Reviewerly/exHarmony huggingface dataset:
A Framework for Information Mining from Audit Data

Companies seek new technologies to enhance their business processes. As information systems in companies become more complex, the traditional audit trail is diminished or eliminated. The importance of audit automation and the utilization of IT in modern audits has grown significantly in recent years due to both technological developments and changing regulatory environment. Automation of business processes has inevitably led to changes in auditing procedures and standards. Additional drivers of audit automation adoption include the ever growing complexity of business transactions and increasing risk exposure of modern enterprises. Therefore, the audit’s purpose, which is namely to examine the true and fair view of financial statements, is heavily increasing in complexity. On the other hand, the prevalence of the data paradigm has manifold impacts on the accounting-relevant processes. To cover the requirements to Audit Information System, we strive for the development of a framework for information mining from audit data. In this paper, we report on the framework we have developed in the department of Accounting and Finance. Our study identifies the management of audit alarms and the prevention of the alarm floods as critical tasks in this implementation process. We develop an approach to satisfy these requirements utilizing the data mining techniques. We analyse established audit data from a well-known data repository considering the dimensions of the data paradigm. This led us to a tentative proposal of a conceptual mechanism for an integrated audit approach. With the increasing number of financial fraud cases, the application of data mining techniques could play a big part in improving the quality of conducting audit in the future.
A novel method based on digital holographic interferometry (DHI) to in-situ register the dynamic behavior of concrete 20th century building heritage

In the last 100 years concrete and steel have been the most common building materials, that is the reason why they constitute the core of the structures on which the architecture of the 20th’s century is based. Both materials present high resistance and durability but the recent collapse of Minardi Bridge in Italy shows that it is absolutely necessary to carry out real-time monitoring of the conservation status of concrete. In this sense, the building of the Agencia Impositiva Federal (A.F.I.P.) which is located in the city of Paran´a, built in the 70s, represents a clear example of brutalist architecture in Argentina. After 40 years of its construction and during remodelling tasks, serious problems in the concrete structures were discovered that forced the evacuation of the building. In order to develop an in situ monitoring system that allows the premature detection of structural problems in 20th’s century heritage buildings, it was implemented an innovative scheme based on digital holographic interferometry (DHI) which includes a semiconductor laser, optical fibre and and ah hoc plastic support made by 3D printing. This system was tested on concrete specimens sujected to dynamic loads in the range of 1 &times; 10<sup>3</sup> kg to 28 &times; 10<sup>3</sup> kg using an Amsler hydraulic press. Results, though preliminary, showed that it is possible to measure in situ the radial deformation of normalised cylindrical specimens under pressure loads and indicated that this low cost, novel DHI set up, could be applied in situ, as a preventive conservation method, to detect cracks and other defects in monumental concrete works.
A Parameter Transfer Method for HMM-DNN Heterogeneous Model with the Scarce Mongolian Data Set

Hidden Markov Model-Deep Neural Network (HMM-DNN) is one of the most successful architecture in speech recognition. Although the HMM-DNN achieved state-of-the-art results on English and Mandarin, we find that there are lots of not updated parameters during the training of HMM-DNN acoustic model on a small scale Mongolian data set. This caused the model's network training underfitting, which cannot learn the features of the data set. In the speech recognition scenario, the underfitting of speech features leads to a problem that the accuracy of the system decreases. In this regard, we define the concept of the homogeneous model heterogeneous model and propose a parameter learning method for HMM-DNN heterogeneous model in the scarce Mongolian data set. In our experiment, we choose KALDI as the experimental platform with the TIMIT English data set as the source data set and the scarce Mongolian data set as the target data set. Through the proposed parameter transfer method, we achieved much better performance on Mongolian recognition accuracy.
Learning with Heterogeneous Misspecified Models: Characterization and Robustness

This paper develops a general framework to study how misinterpreting information impacts learning. Our main result is a simple criterion to characterize long-run beliefs based on the underlying form of misspecification. We present this characterization in the context of social learning, then highlight how it applies to other learning environments, including individual learning. A key contribution is that our characterization applies to settings with model heterogeneity and provides conditions for entrenched disagreement. Our characterization can be used to determine whether a representative agent approach is valid in the face of heterogeneity, study how differing levels of bias or unawareness of others’ biases impact learning, and explore whether the impact of a bias is sensitive to parametric specification or the source of information. This uniﬁed framework synthesizes insights gleaned from previously studied forms of misspecification and provides novel insights in specific applications, as we demonstrate in settings with partisan bias, overreaction, naive learning, and level-k reasoning.
Blockchain and Scholarly Publishing Industry

This chapter will explore how blockchain and AI technology will address the current problems in the current publishing workflow including the author manuscript submission systems, peer review process, editing, production process, and dissemination process. Further, after the article has published, blockchain and AI technologies will allow all of the stakeholders in the value chain to benefit from a more efficient and effective upstream and downstream publishing process. This chapter will explore rights and royalties, anti-piracy and ebooks, and how blockchain and AI will create new research and business opportunities.
Real-Time Watermarking of Medical Images and Secure Transmission Through Steganography

Medical images and reports are highly confidential information and should not be leaked, or modified in any circumstances. Healthcare organizations constantly face cyber-attacks with the motive of stealing medical information for their personal gains, and during the COVID-19 pandemic, the attacks have almost doubled. Encrypting medical information and transmitting them over a secure channel ensures the confidentiality of information, however, can be stolen and decrypted later. Also, most of the health organization lacks in terms of security and privacy of their patient’s information. A real-time watermarking and steganography protocol can mitigate the aftermath of a cyber-attack by hiding confidential reports into useless looking files such as log files and user manuals as soon the reports are generated. We propose a secure steganographic medical report system that can hide medical reports as well as identify fake ones. The system uses real-time watermarking during report generation which prevents any kind of modification; thereafter, these data are transmitted/stored using a secure steganography protocol. Finally, the data is retrieved by doctors or health workers after stego-extraction and watermark verification.
Deep Reinforcement Learning Approach for Augmented Reality Games

The Augmented Reality (AR) technology has been around since 1968. It has been used in various applications serving different domains. Especially in the gaming domain, AR made a huge success. Nevertheless, these AR games rarely used Machine Learning (ML) techniques. Instead, they used simple kind of Artificial Intelligence (AI) algorithms. Hence, these games are un-realistic and predictable and so players often got bored too quickly.The use of ML enhances the players' experience dramatically and offers new and creative ways to playing games in AR. The main contribution of this paper is to propose a new approach that adopts Deep Reinforcement Learning (DRL) in AR games. In the proposed approach, a new algorithm is introduced to detect the player's behavior and update its policy. To evaluate the proposed approach, the paper introduces a method that gathers synthetic data in a custom-made environment. To study the impact of the proposed approach, we compare DRL policies of the agents before and after applying the proposed algorithm. The results reveal that the accumulative rewards of the proposed algorithm performs better than the untrained policies.
Ranger Random Forest-Based Efficient Ensemble Learning Approach for Detecting Malicious URLs

The massive quantity of data generated from a variety of resources is hard to manage the process and analyze. Big data enables machine learning algorithms to find meaningful patterns and make accurate predictions. Big data analytics can be used in organizations to process and analyze the vast amount of data to find the insights of data, such as risks, threats, and incidents. These incidents create more security issues. A wide range of non-constructional activities happening in WWW requires the detection of malicious URLs for internet security. Malicious URLs or Websites are hosting spontaneous content and involve many users to become victims of scams. This paper presents ensemble learning-based, faster, and memory-efficient random forest algorithm for detecting malicious URLs. The proposed method proves scale best with the number of instances and variables.
Research on Evaluation and Confirmation of College Students' Learning Behavior Based on Comprehensive Weighted Fusion Algorithm

In order to solve the problem of the lack of information in the evaluation of university students' learning behavior with single data, a multi-source data fusion evaluation of university students' learning behavior is proposed to explore the application value of data fusion technology in teaching. First use Spearman to calculate the correlation between different data sources, calculate the relationship weight and optimization weight of different data sources, and then comprehensively weight for fusion calculation. The fusion calculation of students' comprehensive learning behavior evaluation through comprehensive weighted fusion can not only balance the differences in learning behavior evaluation of students under different teaching methods, but also integrate the advantages and disadvantages of different learning methods. Experiments verify that the comprehensive weighted data fusion algorithm is effective, which can provide help for a more comprehensive analysis of the evaluation of college students' learning behavior and provide a basis for adapting to their own learning.
Fuzzy SVM Classifier for Clothes Pattern Recognition

Cloth pattern recognition is a strenuous effort for partially or completely blind people. The large intra-pattern variations are posing limitations to the machine-based algorithms. With this mind, we are developing a MATLAB code for recognizing patterns of the clothes and enhancing the image parameters like contrast, brightness, etc. To determine the clothes pattern, till now, a simplified SVM classifier was used, but we tend to implement the same with use of the fuzzy support vector machine whose accuracy is considered to be better than the traditional SVM. In a simplified SVM, label is assigned on the basis of hyper plane and kernel function while in fuzzy SVM, membership in terms of probability is also determined for each sample to be fall in each class. The present project model consists of a MATLAB code and a datasets for training images. We create our own fuzzy rules for the image enhancement such as brightness and contrast. We then send the image through fuzzy rules, and the output is shown in the form of audio format. By the help of this, it is of huge help for the blind people who cannot differentiate them due to the loss of vision.
Augmenting Automatic Clustering with Expert Knowledge and Explanations

Cluster discovery from highly-dimensional data is a challenging task, that has been studied for years in the fields of data mining and machine learning. Most of them focus on automation of the process, resulting in the clusters that once discovered have to be carefully analyzed to assign semantics for numerical labels. However, it is often the case that such an explicit, symbolic knowledge about possible clusters is available prior to clustering and can be used to enhance the learning process. More importantly, we demonstrate how a machine learning model can be used to refine the expert knowledge and extend it with an aid of explainable AI algorithms. We present our framework on an artificial, reproducible dataset.
The Champollion Project: Automatic Structuration of Clinical Features from Medical Records

Cancer is one of the leading causes of mortality worldwide and as populations age, the burden is growing. Treating increasing numbers of patients enables us to gather detailed medical records. Databases with exhaustive, high quality structured data are thus an essential resource for cancer researchers and provide invaluable information to clinicians whenever they need to treat their patients. In addition, these databases fuel our data strategy as the cornerstone of our digital healthcare ecosystem and they provide crucial support for the development of Artificial Intelligence-related projects. Feeding such databases and registries requires manual curation to ensure their quality over time. Finding alternatives to manual structuration is essential because around 80% of the relevant clinical information is contained in open text and it is costly to maintain teams of curators given the growing volumes of data generated every year. In this article we describe an Artificial Intelligence system developed at Institut Curie, capable of structuring clinical features from unstructured Electronic Health Records. Our system allows us to structure clinical data with reduced manual labor and with accuracy comparable to that of expert clinicians, empowering our data ecosystem and improving the support we can give to clinicians and researchers.
Coins With Benefits: On Existence, Pricing Kernel and Risk Premium of Cryptocurrencies

Cryptocurrencies come with benefits, such as anonymity of payments and positive network effects of user adoption, and transaction risks, such as unconfirmed transfers, hacks, and frauds. They compete with central-bank-regulated currencies as a means of payment but this competition is not perfect: consumers are not indifferent with respect to the currency to be used in transactions. In our arbitrage-free world consumers' utility from consumption depends on benefits offered by the type of the currency: for example, preserving anonymity may increase utility, while fraud would decrease it. Distinct stochastic processes governing benefits of the two currencies generate incomplete markets with crypto- and central bank currencies operating under distinct pricing kernels. We characterize the bitcoin kernel and its relationship with the pricing kernel in the centralized market. Our model allows us to evaluate the otherwise unobservable benefits and their contribution to the pricing kernel. The model explains both the co-existence of decentralized and centralized currencies and the risk and return patterns in the cryptocurrency world.
A Novel Semantic User Operation Restoration from Massive Web URL Log

In large distributed management information system, user operation logs are often implicit but cannot directly reflect the business semantics of user operations. In order to meet the needs of operation and maintenance personnel to carry out work such as security detection and exception analysis based on the business significance, this paper designs a business semantic restoration method for log URL access sequences based on fasttext algorithm. In our method, the original log data is processed to form a structured sequential log record with users as the unit. The log records of one single user will be mapped into a URL access sequence, and this sequence is divided into several business text segments which are composed of URLs and are associated with context semantics. Finally, through the supervised text classification algorithm fasttext, we will get a model that can recognize the business attributes of the text segment of the user URL sequence, in other words, realize the business semantic restoration of the user operation log sequence, which provides data support for the system operation and maintenance personnel.
A Literature Review on Generative Adversarial Networks with Its Applications in Healthcare

Since the discovery of neural networks, there is a trend of training the data (structured or unstructured) and predicting the outcome based on these training sets. Whereas these new neural networks, Generative Adversarial Networks or GANs are achieving popularity because of its capability to produce new data instead of classifying them. It operates on two neural networks fighting against one another, that would be able to co-train through plain old backpropagation. Its adaptive learning allows it to generate new data without replicating the previous outcome. Due to these advantages, GANs have opened a wide range of applications in the medical and healthcare sector. Essential applications like image segmentation, image-to-image translation, style transfer, and classification are getting more recognized. Given the growing trend of GANs in the medical community, we present an overview of Generative Adversarial Networks with its potential applications in the medical field. The main objective of this briefing is to investigate and provide a descriptive view of GANs and its applications in the healthcare sector. This paper also makes an effort in identifying GANs' advantages and disadvantages. Finally, we conclude the paper with future scope and conclusion.
A monitoring framework for transparency and fairness in big data platform

Summary Big data comprises large volume of complex, (semi) structured, and unstructured data which are processed and analyzed in order to help organizations in making strategic decisions. This paper investigates into privacy, transparency, and fairness of big data. It contends that these factors are crucial and must be taken into account when collecting, analyzing, and using large scale personal data. It proposes a novel framework that monitors personal data usage in big data platform in order to ensure that data usage is compliant with the transparency and fairness as stipulated by Privacy Transparency Fairness Agreement (PTFA) rules. The proposed framework is implemented using Python and Java. It is evaluated using multi‐nodes cluster with millions of lines of data and thousands of contracts. Various experiments are conducted in order to evaluate the efficiency and scalability of the proposed framework. These show that the proposed framework is efficient and scalable when monitoring transparency and fairness in big data collection, analysis, and usage. Visualization experiments are also conducted in order to derive relevant indicators, for instance, to monitor non‐compliance of PTFA rules, visualize the alerts, and show the proportion of personal data being violated as compared to the number of contracts.
A Continuous Speech Recognition System for Bangla Language

This paper presents a Bangla continuous speech recognition system implemented using a publicly available speech corpus collected using the crowd-sourcing method. The open-source Kaldi speech recognition toolkit was used to train different acoustic models. Various language models were trained using the IRSTLM software. A Bangla pronunciation dictionary was created following Indian language speech labels. The dictionary contains a sequence of phone labels corresponding to the pronunciation of words in the transcriptions of the speech data. A combination of various types of acoustics and language models was explored along with the optimal setting of some parameters. The best speech recognition system uses a subspace Gaussian mixture model in conjunction with the hidden Markov model as the acoustic model, and quadgram as the language model. The word error rate of the system using an adequately trained language model is as low as 5% in a threefold cross-validation experiment.
Fuzzy Feature Similarity Functions for Feature Clustering and Dimensionality Reduction

Dimensionality reduction is usually obtained by applying some of the most well unknown methods such as principal component analysis, singular value decomposition, feature selection algorithms which are based on information gain, Gini index etc. The objective behind achievement of dimensionality reduction is reducing computational complexity and at the same time aiming to attain better performance by learning algorithms which may perform supervised or unsupervised learning. In this paper, we present a feature clustering similarity function for dimensionality reduction so that the eventual reduced dataset may be used to reduce the computational complexity and also result better classifier evaluation results interms of accuracy, precision etc.
A Formal Approach to Secure Design of RESTful Web APIs Using SOFL

A primary concern in the design and development of a RESTful Application Programming Interfaces (APIs) is API security. A RESTful API provides data over the network using HTTP and must not violate any of its security properties. When APIs are designed, the functional and security properties are inextricably linked thus security requirements of an API cannot be treated as afterthoughts. We therefore propose an approach to specifying and verifying APIs functional and security requirements with the practical formal method SOFL (Structured-Object-oriented Formal Language). We convert an API specification written in an API description language into SOFL while expressing security requirements as constraints on the APIs functional requirements and dataflow between the API’s trust boundaries. The verification of the specifications can be carried out using specification-based conformance testing. We apply this approach to a model of an online banking API as a case study using Django REST Framework and analyze its results.
Artifact: A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

This document provides the details and guidelines to run the federated learning experiments used in the study “A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison” [1]. The artifact is available in a public GitHub repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> PerCom2021-FL - https://github.com/getalp/PerCom2021-FL. Note that The centralized/local training approaches are not included in the repository.
Scaffolds and Nudges: A Case Study in Learning Engineering Design Improvements

We present a brief case study of a multi-year learning engineering effort to iteratively redesign the problem-solving experience of students using the “Solving Quadratic Equations” workspace in Carnegie Learning’s MATHia intelligent tutoring system. We consider two design changes, one involving additional scaffolds for the problem-solving task and the next involving a “nudge” for learners to more rapidly and readily engage with these scaffolds and discuss resulting changes in the relative proportion of students who fail to master skills associated with this workspace over the course of two school years.