Reviewerly/exHarmony huggingface dataset:
The Application of Text Mining in Detecting Financial Fraud: A Literature Review

Numerous researchers have investigated text-mining techniques to detect financial fraud. The purpose of this chapter is to review research studies on detecting financial fraud using text-mining techniques and to communicate the current state of the art to industry practitioners and academic scholars. The qualitative information contained in a company’s financial statements provides hidden clues for detecting financial fraud. Text mining is a technique for extracting useful information that can be used for decision-making and forecasting. The methodology included an analysis of 45 research studies and conference proceedings using a variety of keyword combinations. The majority of studies were obtained from a well-known online database. The study’s review can assist organisations in selecting the most appropriate technique in terms of accuracy, frequency, and performance.
APF-RL: Safe Mapless Navigation in Unknown Environments

This paper is focused on safe mapless navigation of mobile robots in unknown and possibly complex environments containing both internal and dynamic obstacles. We present a novel modular approach that combines the strengths of artificial potential functions (APF) with deep reinforcement learning. Differing from related work, the robot learns how to adjust the two input parameters of the APF controller as necessary through soft actor-critic algorithm. Environmental complexity measures are introduced in order to ensure that the robot's training covers a range of learning scenarios that vary in regard to maneuvering difficulty. Our experimental results show that differing from the classical navigation methods and end-to-end models, the robot can navigate successfully on its own even in complex scenarios with moving entities without requiring any maps.
Ontological Foundations for Trust Dynamics: The Case of Central Bank Digital Currency Ecosystems

In recent years, disruptive technologies have advanced at a rapid pace. These new developments have the power to accelerate the production and delivery, improve the quality, and reduce the costs of goods and services, as well as to contribute to individual and collective well-being. However, their adoption relies largely on user trust. And trust, due to its dynamic nature, is fragile. Therefore, just as important as to build trust is to maintain it. To build sustainable trust it is fundamental to understand the composition of trust relations and what factors can influence them. To address this issue, in this paper, we provide ontological foundations for trust dynamics. We extend our previous work, the Reference Ontology of Trust (ROT), to clarify and provide a deeper account of some building blocks of trust as well as the many factors that can influence trust relations. We illustrate the working of ROT by applying it to a real case study concerning citizens' trust in central bank digital currency ecosystems, which has been conducted in close collaboration with a national central bank.
“splink” is happy and “phrouth” is scary: Emotion Intensity Analysis for Nonsense Words

People associate affective meanings to words -"death" is scary and sad while "party" is connotated with surprise and joy.This raises the question if the association is purely a product of the learned affective imports inherent to semantic meanings, or is also an effect of other features of words, e.g., morphological and phonological patterns.We approach this question with an annotation-based analysis leveraging nonsense words.Specifically, we conduct a best-worst scaling crowdsourcing study in which participants assign intensity scores for joy, sadness, anger, disgust, fear, and surprise to 272 nonsense words and, for comparison of the results to previous work, to 68 real words.Based on this resource, we develop character-level and phonology-based intensity regressors.We evaluate them on both nonsense words and real words (making use of the NRC emotion intensity lexicon of 7493 words), across six emotion categories.The analysis of our data reveals that some phonetic patterns show clear differences between emotion intensities.For instance, s as a first phoneme contributes to joy, sh to surprise, p as last phoneme more to disgust than to anger and fear.In the modelling experiments, a regressor trained on real words from the NRC emotion intensity lexicon shows a higher performance (r = 0.17) than regressors that aim at learning the emotion connotation purely from nonsense words.We conclude that humans do associate affective meaning to words based on surface patterns, but also based on similarities to existing words ("juy" to "joy", or "flike" to "like").
A Biometrics-Based Behavioral Trust Framework for Continuous Mobile Crowd Sensing Recruitment

The emergence of mobile crowd sensing (MCS) platforms makes it possible to collect data in a time and cost efficient manner. However, one of the challenges in MCS systems is obtaining reliable information especially in the presence of impersonators who could provide false reports without getting detected. User recruitment mechanisms adopted in MCS systems hire users such that the Quality of Information (QoI) of the submitted sensing reports is maximized. Despite that, there is still a risk of recruiting impersonators to the system. This problem is even more prominent in the case of continuous mobile sensing tasks, since multiple false sensing could be submitted by the impersonator during the sensing period, which can impact the quality of the sensing outcome and hence the performance of the system. Therefore, to ensure the reliability of the continuously submitted data, a more robust recruitment mechanism, which detects and eliminates impersonators during the sensing task, is needed. This work proposes a biometrics-based behavioral trust framework that can support a reliable recruitment process in continuous MCS tasks. Behavioral biometrics are unique behavioral traits that can be used to profile users based on how they naturally perform a specific activity. By leveraging machine learning techniques, these behavioral traits can be used in order to detect impersonators in the system. In this work, a unique model for each MCS worker is built based on their unique interaction patterns with the smartphone&#x2019;s touching screen. The proposed approach integrates the trained machine learning models with a dynamic continuous recruitment system, which continuously monitors the QoI of the submitted sensing reports and changes the recruited participants as needed. Simulation results of the proposed approach show its efficacy in detecting and eliminating impersonators in continuous sensing recruitment.
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation

Our paper studies the continual learning (CL) problems in which data comes in sequence and the trained models are expected to be capable of utilizing existing knowledge to solve new tasks without losing performance on previous ones. This also poses a central difficulty in the field of CL, termed as Catastrophic Forgetting (CF). In an attempt to address this problem, Bayesian methods provide a powerful principle, focusing on the inference scheme to estimate the importance of weights. Variational inference (VI), one of the most widely used methods within this vein, approximates the intractable posterior by a factorized distribution, thus offering computational efficiency. Notwithstanding many state-of-the-art performances in practice, this simple assumption about the posterior distribution typically limits the model capacity to some extent. In this paper, we introduce a novel approach to mitigate forgetting in the Bayesian approach via enriching the posterior distribution with mixture models, which intuitively promotes neural networks to acquire knowledge from multiple tasks at a time. Moreover, in order to reduce the model’s complexity growth when the number of components increases, we propose a solution that conducts low-rank decomposition on the variance of each component based on neural matrix factorization. Extensive experiments show that our method yields significant improvements compared to prior works on different benchmarks.
SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis

In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations.Currently, models have been trained for five domains and one general domain and are implemented in a pipeline approach, where the output of one model serves as the input for the next.The results are presented in three interactive dashboards, allowing companies to gain more insights into what stakeholders think of their products and services.The SentEMO platform is available at https://sentemo.ugent.be/ 1 .
Reporting the Application of User Experience Tools and Proxy Users in an Industrial Process Based on Double Diamond

User experience research techniques and tools play a central role in the design of digital solutions. This paper reports the use of proxy users as a source of information about the end users in the discovery, definition and development stages as part of the Double Diamond design process, which offers a structure that diverges and converges between its stages, promoting flexibility to suit the project. The design process of an administrative tool as part of a mobile application to digital commerce is described and discussed. With the support of proxy users and the adoption of design tools such as Service Blueprint, User Journey, Proto-personas and Brainwriting, we arrived at the definition of the initial requirements for this administrative tool. At the end of the process, it was possible to recognize that the application of proxy users in common artifacts for User Experience research enabled the discovery of information about the end users from intermediaries. Unlike a process with direct contact with them, in this case, there was a need to carry out more steps to confirm the data since the user proxy may not represent the real expectations and needs of the end users.
Discontinuous Constituency and BERT: A Case Study of Dutch

In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch.We devise a test suite based on a mildly context-sensitive formalism, from which we derive grammars that capture the linguistic phenomena of control verb nesting and verb raising.The grammars, paired with a small lexicon, provide us with a large collection of naturalistic utterances, annotated with verb-subject pairings, that serve as the evaluation test bed for an attention-based span selection probe.Our results, backed by extensive analysis, suggest that the models investigated fail in the implicit acquisition of the dependencies examined.
Social Media-Based Analysis of Bus Service Satisfaction

People post their emotional expressions about specific events through social media platforms, such as praise and complaints about city bus travel services. In this paper, we propose a social media-based method to analyze bus service satisfaction. Firstly, we collect microblogs related to bus service from Sina Weibo, and pre-process the collected microblogs. Then the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model is combined with Bi-directional Long Short-Term Memory (BiLSTM) model to form BERT- BiLSTM model, using the pre-processed data to train the BERT-BiLSTM model to realize the classification of text data into positive and negative sentiments, and finally using the method of this paper to analyze and verify the satisfaction of Guangzhou bus services system in 2010. Comparing the method of this paper with traditional machine learning and deep learning methods, the experiments show the effectiveness of the BERT-BiLSTM model.
Fuzzy-Chaotic Variant of the Multiverse Optimizer Algorithm in Benchmark Function Optimization

In this work we present a new variation of the Multiverse Optimizer using Fuzzy logic and chaos theory (FCMVO) for optimization of benchmark mathematical functions. Over this study, we present some variations in Mamdani and Sugeno approximations in conjunction with some of the most used chaotic maps in the literature; like the Logistic map, Chebyshev map and Gauss map. By using chaos theory, we are substituting some of the random parameters for the MVO algorithm, and Fuzzy logic is used for dynamic parameter adaptation to substitute some equations of the MVO algorithm. The main tests for the variations presented are done with 13 benchmark mathematical functions, where we compare the original MVO in these functions, the comparison against a Chaotic MVO with 10 different chaotic maps, and the Fuzzy-Chaotic MVO with the same functions, so we can observe the improvement. In this study, we have the objective to determine if the combination of Fuzzy Logic and Chaos theory have significant improvement over the original MVO or its single variations, so we can proceed to further testing over control problems.
Spelling Correction using Phonetics in E-commerce Search

In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries.However, resolving phonetic errors is a critical but overlooked area.The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonetheless inaccurate in spelling (e.g., "bluetooth sound system" vs. "blutut sant sistam") with numerous noisy forms and sparse occurrences.In this work, we propose a generalized spelling correction system integrating phonetics to address phonetic errors in E-commerce search without additional latency cost.Using India (IN) E-commerce market for illustration, the experiment shows that our proposed phonetic solution significantly improves the F1 score by 9%+ and recall of phonetic errors by 8%+.This phonetic spelling correction system has been deployed to production, currently serving hundreds of millions of customers.
Teaching Mental Activity of the New Generation and Basic Pedagogical Competences

The article analyzes changes in the practice of schooling associated with the transition to forced distance learning in the context of a pandemic and the ongoing digitalization of education. These changes make it possible to discover an extremely poor arsenal of pedagogical competencies in the work of a teacher in the upper grades of secondary school. The teacher of today's school is not able to acquaint students with the basic principles of the initial forms of research and project activity, ensure that students master the methods of action of these different types of activity and connect the development of these methods of action with subject-disciplinary education. At the same time, in the system of additional education, in particular, during the NTI1 Olympiad, teams of students successfully master the principles of operation of complex equipment and perform engineering tasks. These data indicate that high school students are capable of much more intensive development of design and experimental-search thinking when solving practical problems in joint activities. In order to organize the processes of mastering project and research types of activity by schoolchildren in the upper grades of secondary school using digital models, the teacher should form a new generation of teaching mental activity based on a different system of pedagogical competencies. The article proposes a generalized description of the most important pedagogical competencies that ensure the organization of activity-based education in high school.
Learning Disentangled Representations of Negation and Uncertainty

Negation and uncertainty modeling are longstanding tasks in natural language processing.Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify.However, previous works on representation learning do not explicitly model this independence.We therefore attempt to disentangle the representations of negation, uncertainty, and content using a Variational Autoencoder 1 .We find that simply supervising the latent representations results in good disentanglement, but auxiliary objectives based on adversarial learning and mutual information minimization can provide additional disentanglement gains.
Multicommunity Graph Convolution Networks with Decision Fusion for Personalized Recommendation

Most graph-based recommendation approaches actually have made an implicit assumption about that representations of all users and all items can be learned in a single latent space. However, this assumption may be too strong to well describe a single use's multifaceted preferences each probably dominated by some latent type of motivations. This paper challenges this assumption and proposes a MultiGCN model (Multicommunity Graph Convolution Networks with Decision Fusion) to leverage multiple latent spaces for capturing multiple types of motivation. Specifically, we first design a community exploration module to construct multiple communities so as to explore different latent types of motivation. We next design a local recommendation module which maps the representations of entities in each community into one latent space and outputs a local recommendation list. A decision fusion module reranks the items of local lists to obtain the final recommendation list. Experiment results on three real-world datasets demonstrate that our MultiGCN outperforms the state-of-the-art algorithms.
Using blockchain technology for recruitment effectiveness in industry 4.0

In this Industry 4.0, with the growing technology and the cyber risk is growing and technology loses its trust, and there comes the concept of Blockchain. Blockchain-based on a distributed digital ledger technology, which helps to create a smart, secure and transparent system. And in Industry 4.0 it becomes a need for any organization to adapt blockchainbased technology in Human Resource functions to ensure a transparent and secure environment. The blockchain-based technology can help to find the right candidate by identifying skills, knowledge, and experience, and as a result, it can boost the productivity of the business very effectively. Blockchain will help fill the business gap, which brings great results to HR and its employees. There is a feeling that technology has dissolve trust in current years, due to rise of cyber threats. So, now blockchain is developing a way to use technology to regain trust. HR purposes and responsibilities need to start putting the blockchain next to other arising technologies in developing their digital strategy. Exploring the potential blockchain to improve efficiency and effectiveness should be considered along with the broader implications for future work. This research paper develops the idea that Blockchain-based technology improves recruitment and selection processes compared to the existing recruitment system and helps in employer evaluation.
End-to-End LSTM-Based Earthquake Magnitude Estimation With a Single Station

In this paper, a method based on Long Short-Term Memory is presented to address the problem of earthquake magnitude estimation for earthquake early warning (EEW) and tsunami early warning (TW) purposes using a seismic station. An end-to-end based scheme is adopted, and a particular attention is paid to compute the magnitude of seismic events larger than M6 and reduce the effective time for TW. More so, these earthquakes are the ones that cause more fear or uncertainty in the population with provision of the most significant destructive potential. However, the occurrence of large earthquakes is low, but to counteract the drawback of limited training data, engineered features were also proposed. The earthquake magnitude relative error estimation reported here in experiments with Chilean seismic data was 4.01% and 8.04% with earthquakes M4.0 or larger (up to M8.1) and M4.0 or smaller, respectively, by employing seismic traces in the nearest station to the corresponding seismic event. The average earthquake-nearest station distance was 196 km and in 26% of the data this distance was greater than 200 km. These results are competitive with those published elsewhere and suggest the possibility to reduce the time required for EEW and specially TW.
Binary Exploitation in Industrial Control Systems: Past, Present and Future

Despite being a decades-old problem, binary exploitation still remains a serious issue in computer security.It is mainly due to the prevalence of memory corruption errors in programs written with notoriously unsafe but yet indispensable programming languages like C and C++.For the past 30 years, the nip-and-tuck battle in memory between attackers and defenders has been getting more technical, versatile, and automated.With raised bar for exploitation in common information technology (IT) systems owing to hardened mitigation techniques, and with unintentionally opened doors into industrial control systems (ICS) due to the proliferation of industrial internet of things (IIoT), we argue that we will see an increased number of cyber attacks leveraging binary exploitation on ICS in the near future.However, while this topic generates a very rich and abundant body of research in common IT systems, there is a lack of systematic study targeting this topic in ICS.The present work aims at filling this gap and serves as a comprehensive walkthrough of binary exploitation in ICS.Apart from providing an analysis of the past cyber attacks leveraging binary exploitation on ICS and the ongoing attack surface transition, we give a review of the attack techniques and mitigation techniques on both general-purpose computers and embedded devices.At the end, we conclude this work by stressing the importance of network-based intrusion detection, considering the dominance of resource-constrained real-time embedded devices, low-end embedded devices in ICS, and the limited ability to deploy arbitrary defense mechanism directly on these devices.
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples

Most of the existing defense methods improve the adversarial robustness by making the models adapt to the training set augmented with some adversarial examples.However, the augmented adversarial examples may not be natural, which might distort the training distribution, resulting in inferior performance both in clean accuracy and adversarial robustness.In this study, we explore the feasibility of introducing a reweighting mechanism to calibrate the training distribution to obtain robust models.We propose to train text classifiers by a sample reweighting method in which the example weights are learned to minimize the loss of a validation set mixed with the clean examples and their adversarial ones in an online learning manner.Through extensive experiments, we show that there exists a reweighting mechanism to make the models more robust against adversarial attacks without the need to craft the adversarial examples for the entire training set.
Proactive Buildings: A Prescriptive Maintenance Approach

Prescriptive maintenance has recently attracted a lot of scientific attention. It integrates the advantages of descriptive and predictive analytics to automate the process of detecting non nominal device functionality. Implementing such proactive measures in home or industrial settings may improve equipment dependability and minimize operational expenses. There are several techniques for prescriptive maintenance in diverse use cases, but none elaborates on a general methodology that permits successful prescriptive analysis for small size industrial or residential settings. This study reports on prescriptive analytics, while assessing recent research efforts on multi-domain prescriptive maintenance. Given the existing state of the art, the main contribution of this work is to propose a broad framework for prescriptive maintenance that may be interpreted as a high-level approach for enabling proactive buildings.