Reviewerly/exHarmony huggingface dataset:
Study of qualitative properties of groundwater and its suitability for different uses in the Eastern of the Al- Dour city/ Salahaldin/ Iraq

A study was conducted to evaluate the groundwater in Abo- Dalaf Area, far east of Al-Dour city, Salahaldin governorate. The field work included, field visits to the area to know the geological reality and to identify the most important geomorphological and structural phenomena, then 10 wells were selected, distributed over the study area.&#x0D; Laboratory work included physical and chemical tests of water samples, The physical characteristics included, Total hardness (TH), Total Dissolved Solid (TDS) Sodium Adsorption Ratio (SAR), Sodium Ratio (Na%), Temperature (T) and pH.&#x0D; Chemical analyses, the main components of water samples, for Cations such as: Sodium (Na+), Potassium (K+), Calcium (Ca+2), and Magnesium (Mg+2), and Anions such as Chloride (Cl-), Sulfate (SO4-2), Bicarbonates (HCO3-), and nitrate (NO3-), and some trace elements were measured such as Iron (Fe), Copper (Cu), Zinc (Zn), lead (Pb), Nickel (Ni), Cobalt (Co), Chromium (Cr) and Cadmium (Cd). A location map of the study area was prepared using ArcGIS.The results showed a variation in the quality characteristics of groundwater in the study area. After comparing the results with local international standards, it was showed that the wells water of the study area was not suitable for human drinking, but suitable for irrigation except well (1).&#x0D; &#x0D; http://dx.doi.org/10.25130/tjps.25.2020.029
Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network

Since the underwater image is not clear and difficult to recognize, it is necessary to obtain a clear image with the super-resolution (SR) method to further study underwater images. The obtained images with conventional underwater image super-resolution methods lack detailed information, which results in errors in subsequent recognition and other processes. Therefore, we propose an image sequence generative adversarial network (ISGAN) method for super-resolution based on underwater image sequences collected by multifocus from the same angle, which can obtain more details and improve the resolution of the image. At the same time, a dual generator method is used in order to optimize the network architecture and improve the stability of the generator. The preprocessed images are, respectively, passed through the dual generator, one of which is used as the main generator to generate the SR image of sequence images, and the other is used as the auxiliary generator to prevent the training from crashing or generating redundant details. Experimental results show that the proposed method can be improved on both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to the traditional GAN method in underwater image SR.
Image Signal Processor Parameter Tuning with Surrogate-Assisted Particle Swarm Optimization

Evolutionary algorithms (EA) are developed and compared based on well defined benchmark problems, but their application to real-world problems is still challenging. In image processing, EA have been used to tune a particular image filter or in the design of filters themselves. But nowadays in digital cameras, the image sensor captures a raw image that is then processed by an Image Signal Processor (ISP) where several transformations or filters are sequentially applied in order to enhance the final picture. Each of these steps have several parameters and their tuning require lot of resources that are usually performed by human experts based on metrics to assess the quality of the final image. This can be considered as an expensive black-box optimization problem with many parameters and many quality metrics. In this paper, we investigate the use of EA in the context of ISP parameter tuning with the aim of raw image enhancement.
A visual tool to explore the composition of international migration flows in the EU countries, 1998–2015

Background: Ternary plots can effectively display compositional data and help to understand its multidimensional patterns. However, they are currently greatly underused in demography.
High Accuracy Parameter Estimation for Advanced Radar Identification of Electronic Intelligence System

Radar identification is one of the vital operations in an electronic intelligence system. The conventional methods based on basic parameters comparison of unique identification of a radar in a cluster of similar radars, is prone to ambiguities. To meet the current tactical requirements of unique identification of a radar, the methodology needs to be based on better feature extraction, even in low SNR conditions. The paper explores a novel technique based on moving autocorrelation for the extraction of intra-pulse and inter-pulse radar parameters. Extensive simulation and empirical studies have been carried out to establish the approach to extend accurate radar parameters in noisy and low SNR conditions. The technique is found to be promising even in field data conditions. The paper describes the methodology, simulation results, FPGA implementation using system generator and resource utilisation summary.
A modified bordered construction for self-dual codes from group rings

We describe a bordered construction for self-dual codes coming from group rings. We apply the constructions coming from the cyclic and dihedral groups over several alphabets to obtain extremal binary self-dual codes of various lengths. In particular we find a new extremal binary self-dual code of length 78.
Text Mining: Design of Interactive Search Engine Based Regular Expressions of Online Automobile Advertisements

Technology world has greatly evolved over the past decades, which led to inflated data volume. This progress of technology in the digital form generated scattered texts across millions of web pages. Unstructured texts contain a vast amount of textual data. Discover of useful and interesting relations from unstructured texts requires more processing by computers. Therefore, text mining and information extraction have become an exciting research field to get structured and valuable information. This paper focuses on text pre-processing of automotive advertisements domains to configure a structured database. The structured database was created by extract the information over unstructured automotive advertisements, which is an area of natural language processing. Information extraction deals with finding factual information in text using learning regular expressions. We manually craft rule-based specific approaches to extract structured information from unstructured web pages. Structured information will be provided by user-friendly search engine designed for topic-specific knowledge. Consequently, this information that extracted from these advertisements uses to perform a structured search over certain interesting attributes. Thus, the tuples are assigned a probability and indexed to support the efficiency of extraction and exploration via user queries.
Decomposing Probabilistic Lambda-Calculi

Abstract A notion of probabilistic lambda-calculus usually comes with a prescribed reduction strategy, typically call-by-name or call-by-value, as the calculus is non-confluent and these strategies yield different results. This is a break with one of the main advantages of lambda-calculus: confluence, which means results are independent from the choice of strategy. We present a probabilistic lambda-calculus where the probabilistic operator is decomposed into two syntactic constructs: a generator, which represents a probabilistic event; and a consumer, which acts on the term depending on a given event. The resulting calculus, the Probabilistic Event Lambda-Calculus, is confluent, and interprets the call-by-name and call-by-value strategies through different interpretations of the probabilistic operator into our generator and consumer constructs. We present two notions of reduction, one via fine-grained local rewrite steps, and one by generation and consumption of probabilistic events. Simple types for the calculus are essentially standard, and they convey strong normalization. We demonstrate how we can encode call-by-name and call-by-value probabilistic evaluation.
Cybersecurity Standardisation for SMEs

There are various challenges regarding the development and use of cybersecurity standards for SMEs. In particular, SMEs need guidance in interpreting and implementing cybersecurity practices and adopting the standards to their specific needs. As an empirical study, the workshop Cybersecurity Standards: What Impacts and Gaps for SMEs was co-organized by the StandICT.eu and SMESEC Horizon 2020 projects with the aim of identifying cybersecurity standardisation needs and gaps for SMEs. The workshop participants were from key stakeholder groups that include policymakers, standards developing organisations, SME alliances, and cybersecurity organisations. This paper highlights the key discussions and outcomes of the workshop and presents the themes, current initiatives, and plans towards cybersecurity standardisation for SMEs. The findings from the workshop and multivocal literature searches were used to formulate an agenda for future research.
CmnRec: Sequential Recommendations With Chunk-Accelerated Memory Network

Recently, Memory-based Neural Recommenders (MNR) have demonstrated superior predictive accuracy in the task of sequential recommendations, particularly for modeling long-term item dependencies. However, typical MNR requires complex memory access operations, i.e., both writing and reading via a controller (e.g., RNN) at every time step. Those frequent operations will dramatically increase the network training time, resulting in the difficulty in being deployed on industrial-scale recommender systems. In this paper, we present a novel general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chunk</i> framework to accelerate MNR significantly. Specifically, our framework divides proximal information units into chunks, and performs memory access at certain time steps, whereby the number of memory operations can be greatly reduced. We investigate two ways to implement effective chunking, i.e., PEriodic Chunk (PEC) and Time-Sensitive Chunk (TSC), to preserve and recover important recurrent signals in the sequence. Since chunk-accelerated MNR models take into account more proximal information units than that from a single timestep, it can alleviate the influence of noise in the user-item interaction sequence to a large extent, and thus improve the stability of MNR. In this way, the proposed chunk mechanism can lead to not only faster training and prediction, but even slightly better results. The experimental results on three real-world datasets (weishi, ml-10M and ml-latest) show that our chunk framework notably reduces the running time (e.g., with up to 7x for training & 10x for inference on ml-latest) of MNR, and meantime achieves competitive performance.
Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent

In this paper, we study the linear regression problem of training an one-hidden-layer non-overlapping convolutional neural networks (ConvNNs) with the rectified linear unit (ReLU) activation functions. Given a set of training data that contains the inputs (feature vectors) and outputs (labels), the outputs are assumed to be generated from a ConvNN with unknown weights, and our goal is to recover the ground-truth weights by minimizing a non-convex optimization problem whose object function is the empirical loss function. We have proved that if the inputs belong to Gaussian distribution, then the optimization problem can be solved by accelerated gradient descent (AGD) algorithm with a well-designed initial point and enough samples, and the iterates via AGD algorithm converge linearly to the ground-truth weights.
steganography in colored images based on biometrics

Due to the great significance of the concealment and transfer of confidential data and the many and varied algorithms presented in this field, as well as the big and rapid development of the use of biometrics in the issues of reliability and verification of the identity of people in support of information security. Therefore this research has provided a suggested algorithm to hide confidential data in colorful images based on the features of engineering dimensions of the human hand as one of the types of biometrics; we extracted a set of these characteristics and processed them to build a matrix that specifies the map of the distribution of confidential data in the cover image. The proposed method was applied to a set of images to hide a set of confidential messages and the visual quality of the cover image was not affected after the concealment. The practical results explain the efficiency of this method was measured in terms of comparison between the original image before and after concealment. The performance of the algorithm and the method of its application were measured using measures of efficiency of the work, whereas the Peak Signal Noise Ratio was used, the highest ratio obtained was 81.3417, and Mean Square Error was approximately 0.0008 as a maximum limit, while the error rate was the restored confidential message after hiding Bit Error Rate = 0 in all cases.&#x0D; &#x0D; http://dx.doi.org/10.25130/tjps.24.2019.056
Support vector regression (SVR) model for forecasting number of passengers on domestic flights at Sultan Hasanudin airport Makassar

Sultan Hasanudin Airport is one of the largest airports in Indonesia, located in Makassar City. Its strategic location is the entrance of eastern Indonesia because it is a transit airport to other eastern regions of Indonesia. The number of airplane passengers at Sultan Hasanudin Airport has increased and decreased each time depending on certain moments. The increase in the number of passengers is closely related to the moments of religious holidays or year-end holidays. Whereas the decrease in the number of passengers was greatly influenced by the policy of rising plane ticket prices some time ago. Estimated number of passengers every month is needed in planning and making appropriate decisions from the government relating to fluctuations in the number of domestic flight passengers at Sultan Hasanudin Airport. Therefore, accurate forecasting techniques are needed to predict the number of passengers in the future. Because the data pattern of domestic flight passengers at Sultan Hasanudin Airport is not stationary, the ARIMA model can be used. However, the data on the number of passengers has a nonlinear data pattern, so we need a method that can overcome these problems. In this study the SVR model is used to overcome nonlinear patterns in the data. Compared to the ARIMA model, SVR has the advantage because it does not require stationary data assumptions as in ARIMA. The results of forecasting data on the number of domestic flight passengers at Sultan Hasanudin Airport using SVR show better accuracy or accuracy compared to the ARIMA model because it has a smaller MAPE value.
TouchYou: A wearable touch sensor and stimulator for using our own body as a remote sex interface

Abstract In this article, we present TouchYou, a pair of wearable interfaces that enable affective touch interactions with people at long-distance. Through a touch-sensitive interface, which works by touch, pressure and capacitance, the body becomes the own input for stimulating the other body, which has a stimulation interface that enables the feeling of being touched. The person receives an electrical muscle stimulation, thermal and mechanical stimulation that react depending on the touch sensed by the first interface. By using the TouchYou, people can stimulate each other, using their own body, not only for sexual relations at a distance but for the production of affection and another way of feeling. We discuss the importance of the touch for human relationships, the current state of the art in haptic interfaces and how the technology can be used for the affection remote transmission. We present the design process of the TouchYou sensitive and stimulation interfaces, with a contribution of a method for developing custom touch sensors, we explore usage scenarios for the technology, including sex toys and sex robots and we present the concept of using the body as a remote sex interface.
Electric Dreams of Ukiyo: A Series of Japanese Artworks Created by an Artificial Intelligence

This opinion piece provides insight on the creation of artistic visuals using artificial intelligence. It describes how 3 artists evolving in this field decided to use technology in a derived way, with the help of researchers and enthusiasts working open source, to talk about how our society is developing with regards to technology adoption.
English Scholarly Publishing Activities in the Industrial Revolution 4.0: What, Why, and How?

The presence of the Industrial Revolution (IR) 4.0 has inevitably affected the way how scholars proceed to write. Thus, activities intended to yield a piece of scientific writing facilitated by the obtainability and accessibility of internet browsing is called scholarly publishing. Since English has indisputably become the language of international scholarship and research, it is urgent to have the skill in writing scientific articles benefitting the support from IR 4.0 principles. What is going on with academics and what should they do dealing with the IR 4.0 pertinent to their scholarly writing activities, benefits, and drawbacks. This paper discusses what scholarly publishing in the Industrial Revolution 4.0 and how it supports the process of writing articles for publishing. The purposes of this paper are to explain (1). What are scholarly publishing and industrial revolution? (2). Why is it essential for academics? (3) How does writing scholarly publication in Industrial Revolution 4.0 work? Writing the scholarly publication in English has become obligatory for Teacher-Educator Authors (TEAs) and undergraduate students to doctoral students in Indonesia. The study was a descriptive qualitative library to build a conceptual paper for the purpose of a further study. The method was by reviewing any related kinds of literature to provide sufficient arguments for the three research questions. The outcome is several claims, arguments, and evidence gambits for any author to consider. Theoretically, the IR 4.0 principles have significantly eased the flexibility of the process, the quality, and the credibility of research outputs.
Scipp: Scientific data handling with labeled multi-dimensional arrays for C++ and Python

scipp is heavily inspired by the Python library xarray. It enriches raw NumPy-like multi-dimensional arrays of data by adding named dimensions and associated coordinates. Multiple arrays are combined into datasets. On top of this, scipp introduces (i) implicit handling of physical units, (ii) implicit propagation of uncertainties, (iii) support for histograms, i.e., bin-edge coordinate axes, which exceed the data’s dimension extent by one, and (iv) support for event data. In conjunction these features enable a more natural and more concise user experience. The combination of named dimensions, coordinates, and units helps to drastically reduce the risk for programming errors. The core of scipp is written in C++ to open opportunities for performance improvements that a Python-based solution would not allow for. On top of the C++ core, scipp’s Python components provide functionality for plotting and content representations, e.g., for use in Jupyter Notebooks. While none of scipp’s concepts in isolation is novel per-se, we are not aware of any project combining all of these aspects in a single coherent software package.
A Survey Paper On Consensus Algorithm Of Mobile-Healthcare In Blockchain Network

Technology grows at a rapid rate and trust has involved in a risk judgment between different parties. Authentication and Authorization generally determines the trust while dealing with transactions of data over different channels. Blockchain has become a digital backbone due to its security, time-management, and transparency on data decentralization ledger. This survey paper studies the existing blockchain structure, analyses the research gaps and proposes a new theoretical approach based on neighbor monitoring and updating model. This model may help the patients to decentralize real data and results in the blockchain network. This work discusses in detail about the various security challenges in block chain against 51% attack and double-spend attack by proposing a theoretical framework for the problem.
The Impact of Human Capital and Foreign Direct Investment on Economic Growth: An Empirical Study on Jordan

Human capital is a real factor in improving the investment climate and attracting foreign investment. FDI also increases human capital in the host country through the transfer of advanced technology and the rehabilitation of local labor. The importance of the study comes from Jordan's serious endeavor to attract foreign direct investment and to present itself as a rich country in human and qualified capacities. This study examines the effect of human capital and foreign direct investment on economic growth in Jordan employing Auto Regressive Distributed Lags Bounds Testing (ARDL BT) co-integration method for the spanning period from 1984 to 2018. The results indicate that there is a long- run relationship among variables. The results showed that there is a negative and statistically significant effect of human capital index (HCI) on economic growth (GWP) in the long run, and a positive and statistically significant effect of FDI (GFDI) on economic growth (GWP) in the long run. The estimation results indicate that a 1% increase in (HCI) decreases (GWP) by 0.272%, and a 1% increase in (GFDI) increases (GWP) by 0.006%. This study is one of the few studies that highlight the challenges facing both HC and FDI in increasing economic growth in Jordan and provides some recommendations.
Differential privacy location data release based on quadtree in mobile edge computing

Abstract To preserve user privacy in the release of location data, some researchers have proposed a distributed data release framework and a location data release algorithm based on mobile edge computing. This framework can preserve location privacy even if location‐based service providers are not trusted. However, the proposed algorithm fails to balance the noise error and the uniform hypothesis error and fails to take the query consistency constraint into account, so query accuracy needs to be improved. Therefore, we follow this framework and propose a differential privacy quadtree partitioning algorithm based on data uniformity heuristic adjustment to further improve query accuracy. First, a differential privacy complete quadtree is constructed, and then the quadtree is heuristically adjusted based on the predefined noise count threshold and the data uniformity threshold to balance the two types of error. Finally, query consistency processing is performed to further improve query accuracy. Experiments based on real‐world datasets are conducted to study the impact of two thresholds on query accuracy. Compared with the previous quadtree‐based differential privacy partitioning algorithm, our proposed algorithm has higher query accuracy while preserving location privacy. In addition, to address the problem of location privacy leakage in the query process, a range counting query framework based on the Hilbert curve is proposed to preserve location privacy in the query process.