Reviewerly/exHarmony huggingface dataset:
Afterword: Novel Knowledge, or Cleansing Dirty Data: Toward Open-Source Histories of the Novel

Abstract This afterword discusses the most important, most under-rewarded, and most unsexy aspect of data visualization: the production and use of reliable underlying data. Starting from the premise that visualizations are only as good as their underlying evidentiary base, Freidman addresses the contributions of digital projects that have laid the foundation for such practices, including massive multi-institution projects like Orlando , mid-sized projects like The Early Novels Database (END), and the author’s own small-scale project, Manuscript Fiction in the Age of Print (MFAP). Following this assessment, the author proposes a set of guidelines for best practices in creating new data so that amendable, transformable visualizations can be produced, built on collective knowledge.
Lexicographic Constrained Multicriteria Ordered Clustering

Researchers in the multicriteria analysis community have recently started to study the application of clustering techniques. Different methods have been proposed leading to complete or partial multicriteria partitions. Recently, a method based on the exploitation of the ordinal properties of a valued preference relation has been developed in order to obtain a totally ordered clustering. The underlying idea is to find a partition that minimizes the inconsistencies between the obtained ordered clusters and the input preference matrix. The exact algorithm gives the guarantee to achieve this goal based on a lexicographic order. In this contribution, we propose to further extend their approach to the case where minimum and maximum capacities are imposed on the cluster sizes. This leads to add a constraint satisfaction test (that is based on the levels and the ranks of the graph built during the procedure). The algorithm is evaluated regarding its execution time and illustrated on the academic ranking of world universities.
Applicational Statistics in Data Science and Machine Learning

In the domain of data science and machine learning, statistics plays a huge role. When it comes to gaining insights and building quality features out of the data to train any model, statistical tools and techniques along with the concepts of exploratory data analysis assist in doing the same. A data scientist or data analyst is incomplete without the knowledge of statistics because this is the building block of a machine learning or deep learning model which has learned or needs to learn trends and patterns from the features which were built by analysing the data end-to-end, be it in any tabular form or in picture format or video format. Also, as it covers a lot many concepts under statistics like variables, sampling, correlation, outlier treatment and much more, this chapter solely aims to take the reader to a tour of applicational statistics and how it can be combined with exploratory data analysis to easily work on data science and machine learning. Also, data analysis and machine learning are domains that are experiment heavy and need correct statistical methods for correct inferencing. Hence, for these experiments, the different statistical methods in place are discussed here in detail. There are different languages like Python, MATLAB, R and much more which have libraries for statistical mathematics and make simple API calls to do the required experiments within any dataset.
Exploration for Fe-Mn Oxides Using Geochemical Signatures in Soil: A Case Study of Part of Northwestern Nigeria

Part of northwestern Nigeria was investigated with the aim of delineating concealed mineralization using geochemical signatures in soils. To achieve this, 30 selected soil samples were analysed geochemically. The result of the elemental analysis was subjected to Principal Component Analysis (PCA) and isograde plotting, while selected elements were correlated. From the geochemical result, most of the analysed elements have anomalous value in the southern part of the area, while the least values are in the southwestern. From the PCA analysis, six factor groups were distinct. The factor groups were interpreted geochemical to fingerprint mineralization in the area. The result of correlation analysis shows that Fe is negatively correlated with most of the correlated elements. The study concluded that the central part of the study area is mineralized with both manganite and goethite. In addition, manganese mineralization is indicated by elemental association: Zn+As+Be+Bi+Co+Nb+Ni+CsP+Al+Ca+Cd+Li+K, while iron mineralization is indicated with Zr+Th+Pd+Mo+V+Sn+Cr+Ce+InSc+P+Pb association.
Application of Deep Learning in Recognizing Bates Numbers and Confidentiality Stamping from Images

In eDiscovery, it is critical to ensure that each page produced in legal proceedings conforms with the requirements of court or government agency production requests. Errors in productions could have severe consequences in a case, putting a party in an adverse position. The volume of pages produced continues to increase, and tremendous time and effort has been taken to ensure quality control of document productions. This has historically been a manual and laborious process. This paper demonstrates a novel automated production quality control application which leverages deep learning-based image recognition technology to extract Bates Number and Confidentiality Stamping from legal case production images and validate their correctness. Effectiveness of the method is verified with an experiment using a real-world production data.
The design of learning media using JavaScript and its implementation in the local web server

Abstract The ranking of Indonesian science competencies at the global level in 2018 was in the 70th of 78 countries. The low student learning activities is one of the trigger factors. One impacts the use of learning media which is not appropriate, such as decreased learning motivation which ultimately impacts the low student learning activities. This research intends to design learning media based on offline using JavaScript programming language and combined in a local network system. The method used is Research and Development. The specifications of the tools and materials used are MAC Mini Intel Core I7 Quad-Core 3.6 GHz, 16 GB DDR4 memory, and SATA 2 TB HDD. The use of learning media is expected to overcome several problems in conventional learning and become one of the solutions to distance learning systems.
Accelerated Evaluation of Autonomous Drivers using Neural Network Quantile Generators

Whereas autonomous driver engineering has limited control on outcomes for individual scenarios, engineering processes must exert control over the performance statistics. A key challenge impeding statistical confidence is the need to test for a long list of infrequent hazardous events. Using "smart miles" promises to reduce the evaluation cost by increasing the frequency of those infrequent hazardous events. We propose a simulation based Bayesian approach which increases the frequency of those infrequent events by many orders of magnitudes as compared to naturalistic miles. We represent the Operational Design Domain (ODD) using a population of scenarios, and provide methods for sampling the ODD. We propose a quantile function based sampling approach which is able to generate a single sample with thousands of instances using a single forward pass of a deep neural network (DNN). We develop a practical sampler training method for an "inverted DNN" architecture. The resulting sampler is capable of generating a skewed distribution comprising of "smart miles" in which hazardous events of interest occur at a frequency >95%. To gauge the quality of the generated sample we propose the quality metrics of histogram diversity, histogram homoscedasticity and average sample distance.
Digital Forensic Readiness in IoT - A Risk Assessment Model

With the increased adoption of IoT devices they have become an important source of digital evidence, and could be a vital part of investigations both for companies and law enforcement agencies. There are however some present challenges such as identification of devices, what data could be evidence (if the device stores any), and privacy. Because of this, digital forensics readiness is essential in these ecosystems. It is an important part of both risk assessment and preparation for contingencies. The devices, their potential, and procedures in case of an incident or attack, needs to be predetermined. In this paper we suggest a risk assessment model to prepare for forensic analysis in IoT, which we have called Forensics Readiness in IoT Implementation (FRIoTI), to meet the mentioned challenges.
Blockchain Applications in Oilfield Underground Injection Operations

Abstract This research project has successfully built a Distributed Ledger Technology (DLT) based prototype using R3 Corda open source. Its purpose applies in the oil &amp; gas underground injection control (UIC) operations for the underground aquifer protection. This DLT prototype is a permissioned network that allows oil &amp; gas companies to create, disseminate, and trace immutable records. The network enables oil and gas companies, government regulatory agency, and all other participants to share secure records such as well information while maintaining data integrity, traceability, and security. The purpose is to create a network of trust among all the stakeholders in the UIC processes for underground aquifer protection. In this DLT network, a company submits well information, which will be digitally signed and notarized. Unauthorized changes to the information, ownership, or history will become infeasible, thanks to the underlying cryptographic technologies of DLT. The network designs so that information stored and communicated will have a high level of trustworthiness. Every participant in the network can get simultaneous access to a common view of the data. Corda platform also provides multiple functionalities, e.g., Smart contract, Vault, Identity Management, Scheduler, Notary Services, etc. Many of the functionalities automate the data processing within the DLT databases. This project's results expect to enhance public safety and improve the aquifer protection review and operational processes. Kern County uniquely poises for a project to develop more streamlined, effective, and entirely digitized DLT-based workflows that will secure regional environmental data integrity. Water contamination is a primary concern in a region where water and petroleum play vital roles in the economy. Both industries and regulatory agencies pay close attention to environmental quality. Data integrity is a primary issue concern for those that monitor and analyze environmental data. Monitoring and forecasting based on available immutable data are imperative to mitigate complications. We have changed the manual workflow into DLT applications which takes advantage of built-in functionalities. The new review process can avoid repetitive reviews among all participants and shorten the approval time. The embedded smart contracts on the DLT network will also help automate the workflows, and therefore, will be able to help eliminate human errors and improve the turnaround time. The prototype model proves the concept of using DLT. Our research work demonstrates DLT successfully implement into energy technology. The prototype model will further expand to all the UIC processes, such as thermal, wastewater disposal, waterflood, gas injection &amp; disposal, etc. It is a substantial cost and time savings for all the oil and gas companies. The results of this analysis could provide the government with valuable information for significant policy and regulation decisions to further benefit the community and society.
External Auditors' Assessments of Cyber-Security Risks

The mushrooming occurrences of cyber criminals in the recent years have provoked an alarm about the drawback of technological growth and the increasing dependence of human beings on technology. The severity of this situation in the business world is even greater and greater than other fields, which leads many people raise a question about the response of external auditors - the ones who are responsible for detecting any accounting faults - towards cybersecurity-attacked companies - the ones which can try their best to hide their difficulties from their investors and stakeholders. Hence, this study investigates whether external auditors pay more attention to cybersecurity-attacked companies by applying higher audit fee charges. Using a sample of 100 global small, medium and large companies, the study has found out that there is a positive relationship between audit fees and breach, which means that external auditors find more risks and exert more efforts when auditing the cybersecurity-attacked companies.
Target Detection Method of Remote Sensing Image Based on Deep Learning

With the rapid development of aerospace and sensing technologies, the remote sensing technology of various countries all over the world has reached a relatively high level. It is an important part of machine learning field to apply deep learning to target detection. Based on the target detection of remote sensing image, this paper analyzes the existing target detection algorithms of remote sensing image and studies the optimal solution based on deep learning.
Probabilistic Segmentation of Word Forms into Affixes and Word Roots

This paper introduces a method for segmenting a given word into word parts, including affixes, word stem, and word roots. In our approach, word parts including affixes and word roots in a given training dataset are counted and relevant probability values estimated. The method involves training a probabilistic model on a set of annotated word segmentation, finding most probable word stem and affixes, and finally further segment word stem into word roots. At run-time, we first strip the affixes off the given word to derive the stem. Then we segment the stem word into word roots. We enumerate all possible segmentation, and the most probable segmentation is then returned as the best morphological segmentation of the given word. Moreover, we adjust our probabilistic model by considering the rules for adding suffixes to word roots and the positions of prefixes and suffixes in a word. Preliminary evaluation shows that the proposed method is competitive with previous works.
Crowd Counting Method Based on Scale Adaptive Network

In view of the difficulty in crowd counting due to occlusion and unequal distribution in crowded scenes, this paper presents a people counting method based on scale adaptive network. In this method, the first ten sixteen-vgg layers are used to initially obtain the population characteristics; then the scale adaptive module designed by us is regarded as the main branch, which can extract the multi-scale population characteristics, and at this stage, the expansion convolution is introduced to further optimize the feature map; in order to effectively solve the occlusion problem between crowds, this paper designs a shallow convolution module as another branch, and fuses its output feature map with that of scale adaptive module. Finally, this paper selects representative datasets to test the method. The results show that in terms of mean absolute error (MAE) and mean square error (MSE), the proposed method is significantly lower than the comparison method.
Social Media Analytics: A Case Study of Singapore General Election 2020

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.
Validation of Test Construction Skills Inventory through the lens of Item Response Theory (IRT)

The Item Response Theory (IRT) examines how testees at different ability levels respond to an item. The theory assumes that latent traits that are fewer in number than the test items account for responses to the items. This study validated the test construction skills inventory through the lens of IRT. Also, the study assessed teachers’ level of skills in test construction. The descriptive survey design was employed for the study. Using the cluster sampling, 583 senior high school teachers in the Cape Coast Metropolis were engaged in the study. The 3-parameter logistic model (3-PLM) through the IRTPRO software was used to validate the test construction skills inventory. Eighteen out of 25 items were deemed valid and used to assess teachers’ skills in test construction. It was found that the majority of the teachers had higher skills in test construction. Recommendations were made to the Ministry of Education, Ghana (MoE), Ghana Education Service (GES), and heads of the various SHSs in the Cape Coast Metropolis.
Chosen Ciphertext Attacks Secure Inner-Product Functional Encryption from Learning with Errors Assumption

Functional Encryption (FE) is an ambitious generalization of public key encryption (PKE), which overcomes the all-or-nothing feature and is an emerging technique for cloud computing. Security against chosen ciphertext attacks (CCA) is the de facto level of security required for PKE used in practice. We first show a generic construction from (selective id) chosen plaintext attacks secure identity-based functional encryption (sIBFE-CPA) to CCA secure FE, which is efficient and interesting, resulting in constructing CPA, even CCA FE is to construct CPA IBFE. Then we give an instantiation of sIBFE scheme for inner product (IP) functions from standard learning with errors (LWE) assumption, which applying our transformation gives the first CCA secure IPFE under the same assumption.
Data Visualization and the Data Funnel Interface

The paper introduces the "data funnel" interface to visualization studies. This database interface organizes raw data into nested sets of data that guide users to information in a table. It combines a graphic display of the nested sets of data with the ability to navigate them to pinpoint a table row. The newly discovered branching data model (BDM) provides the framework for combining these two capabilities into a single hierarchical structure. This data model relies on a mathematically precise representation of the data mapping between two table attributes. It constructs a uniform pattern of one-to-many data mapping that depicts a primitive tree structure. A second, more advanced expression, the BDM chain, aggregates these small tree structures. Program logic parses the BDM chain to absorb the individual one-to-many branching patterns into larger, tree structures. The paper discusses how these tree structures reflect upon the logical components of the table, including how the data in its paths model each table row. It also explains how the BDM constructs data files for two different types of data funnel interfaces. In both interfaces, the technology generates all of the components automatically. With these interfaces, end-users are free to browse and explore the data content of a database table. Each time the user selects a data option, the interface updates its display of relational data to recommend the information located in the table. With each new selection, the interface acts like a funnel that moves the users one step closer to the information they want to see.
Study on parameters influencing the VM migration in datacenters

The desired attributes of datacenter gained from virtual machine migration are Fault tolerance, Load balancing, high availability, energy saving and disaster recovery. The datacenter administrator initiates Virtual Machine (VM) migration and as part of the migration process it is necessary to make an intelligent decision to recognize a suitable VM to be migrated. Identifying an apt VM to be moved will aid in remarkable performance. The choice of opting for the exact VM to be migrated rest on various factors namely downtime, total migration time, total transferred data, computational resources relinquished, memory resources relinquished and application performance during migration. The parameters are influenced based on type and nature of the application. This paper discusses the different parameters influencing the apt VM to be migrated by analyzing the afore mentioned parameters.
MENINGKATKAN HASIL BELAJAR IPS MENGGUNAKAN MEDIA GAMBAR MENGENAI KERAGAMAN SUKU BANGSA DAN BUDAYA DI INDONEISA

The problem in the classroom is the low learning outcomes of students social studies learning. Where to solve this problem researchers apply approach with image media so that it is expected to improve results learning fourth grade students of SD Sejahtera Way Kandis in social studies learning. Method The research used is classroom action research with a groove action planning, action, observation and reflection. Subject The research was the fourth grade students of SDN 2 Palembang. The material taught in the first cycle is various ethnic groups, and the second cycle is cultural diversity. Data collection was carried out in each cycle. The data collected in the form of qualitative data and quantitative data. Data collected in three ways, namely tests, observations, interviews. The results obtained on cycle I, namely from 20 students 14 students experienced completeness of learning with the percentage of classical completeness 70% and the percentage of classical absorption 77.75%, This result is still in the sufficient category so that there are still shortcomings that must be fixed. The results obtained in cycle II experienced increase, all students experience learning completeness, with a percentage 100% classical complete and 86% classical absorption. Average value teacher activity in cycle I is still in good category and cycle II is very good, and the average value of student activity in cycle I is sufficient and cycle II is located in either category. Based on predetermined indicators, it can be concluded that by using a group learning model student mastery of the material continues to increase so that it has an impact on increase in social studies learning outcomes for fourth grade students of SDN 2 Palembang.
Transfer Learning for Multi-Premise Entailment with Relationship Processing Module

Using the single premise entailment (SPE) model to accomplish the multi-premise entailment (MPE) task can alleviate the problem that the neural network cannot be effectively trained due to the lack of labeled multi-premise training data. Moreover, the abundant judgment methods for the relationship between sentence pairs can also be applied in this task. However, the single-premise pre-trained model does not have a structure for processing multi-premise relationships, and this structure is a crucial technique for solving MPE problems. This paper proposes adding a multi-premise relationship processing module based on not changing the structure of the pre-trained model to compensate for this deficiency. Moreover, we proposed a three-step training method combining this module, which ensures that the module focuses on dealing with the multi-premise relationship during matching, thus applying the single-premise model to multi-premise tasks. Besides, this paper also proposes a specific structure of the relationship processing module, i.e., we call it the attention-backtracking mechanism. Experiments show that this structure can fully consider the context of multi-premise, and the structure combined with the three-step training can achieve better accuracy on the MPE test set than other transfer methods.