Reviewerly/exHarmony huggingface dataset:
Environmental Geochemical Assessment of Heavy Metals in Soil and Sediment of (Shatt-Al-Hilla) Babil Governorate, Central Iraq

Heavy metals concentration in the soils and sediments has increased worldwide during the last century as a result of the rapid increase in population which combined by an increase in human activity as agriculture, industrial and many other activities. Ten soil and three river sediment samples were collected from 10 main sampling stations at Shatt Al-Hilla River from Sada area to Dora Bridge in Babylon province. The chemical analysis of the sedimentation sample in the laboratory included pH calculation, electrical conductivity (EC) (Cation Exchange Capacity (CEC), organic matter (OM), and heavy metals as (Mn, Ni, Cr, Zn, Cu, Co, Pb, Cd, As and Fe). Indirect geochemical background (IGB) of heavy metal was calculated by the iterative 2 standard deviations (SD) method.&#x0D; The results of enrichment factor for heavy metals of the soil and sediment show that the all heavy metals in the studied samples were within Ef&lt;2 indicate to depletion to minimal enrichment (i.e. no or minimal pollution). While Cd in the sample (5), Co in the sample (7), Cr in the sample (8) and Mn in the sample (1S) are within 2 â‰¤ EF &lt; 5 indicate to Moderate enrichment. Contamination factor (Cf) for heavy metals of soil and sediment show that the all heavy metals in the studied samples were within Cf &lt; 1 -Low contamination. While Sample "2" (Cd, Fe), sample "3" (Ni, Cr, Pb, and Cd), sample "4" (Mn, Fe) and sample "8" (Cr) are within 1 â‰¤ Cf &lt; 3 moderate contamination.&#x0D; Pollution load index result of all the soil and sediment samples are less than one indicate that "no pollution" are present, except sample 3 where the PLI value higher than 1 indicates the samples have been "polluted ". The modified degree of contamination (mCd) data indicate nil to a low degree of contamination for all of the soil and sediments samples.
An Application of the Kalman Filter Recursive Algorithm to Estimate the Gaussian Errors by Minimizing the Symmetric Loss Function

Kalman filtering is a linear quadratic estimation (LQE) algorithm that uses a time series of observed data to produce estimations of unknown variables. The Kalman filter (KF) concept is widely used in applied mathematics and signal processing. In this study, we developed a methodology for estimating Gaussian errors by minimizing the symmetric loss function. Relevant applications of the kinetic models are described at the end of the manuscript.
FILM DOKUMENTER SEBAGAI MEDIA INFORMASI KOMPETENSI KEAHLIAN SMK NEGERI 11 SEMARANG

SMK Negeri 11 Semarang is a school that has four competency skills, namely Graphic Preparation, Graphic Production, Multimedia, and Animation. Every time they enter the beginning of the school year, school socialization and skills competencies are carried out to new students through the Student Orientation Period, using the lecture method from each of the Head of Skills Competencies. Researchers have created an attractive and informative audio-visual presentation media, packed with docudrama stories, namely documentary films. The results of validation tests conducted by expert experts and users show that the documentary films that have been developed by researchers fall into the very good criteria with a percentage of 93.75%. In addition, through the use of the 228 new students as respondents, it can be concluded that the documentary film developed by the author falls into the good criteria with a percentage of 61%.
Intrusion Detection System Using Data Stream Classification

Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time.
Genetic-based Summarization for Local Outlier Detection in Data Stream

Outlier detection is one of the important tasks in data mining. Detecting outliers over streaming data has become an important task in many applications, such as network analysis, fraud detections, and environment monitoring. One of the well-known outlier detection algorithms called Local Outlier Factor (LOF). However, the original LOF has many drawbacks that can’t be used with data streams: 1- it needs a lot of processing power (CPU) and large memory to detect the outliers. 2- it deals with static data which mean that in any change in data the LOF recalculates the outliers from the beginning on the whole data. These drawbacks make big challenges for existing outlier detection algorithms in terms of their accuracies when they are implemented in the streaming environment. In this paper, we propose a new algorithm called GSILOF that focuses on detecting outliers from data streams using genetics. GSILOF solve the problem of large memory needed as it has fixed memory bound. GSILOF has two phases. First, the summarization phase that tries to summarize the past data arrived. Second, the detection phase detects the outliers from the new arriving data. The summarization phase uses a genetic algorithm to try to find the subset of points that can represent the whole original set. our experiments have been done over real datasets. Our experiments confirming the effectiveness of the proposed approach and the high quality of approximate solutions in a set of real-world streaming data.
An Effort Estimation Method for Service-Oriented Architecture

Determining size and effort of SOA systems is critical for managing SOA projects.As a consequence, a number of methods have been proposed to estimate effort of building SOA projects but the problem of estimating SOA development effort still remains largely unresolved mainly because there is limited attempt in using size metrics to estimate SOA development effort.To address this problem, this study proposed an effort estimation method for SOA centred on size metrics and effort factors.The proposed method enables estimation of effort factors using fuzzy logic technique to improve on estimation accuracy.The method was automated into a tool to facilitate entry of parameters and display of results.The study employed experiment research design based on 15 SOA projects developed by computer science undergraduate students to validate the proposed estimation method.To complement the experiment, we used a survey study involving 20 programmers from the industry to confirm the relevance of effort estimation factors proposed in this study.Result from the experiment revealed that the proposed method is more accurate and returned a lower Mean magnitude of relative error (MMRE).Response from the survey showed that the proposed effort factors are valid and they have influence on SOA development effort.
Geochemical assessment of heavy metal impact on soil around Ewu-Elepe Dumpsite, Lagos State, Nigeria

The concentrations of certain heavy metals in the top soils around the Ewu-Elepe dumpsite and its environs were determined with a view to ascertaining the level of metal pollution in the soils of the dumpsite vicinity. Thirty-two (32) soil samples were randomly collected within the dumpsite vicinity and one control sample was collected from a distance of about 9.0 km from the dumpsite. The samples were prepared according to standard procedures and analyzed for some heavy metals (Ni, Mn, Co, Cd, Cu, Pb, and Zn) using the Atomic Absorption Spectrophotometer at the Geochemistry Laboratory of the Department of Geology, Obafemi Awolowo University, Ile-Ife. The geochemical data was subjected to both univariate and multivariate statistical treatments and comparisons were made with various world standards. The results obtained showed that the mean concentrations of Ni (12.00 ppm), Mn (525.60 ppm), Co (5.64 ppm), Cu (44.59 ppm), and Zn (105.28 ppm) were within the acceptable limits for agricultural soils while those of Cd (1.40 ppm) and Pb (33.74 ppm) exceeded the limits. Geo-accumulation index and contamination factor revealed that the soils around the dumpsite were moderately contaminated with Cd and slightly contaminated with Pb. The overall decreasing order of heavy metal concentration in the dumpsite soil is: Cd &gt; Pb &gt; Zn &gt; Cu &gt; Mn &gt; Co &gt; Ni. The study concluded that the soils around the Ewu-Elepe dumpsite was contaminated with Cd and Pb and as such should be discouraged in its usage for agricultural related purposes as these highly toxic trace elements can be absorbed by plants. A well-engineered landfill that takes into consideration the local geology and the topography of the area should be designed so as to prevent infiltration of leachates into the soil and shallow groundwater systems.&#x0D; Keywords - Heavy metals, Soil,Ewu-Elepe,Nigeria,Dumpsite,Contamination.
Dealers of Peaches and Lemons: How Can Used Car Dealers Use Trusted Car Data to create value?

The used car market is full of mistrust and uncertainties.Providing a vehicle history with trusted car data increases market transparency but also threatens the market position of used car dealers.But does a vehicle history also provide opportunities for them?Based on expert interviews, we propose three design principles addressing current problems.The evaluation results of prototypes -which are based on these principles -indicate that they could improve the current market situation by easing data access, increasing the efficiency of sales related processes, and by expanding the competences of used car dealers.This paper is one of only few papers analyzing the current situation of used car dealers in the light of emerging new technologies.It indicates that used car dealers do not necessarily become disintermediated by the provision of trusted car data but rather have opportunities to reinvent themselves.
Challenges and Opportunities in the Application of China’s Central Bank Digital Currency to the Payment and Settle Account System

&lt;p&gt;With the advancement of the Internet and big data technology, currency has gradually developed in the direction of virtualization and digitization. The governments and central banks of various countries have gradually begun to pay attention to the role of digital currencies, and central bank digital currencies have emerged as the times require. As one of the first countries to study digital currency, China will devote itself to applying central bank digital currency to actual transactions in the future.&lt;/p&gt;&lt;div&gt;This article focuses on how China’s CBDC is used in the transaction settlement system. The opportunity is that the central bank’s digital currency technology improves the security, flexible operation and diversified application scenarios of the overall transaction settlement system, and further safeguards China’s national financial sovereignty. The challenge is that the current technology is still uncertain and different from the traditional RMB. In the future, China’s CBDC will also compete fiercely with other sovereign currencies. It is recommended to strengthen technological innovation, improve the relevant systems and regulations of the central bank’s digital currency operation, and make relevant policies to prevent the United States from imposing political and economic sanctions on China. This article will provide&lt;/div&gt;&lt;div&gt;suggestions on the challenges that the People’s Bank of China may face in the future application of digital currency to the transaction settlement system.&lt;/div&gt;
Classification of Underdeveloped Areas in Indonesia Using the SVM and k-NN Algorithms

The determination or classification of underdeveloped areas essentially consists of classifying several observations taking into account existing indicators. The classification method used is K-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). This study aims to analyze the accuracy of the classification between SVM and k-NN algorithms in the classification of underdeveloped areas in Indonesia. The data source used in this study is secondary data obtained from the Central Bureau of Statistics (BPS). The data used are 514 districs and municipalities of Indonesia. After analysis, the conclusion is that there are 122 districs and municipalities that are left behind out of a total of 514 districs and municipalities in Indonesia. The most underdeveloped areas are on the island of Papua, followed by the areas of the islands of Bali and Nusa Tenggara, and Sulawesi. Based on the results of the classification of underdeveloped areas using the method SVM with the kernel RBF has the best results with the parameters C = 1 and γ = 0.05 while the results of the classification of underdeveloped areas using the method k-NN obtains the best results with k = 15 Based on the results of classification of underdeveloped areas using the SVM and the k-NN method, including the level of classification is very good. The two methods compared have the same precision value of 92.2% and can be used to determine the classification of underdeveloped areas. Keywords: classification, machine learning, supervised learning, underdeveloped areas.
Higher Education and Globalization in the Context of the COVID-19 Crisis

The competition of civilizations forced the European universities to adapt to the competition with the Chinese and American universities. European integration cannot advance without the collaboration between European universities. An answer to these challenges is the CIVICA project, the European University of Social Sciences, a consortium of the following universities: Bocconi University (Italy), Central European University (Hungary), European University Institute (European Intergovernmental Organization), Hertie School of Governance (Germany), The National University of Political Studies and Public Administration (Romania), Sciences Po (France) and the School of Economics in Stockholm (Sweden). This experiment aims to become one of the European pilot universities, in the first round of applications for Erasmus+ in February 2019. The experiment takes into consideration the most important resource of a country: the human resource. The network of universities that are part of the CIVICA consortium will group approximately 38,000 students, 7,000 teachers and 3,000 people from the administrative apparatus. The London School of Economics is part of the CIVICA consortium, as an associate partner. At the Bucharest conference, the public presentation of the consortium and its objectives, the rectors of the seven universities set out to educate the future generations of professionals in social sciences, in order to solve the most pressing problems of the world. Creating a European identity is essentially the long-term, fundamental objective of the CIVICA consortium. In the context of this conference, we interviewed a few representatives of CIVICA, and their answers will be analyzed in this paper’s section dedicated to the results of the research.
Word Recognition and Reading Skills to Improve Reading Comprehension

This research aimed at showing the influence of word recognition, and using skimming and scanning skills to improve reading comprehension. Participants were a group of 15 students whose ages ranged from 14 to 16. They had problems in reading comprehension and vocabulary. This action research was conducted at a private language institute. The instruments to collect data were pre- and post-surveys, pre- post-tests, learning logs, skimming and scanning forms, and an interview. They provided quantitative and qualitative information. Results showed that there was a statistically significant improvement in parts of speech knowledge from the pre- to the post-test. The result was an average improvement of 28.2% in student performance. Cohen’s d was calculated with a result of 1.09 which means there is impact in learning. There was also a steady improvement in skimming and scanning which was exemplified by the ability to correctly complete a form after reading texts. Lastly, students’ perspectives were positive to this innovation. Therefore, it is advisable to apply the same innovation with other learners in order to compare results of improvement of reading comprehension and overall proficiency.
Artifact Mitigation in High-Fidelity Hypervisors

The use of hypervisors for cyber operations has increased significantly over the past decade, resulting in a concomitant increase in the demand for higher fidelity hypervisors that do not exhibit the markers, or artifacts that identify the execution platform type (virtualized or bare metal), prevalent in most currently available virtualization solutions.To address this need, we present an in-depth examination of a specific subset of virtualization artifacts in order to design and implement a method of mitigation that reduces the detectability of these artifacts.Our analysis compares the performance of a bare metal machine, a virtual machine without artifact mitigation, and a virtual machine with our proof-of-concept mitigation technique applied to a temperature sensor.Results of the implementation are analyzed to determine the potential impact on system performance and whether our mitigation technique is appropriate for extending highfidelity hypervisors.
Research on the Architecture of Convolutional Neural Network Accelerator

Abstract Convolutional neural networks are widely used in human production and life, but due to their large amount of calculation and complex calculation mode, their calculation speed is slow, so it is necessary to design a dedicated hardware accelerator. This paper firstly analyzes the algorithm of the convolutional neural network and decomposes the algorithm into multiple basic operations. For the convolution operation with the largest amount of calculation and complex operation mode, a near calculation storage array is designed according to its operational characteristics. Furthermore, a convolutional neural network accelerator architecture is proposed to realize the fast operation of a convolutional neural network.
Regulatory and Security Standard Compliance Throughout the Software Development Lifecycle

Our systematic literature review aims to survey research on regulatory and security standard requirements as addressed throughout the Software Development Lifecycle.Also, to characterize current research concerns and identify specific remaining challenges to address regulatory and security standard requirements throughout the SDLC.To this end, we conducted a systematic literature review (SLR) of conference proceedings and academic journals motivated by five areas of concern:1. SDLC & Regulatory Requirement 2. Risk Assessment and Compliance requirements 3. Technical Debt 4. Decision Making Process throughout the SDLC 5. Metric and Measurements of found Software Vulnerability.The initial search produced 100 papers, and our review process narrowed this total to 20 articles to address our three research questions.Our findings suggest that academic software engineering research directly connecting regulatory and security standard requirements to later stages of the SDLC is rare despite the importance of compliance for ensuring societally acceptable engineering.
Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The smart black box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with an FIFO strategy. The results show that SBB data compression can increase the anomalous-to-normal memory ratio by ∼25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to an FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.
LEARNING METHOD USING COMMUNICATIVE APPROACHES TO ENGLISH GRAMMAR TEACHING

Abstrak: Penguasaan grammar pada mata kuliah Basic English Grammar mahasiswa semester pertama di Universitas Billfath masih rendah sehingga diperlukan metode pengajaran yang tepat untuk mengakomodasi permasalahan tersebut yaitu pendekatan komunikatif dengan metode pembelajaran diskusi kelompok dan metode pembelajaran dengan presentasi. Penelitian ini bertujuan untuk mengetahui apakah diskusi kelompok dan presentasi dapat meningkatkan pemahaman dan penguasaan mata kuliah Basic English Grammar. Empat kelompok diskusi dengan total 16 mahasiswa semester pertama ditemukan setelah pretest. Diskusi kelompok dilakukan pada setiap hari perkuliahan (14 pertemuan). Hasil akhir perkuliahan ditentukan dengan posttest. Presentasi individu dilakukan di akhir perkuliahan. Uji t digunakan untuk menganalisis variabel skor pretes, postes, dan presentasi. Uji t Mann Whitney menunjukkan perbedaan yang signifikan sebesar 0,001 &lt;0,05 antara skor pretes dan skor postes. Perbedaan signifikan 0,026 &lt;0,05 juga diperoleh antara skor pretes dan skor presentasi. Analisis data menunjukkan bahwa proses perkuliahan dengan metode diskusi kelompok secara signifikan meningkatkan pemahaman dan penguasaan mahasiswa mata kuliah Basic English Grammar, sehingga membantu penguasaan materi tes presentasi.&#x0D; &#x0D; Abstract: The mastery of basic English grammar for first semester students in University of Billfath is still low. So that the right teaching method is needed to accommodate the problem, namely communicative approach with group discussion and presentation learning method. This research is aimed to find out whether group discussion and presentation can improve the students’ understanding and mastery Basic English Courses. Four discussion group with a total of 16 first semester students were found after the pretest. Group discussions are conducted on each lecture day (14 meetings). The final results of the lecture are determined by posttest. Individual presentations are carried out at the end of the lecture. The t test is used to analyze the variables of the pretest, posttest, and presentations scores. The Mann Whitney t test showed a significant difference of 0,001 &lt; 0.05 between the pretest score and the posttest score. A significant difference of 0.026 &lt; 0.05 was also obtained between the pretest score and the presentation score. The data analysis indicated that the lecture process with the group discussion method is significantly improved student understanding and mastery of Basic English Grammar courses, so that is help mastery the presentation test material.