able to inspect and maintain links, repositories, and metadata associated with the SD for specific articles on an ongoing basis.
BASN—Learning Steganography with a Binary Attention Mechanism

Secret information sharing through image carriers has aroused much research attention in recent years with images’ growing domination on the Internet and mobile applications. The technique of embedding secret information in images without being detected is called image steganography. With the booming trend of convolutional neural networks (CNN), neural-network-automated tasks have been embedded more deeply in our daily lives. However, a series of wrong labeling or bad captioning on the embedded images has left a trace of skepticism and finally leads to a self-confession like exposure. To improve the security of image steganography and minimize task result distortion, models must maintain the feature maps generated by task-specific networks being irrelative to any hidden information embedded in the carrier. This paper introduces a binary attention mechanism into image steganography to help alleviate the security issue, and, in the meantime, increase embedding payload capacity. The experimental results show that our method has the advantage of high payload capacity with little feature map distortion and still resist detection by state-of-the-art image steganalysis algorithms.
Ranking Dutch intensifiers: a usage-based approach

ABSTRACT The present approach estimates the strength of intensifiers in Dutch by computing their information values in a language corpus, that is, contextual information content (Cohen Priva, 2008; Piantadosi, Tily, &amp; Gibson, 2011) and Shannon Information (Shannon &amp; Weaver, 1948), to respectively explain the use value and the expressive value of intensifiers when they intensify a predicative adjective. Conflicting strength values help in understanding the high number of intensifiers commonly available in particular languages and the constant need for adding new ones. Our approach underlines the relevance of two measures of information content (IC) for ranking intensifiers: (i) IC in context: the more combinatorial or transitional options an intensifier has, the higher its contextual information content and consequently its use value; and (ii) IC in relation to all alternative intensifiers: the higher the surprisal value that the occurrence of an intensifier evokes, the higher its expressive value. We shall investigate the validity of these two measures by researching a large corpus of Dutch tweets and shall test whether the values of these two measures can predict the stacking order in sequences of intensifiers.
Development of IoT Monitoring Device and Prediction of Daily Life Behavior

In this study, we developed an Internet of Things (IoT) monitoring device to monitor over the people inside a room. We collected sensor data at a specific location using the device. Based on the data, we tried to predict the behavior of the person at that location. Monitoring and predicting human daily behavior is trivial task. Most of the research on monitoring and predicting daily life behavior are based on the data available from smart home [7] [16] [17]. But smart home is expensive compare to normal home, as different kind of sensor are attached in the room and have more facilities. So, we developed a low cost IoT monitoring device and predict the daily life behavior of human from the sensor data taken from the device. We can extract information from the daily life behavior and share it with the family living in distant places.
Optimal Computing Budget Allocation for Binary Classification with Noisy Labels and its Applications on Simulation Analytics

In this study, we consider the budget allocation problem for binary classification with noisy labels. The classification accuracy can be improved by reducing the label noises which can be achieved by observing multiple independent observations of the labels. Hence, an efficient budget allocation strategy is needed to reduce the label noise and meanwhile guarantees a promising classification accuracy. Two problem settings are investigated in this work. One assumes that we do not know the underlying classification structures and labels can only be determined by comparing the sample average of its Bernoulli success probability with a given threshold. The other case assumes that data points with different labels can be separated by a hyperplane. For both cases, the closed-form optimal budget allocation strategies are developed. A simulation analytics example is used to demonstrate how the budget is allocated to different scenarios to further improve the learning of optimal decision functions.
L-CLAHE Intensification Filter (L-CIF) Algorithm for Underwater Image Enhancement and Colour Restoration

Contrast degradation due to wavelength-based light attenuation compromises quality of sub-aquatic images. In this paper, L-CLAHE Intensification Filter (L-CIF) algorithm is proposed to improve the quality of underwater images and to enhance underwater images in pre-processing stages. The proposed algorithm consists of three modules, named L-CLAHE, intensification, and filtering. The input image is converted to L*a*b colour space. CLAHE algorithm is used to process the extracted L* channel. The output of L-CLAHE module is processed with gamma correction, histogram equalization and bilateral filter in intensification and filter stages. The output image is contrast improved and colour corrected.
Evaluation and Extensions of an Automatic Speech Therapy Platform

Speech and language technology is currently being incorporated into e-health services addressed to the detection, assessment and rehabilitation of different types of speech and language disorders. This is the case of the Portuguese platform VITHEA, which provides remote access to automatic training sessions for aphasia rehabilitation. In VITHEA, patients’ answers to semantic confrontation exercises are validated using automatic speech recognition. Text-to-speech synthesis and a virtual agent are exploited to allow a natural multimodal interaction. While the effectiveness of the different technological components has been individually validated, there is a need for the assessment of the overall therapeutic platform. In this work, we report evaluation results of the complete therapeutic platform based on a survey collected from speech and language therapists. The results not only confirmed the significant usefulness of the platform for the treatment of aphasic patients, but also provided very relevant feedback that led to the introduction of additional features and to the development of similar solutions for other diseases .
Performance Prediction Method of Examinees Based on Matrix Completion

This paper presents a novel performance prediction method of examinees based on matrix completion. The proposed method newly introduces the matrix completion for predicting the performance of the entrance examination from the results of a mock exams. This approach using the matrix completion provides a solution to the problem that there is unknown information on the explanatory variable side. Specifically, we adopt singular value decomposition for solving the problem. Therefore, accurate prediction can be expected by the proposed method. This work is the first trial to realize predicting performance of examinees and this is the main contribution of this paper. Experimental results are shown for verifying the effectiveness of the proposed method.
Exemplar models are useful and deep neural networks overcome their limitations: A commentary on Ambridge (2020)

Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up each example, and such architectures have not been as successful as deep neural networks in capturing language usage and meaning. More work is needed bringing contemporary deep learning architectures used in machine intelligence to the effort to understand human language processing.
Variational Inference of Finite Generalized Gaussian Mixture Models

This paper presents a variational learning framework to analyze finite g eneralized G aussian m ixture models (GGMM). The model incorporates several mixtures that are widely used in signal and image processing applications. The motivation behind this work is the shape flexibility characteristics of the generalized Gaussian distribution (GGD) because of which it can be applied to different types of data. We present a method to evaluate the posterior distribution and Bayes estimators using the variational expectation-maximization algorithm. The effective number of components of the GGMM is determined automatically. The test results show the adequacy of the proposed algorithm by applying it to medical, astrological, and image segmentation applications; while comparing it with various other approaches.
Privacy-preserving Top-k Dominating Queries in Distributed Multi-party Databases

In most of the business areas, many organizations are running similar trades and maintaining comparable databases. These organizations have noticed the importance of analyzing results obtained from the union of databases owned by different organizations. However, they do not want to disclose their contents to others since some of the contents are sensitive and private. Recently, preference-based queries have drawn massive attention in the database community. Particularly, the top-k dominating queries have been studied extensively, which selects the k objects that are better than other objects based on the `domination score'. In this paper, we have considered the top-k dominating queries on the combined databases of different organizations. We propose a secure framework for multi-party top-k dominating queries, where individual organizations do not need to expose their private databases to others. We analyze the privacy of our proposed framework and also evaluate its performance for various settings.
An Intelligent Class: The Development Of A Novel Context Capturing Framework Supporting The Functional Auto-Classification Of Records

The need to accurately classify records is a core problem in many domains. Current methods for auto-classification focus on a record's content and not its context. As a result, current auto-classification methods are unable to achieve the levels of precision, accuracy, and recall that match or exceed the levels generated by human classifiers. In order to address this challenge, a new methodology is needed that specifies how to extract contextual features from a record in order to improve the auto-classification accuracy, precision, and recall of records at scale. This paper closes this gap, using the diplomatic definition of context to specify a mapping that will operationalize the capturing of context from a record. This mapping, makes it possible to continue developing a formal method for functional auto-classification and contextual feature extraction that will utilize a record's context to improve functional auto-classification accuracy, precision, and recall.
Preprocessing for Identification of Degraded Urdu and Devanagari Printed Script

An optical character recognition (OCR) comprises of the phases: preprocessing, segmentation, feature extraction, and classification. The objective of this paper is to improve the quality of the input image by preprocessing it. This preprocessing of degraded Urdu and Devanagari documents focuses on binarization, skew detection, and noise removal. Binarization is done using modified wolfs algorithm. Hough transform is used for skew detection and shearing method is used for skew correction. Finally, the image is enhanced by removing the noise using Gaussian filter and median filter.
One Book, Two Language Varieties

This paper presents a comparative study of alignment pairs, either contrasting expressions or stylistic variants of the same expression in the European (EP) and the Brazilian (BP) varieties of Portuguese. The alignments were collected semi-automatically using the CLUE-Aligner tool, which allows to record all pairs of paraphrastic units resulting from the alignment task in a database. The corpus used was a children’s literature book Os Livros Que Devoraram o Meu Pai (The Books that Devoured My Father) by the Portuguese author Afonso Cruz and the Brazilian adaptation of this book. The main goal of the work presented here is to gather equivalent phrasal expressions and different syntactic constructions, which convey the same meaning in EP and BP, and contribute to the optimisation of editorial processes compulsory in the adaptation of texts, but which are suitable for any type of editorial process. This study provides a scientific basis for future work in the area of editing, proofreading and converting text to and from any variety of Portuguese from a computational point of view, namely to be used in a paraphrasing system with a variety adaptation functionality, even in the case of a literary text. We contemplate “challenging” cases, from a literary point of view, looking for alternatives that do not tamper with the imagery richness of the original version .
Optical Assistance for Motor Neuron Disease (MND) Patients Using Real-time Eye Tracking

Face detection and eyes extraction has an important role in many applications such as face recognition, facial expression analysis, security login etc. The system is designed to develop an eye-motion based communication system for patients suffering from motor neuron disease (MND) to contact with care takers whenever they want. In this paper, we proposed a method to efficiently track eye gaze detection in real time from video acquired by a web camera. The camera is stationary with respect to the head. Proposed system is mainly divided into three steps; face detection, eye-ball(pupil) detection and finally eye gaze detection. Using predefined facial landmarks face is detected in real time. Then in the second step face point that were extracted in phase one helps to detect eye point and using circular Hough Transform eye pupil was accurately detected. Finally, in the last stage eye gaze was calculated using gaze ratio formula. The experiments are conducted on people from different age groups and a high accuracy rate is achieved.
A Modified Blind Deconvolution Algorithm for Deblurring of Colored Images

In digital photography, the blur which is caused by motion or camera shake is most common artifact. Due to lack of light or less shutter speed, the image becomes blurry and causes significant degradation in image. The information in the image is affected by this effect. In digital imaging recovering, the unblurred color image from these images is fundamental research problem. There are various types of blur like motion blur, Gaussian blur, etc. Various techniques have been introduced to deblur the images, but specific method is used to deblur specific type of blurring. In this paper, neural networks are used to deblur the images to obtain a sharp one and modified approach in algorithm is introduced in which the image is converted and Sobel color (red, green and blue) filters are applied on the image for better results. The blur kernel is deconvolved out of the blurred image to obtain a sharp image. This approach is better than the baseline approach of using Richardson–Lucy algorithm.
RETRACTED: CRC algorithm for embedded system based on table lookup method

One byte of CRC (Cyclic Redundancy Checks) code is calculated in the general table lookup method each time, and the processing efficiency is not high. Therefore, an improved algorithm based on the general table lookup method is proposed. The improved algorithm processes 2 bytes at a time, that is, calculates a CRC of 2 bytes, and needs to keep the CRC of these 2 bytes separately. After the two-byte information of the next group is input, the CRC of the next two bytes is calculated by using the CRC of the first two bytes. By analogy, the 2-byte CRC code of each group is calculated based on the 2-byte CRC code of the previous group until all information is input. After experimental verification, the results show that the running time of the algorithm is relatively reduced. Aiming at the application of embedded system, based on the analysis of the principle of traditional cyclic redundancy check algorithm, a cyclic redundancy check algorithm based on the look-up table method is proposed to improve the data calibration while ensuring the reliability of the embedded system data. Check the processing speed.
Collaborator Recommender System

Nowadays, a lot of scientists’ works aim to improve the quality of people’s life but it could be quite complicated without building a successful collaboration. Productive partnerships can increase research efficiency in many cases and make a huge impact on society. However, today there is no clear way to find such collaborators. In this paper, we propose a recommender system for the scientists from the Higher School of Economics university to help them find co-authors for their prospective studies.
A Mobile Robot Path Planning Scheme for Dynamic Environments

Abstract This paper proposes a four-direction search method for obstacle avoidance of mobile robots, and the collision energy of obstacles is modeled based on the neural network. For comparison and discussion purposes, the different invariant step lengths are tested in the static environment. To take the advantages of the large and small step lengths effectively, a variable step length method is adopted to improve the performance, such as less iteration number and lower total energy. The variable step length method is also applied to the dynamic environment to explore the real-time performance for path planning. Simulation results demonstrate the effectiveness and practicability of the presented scheme.