Reviewerly/exHarmony huggingface dataset:
Enterprise risk management: how do firms integrate cyber risk?

Purpose The purpose of this study is to examine how companies integrate cyber risk into their enterprise risk management practices. Data breaches have become commonplace, with thousands occurring each year, and some costing hundreds of millions of dollars. Consequently, cyber risk has become one of the gravest risks facing organizations, and has attracted boardroom-level attention. On the other hand, companies already manage many kinds of difficult and growing risks, and that firms lose less than 1% of annual revenues as a result of cyber incidents. Therefore, how should firms appropriately address cyber risk? Is it indeed a materially different kind of risk area, or is it simply just one more risk that can seamlessly be integrated into existing enterprise risk management (ERM) practices? Design/methodology/approach The authors performed thematic analysis based on semi-structured interviews, with non-probabilistic, purposive sampling, to answer two main questions. First, how do firms manage enterprise risks, generally? And second, how are they integrating cyber risk into these existing processes? Findings The authors find that there is considerable variation in the approach and sophistication in ERM practices, such as whether they are driven more like an auditing function, or as a risk champion. The authors also find that despite the novelty of cyber risk, it can be integrated like other enterprise risks, and that cyber risk is most often seen as an operational risk (similar to workplace accidents or fraud), rather than a strategic risk, emerging from, for example, technology innovation and R&amp;D. Research limitations/implications The generalization of the results is limited by the sample size and variation of firms interviewed. While the authors attempted to interview enterprise risk managers across a wide variation of firms, there were clear limitations in the scope. That being said, the authors were fortunate to be able to examine ERM and cyber risk practices across small and large, private and publicly traded companies, from a variety of business sectors. Practical implications The authors believe these finding are important because they present evidence that while cyber risk may be new, it does not require specialized handling or processes to track it at the enterprise level. While some firms may choose to provide special accommodations or attention because of their data collection or business practices, this approach is neither necessary nor required of all firms in all situations. Originality/value This research is one of the only papers that, to the best of the authors’ knowledge, examines how cyber risk is integrated at an enterprise level.
Vision-based Real Time Bangla Sign Language Recognition System Using MediaPipe Holistic and LSTM

The Bangla Sign Language Detection System converts the Bangla sign language into text so that deaf-mute people can communicate with ordinary people. Deaf-mute people are quite detached from society because normal individuals and deaf- mute individuals have a communication gap. On the strength of technological welfare, it is now possible to capture any deaf- mute person’s gesture, and with the help of machine learning, it can be converted into text. In this research, we adopt a development model for recognizing gestures that accommodates MediaPipe for extracting hands and posing landmarks and long short-term memory (LSTM) to train and recognize the gesture. This will convert Bangla sign language gestures into readable text. The requirements analysis served as the foundation for our proposed model, which will be carried out in four stages: collecting data and processing it, training and testing the chosen neural network, and finally, real-time testing. A gesture model is taught to recognize gestures with the help of a self-created dataset of Bangla sign language. The trained model successfully identifies the gesture, and the text equivalent of the gesture is displayed on screen. The purpose of this model is to help create a medium to communicate between normal people and the hearing impaired.
Investigating AI Languages’ Ability to Solve Undergraduate Finance Problems

The rapid advancement of artificial intelligence (AI) has given rise to sophisticated language models that excel in understanding and generating human-like text. With the capacity to process vast amounts of information, these models effectively tackle problems across diverse domains. In this paper, we present a comparative analysis of prominent AI language models—ChatGPT and Google Bard—focusing on their ability to solve undergraduate finance problems. We find that GPT-4 significantly outperforms Bard-1.0, excelling in easy problems but struggling with complex ones. The results suggest that it is crucial to handle AI with care in order to uphold academic integrity.
Applications: Selection of Research Studies

Many studies have been developed around the blockchain, and it is obviously impossible to deal with all of them. In this final section, we would like to highlight key blockchain research topics through the viewpoint of the developed theory above. The selected articles bore the attention of the author, but are far from exhaustively reflecting the overall research, and the below could specifically be seen as examples whose underlying blockchain theory, the one developed previously in this book, is general, and can gather a priori distinct blockchain topics.
A novel vision-based scheme to levelness estimation of DLP projector

Digital Light Processing (DLP) 3D printing is implemented by projecting the image of the slice of the model on the exposure surface, and the projected image's accuracy will influence the printed object's quality. It is necessary to maintain the accuracy of the projected image. A prerequisite for high-precision printing is that the projector is horizontal. The existing methods calibrate the levelness of the projector by observing the level meter; the calibration precision cannot be guaranteed. In this paper, a vision-based scheme is proposed to evaluate the levelness of a projector and to estimate the rotation angles. Firstly, the hardware is designed to capture the projected image on the exposure surface. A pixel in the projected image is about 74*74 pixels in the captured image. Secondly, the possible variation of pixel relation in different situations is analyzed in theory. Then, the variation of pixel distance (or slope) by tilt angle is further analyzed. The corresponding variation curves are obtained by curve fitting. Finally, based on the corresponding curves, the tilt direction and angle of the projector can be estimated from the captured image. The experimental results demonstrate that the average error of the estimated tilt angle is smaller than 0.2 degrees.
Customer Satisfaction Assessment System on Transactions E-commerce Product Purchases Using Sentiment Analysis

Currently, more products appear, and various services that offer similar products make it difficult for buyers to decide to buy before seeing reviews from other users.The growth of different e-commerce platforms exacerbates this.Users spent more time choosing products on each platform with many alternative considerations, such as looking at ratings, prices, and reviews from other buyers.This study conducted the optimization process of selecting e-commerce products so that users do not have to spend a long time reading every review when they want to buy a product.This research is expected to provide a comprehensive assessment of the purchase transaction of a product from the reviews provided.The data is sourced from product reviews on e-commerce in Indonesia, which are then classified into positive, negative, and neutral sentiments.The data is divided into 10 folds of data using stratified k-fold crossvalidation, consisting of training and testing data with ratios of 90% and 10% of the total data.Our research proposed a system that implemented our modified Naive Bayes model to calculate a product's Customer Satisfaction (CSAT) score and compare it with the Google Cloud NLP model.In our model, the log prior and log-likelihood formulas are modified in the algorithm, adding the prefix "NOT_" after the negation words in the preprocessing.This doubled our model's F1 score and increased the accuracy by 32%, from 59% to 91%, when compared to the Naive Bayes algorithm without modification.
Loyalty Points Exchange System Using Blockchain

The motive behind this research is to build a blockchain-based system that allows users to manage their rewards/loyalty points, trade rewards/loyalty with other users, and offer a transparent, secure network for transactions. Customers that are loyal to a brand are rewarded with points through loyalty programmes, and after they have accumulated enough points, they can redeem them for goods or services. But there are some issues with the current system of loyalty points such as long collection, low liquidity and also fragmented system results in poor user experience. The proposed system in this paper can solve such issues by providing better security and a smooth user experience. Thus with the help of technologies such as solidity which is used to write smart contracts, and other technologies like ethereum and metamask, a loyalty system has been built.
Analysis of Sentiment on Amazon Product Reviews

Sentiment Analysis is an essential process in the field of NLP (Natural Language Processing) that includes identifying the sentiment or emotion behind a text. Natural Language Processing (NLP) has a rapidly growing subfield called sentiment analysis that aims to determine the sentiment or emotion underlying a given text. Using the TF/IDF (Term Frequency-Inverse Document Frequency) and Logical Regression techniques, In this study, we did a sentiment analysis on user reviews of products on Amazon. This study aims to assess the tone of Amazon customer reviews and give significant information into how customers see the items. A customer review dataset was acquired from kaggle and preprocessed to remove noise and extraneous information. Utilizing the TF/IDF technique, features were extracted from the preprocessed reviews. Then, these characteristics were utilised to train a Logistic Regression classifier to predict the reviews' sentiment. Standard performance indicators such as accuracy, In order to evaluate the performance of the classifier of Logistic Regression, precision, recall, & F1 score have been used.
Contrastive Time Series Anomaly Detection by Temporal Transformations

Detecting anomalies in time series data is challenging due to their complex and volatile temporal features. Some anomalies only show deviating patterns to their local context instead of the overall distribution. Additionally, the biased sample distribution between normal and abnormal classes hinders the efficient usage of the available data labels. Self-supervised approaches are practically efficient for anomaly detection, in which only normal data is used during the training. However, they often fail to detect contextual anomalies in high-dimensional time series data, while the representation learning of such complex data patterns is sub-optimal. This paper introduces ContrastAD, a novel self-supervised framework for time series anomaly detection. Specifically, we employ the contrastive learning process with anomaly-induced temporal transformations. Targeting the point and contextual anomalies appearing in time series data, we develop corresponding transformations to enforce the model to learn discrepant representations for normal and abnormal data in the latent space. With extensive experiments, we show that our approach outperforms baseline anomaly detectors on various benchmark datasets. Our empirical results indicate that ContrastAD improves anomaly detection performance on noisy and high-dimensional time series datasets, even without common repeating patterns.
Predictive Taxonomy Analytics (LASSO): Predicting Outcome Types of Cyber Breach

Abstract Cyber breaches are costly for the global economy and extensive efforts have gone into improving the cybersecurity infrastructure. There are numerous types of cyber breaches that vary greatly in terms of cause and impact, resulting in an extensive literature for individual cyber breach type. Our paper seeks to provide a general framework that can be easily applied to analyze different types of cyber breaches. Our framework is inspired by the taxonomy approach in the cybersecurity literature, where it was proposed that an effective set of taxonomy can provide a direction on supporting improved decision-making in cyber risk management and selecting relevant cybersecurity controls. Our paper extends upon the current approach by using this taxonomy to model and predict the associated breach outcomes, given the occurrence of a cyber breach. Specifically, our paper applies least absolute shrinkage and selection operator (LASSO) within a taxonomy framework. Using a proprietary database of known cyber breaches, we show that this analytical tool performs well in out-of-sample predictions and a stable model that generates consistent predictions. For each cyber breach outcome type, we also provide the list of keywords that are useful in predicting the outcome type. We envision researchers, insurers, underwriters, and cybersecurity professionals can use (or expand on) our list of keywords, or use our method to yield their own set of keywords. Practitioners who seek to mitigate their cyber risk may use these keywords as a guide towards the specific attack surfaces that might be most susceptible to the corresponding breach. Our paper lays the groundwork for researchers to better apply the taxonomy approach within cybersecurity research. We also perform regression analysis to identify industries that are most susceptible to various cyber breach events. Our results corroborate with the literature, where some industries are indeed more likely to be impacted by certain types of cyberattacks.
Designing of A Plastic Garbage Robot With Vision-Based Deep Learning Applications

To address the issue of plastic waste, a robot using deep learning technology for visual recognition to classify plastic waste has been developed. This system includes a 3DOF robot arm, a conveyor, a camera, an electrical cabinet, and a computer. The object detection component of the system is designed using transfer learning with a pre-trained YOLOv5 model to ensure the system operates in real time. Selecting the best model by evaluating and comparing the results of models trained using labeling by bounding box and polygon methods. Then, the real-world coordinates for the origin of the robot arm are determined by utilizing matrices obtained from MATLAB through chessboard images. The computer processes the data and transmits commands to the robot arm system and conveyor, which is controlled by a PLC and 3 different Servo Drivers, for object sorting on the conveyor. The best-performing model has a Precision of 92.1% and a Recall of 87.3%, and the success rate of picking up an object is 91.5%. While the experimental results indicate complete stability in inter-device connectivity, implementing it would necessitate hardware improvements to leverage its potential.
AR-Bicycle: Smart AR Component Recognition to Support Bicycle’s Second Life

The mass-product industry sees in the disposable object a solution able to provide a low-cost answer to the growing needs of consumers, feeding a consumerist circuit with negative social and environmental impact. The extension of this concept over a long time leads to the use of products composed by not durable components until they break down, then to the immediate replacement of the whole creation. Do-it-yourself, meant the ability to fix derivatives, counteracts this trend by extending the life cycle of objects, positively affecting both the environment and self-esteem. Advanced visualization technologies, such as augmented reality, can accompany and facilitate this process. Indeed, they provide tools accessible to all, thanks to smartphones, contextualizing digital info in the natural environment and expanding knowledge about the product's accommodation. This process's criticality is related to creating a 3D representation of the product to recognize the different components and associating multimedia data. The article's research aims to experiment with an Augmented Reality application to improve maintenance and repair activities anywhere and anytime on design products. The application is created and tested on the bicycle, a rapidly growing product even in large Italian cities. The purpose is to experiment with using Augmented Reality in the Industrial Product domain for fixing purposes, scaling its application to different types of products belonging to homogeneous classes.
Assessing Massive Open Online Courses for Developing Digital Competences among Higher Education Teachers

The outstanding growth in digital technologies has raised higher education teachers’ attention towards developing digital competences for effectively fulfilling students’ learning requirements and keeping up with the rapidly evolving global education system. Nowadays, higher education teachers can find a plethora of online courses provided by educational organizations, universities, and businesses for reskilling and upskilling. The objective of this paper is to provide results from the assessment of existing massive open online courses regarding their potential to cover a large range of digital competences that higher education teachers should possess. A total of one hundred and sixty-two (162) online courses from three prominent international digital platforms, offering massive open online courses, were subjected to evaluation using a model incorporating six categories of attributes aligned with pertinent digital competences. The results indicate that the majority of the existing online courses lack sufficient coverage of key digital competences required by higher education teachers. Notably, these inadequately addressed competences regard evaluating students’ performance in the digital environment and ensuring positive online student experiences through collaborative engagement and self-reflection. The findings of this study offer valuable insights not only to higher education teachers, but also to online course developers and education policy-makers. The identified gaps in digital competences underscore the pressing need for substantial improvements in existing online courses or the development of new courses to effectively bridge these competence gaps. By addressing these shortcomings, higher education institutions can better equip their teachers to navigate the digital realm and elevate the overall quality of education in the digital era.
A software vulnerability detection method based on deep learning with complex network analysis and subgraph partition

The increasing size and complexity of software programs have made them an integral part of modern society’s infrastructure, making software vulnerabilities a major threat to computer security. To address this issue, the use of deep learning-based software vulnerability detection methods has become increasingly popular. Although the effectiveness of the deep learning-based methods has been demonstrated, these methods have faced challenges in scalability and detection performance. To tackle this challenge, we propose a new vulnerability detection method based on deep learning with complex network analysis and subgraph partition that enhances detection accuracy while maintaining scalability. The method uses complex network analysis theory to convert the CPG into an image-like matrix, and then utilizes TextCNN for vulnerability detection. As a result, our method shows a 6% improvement in accuracy and a 10% reduction in false positive rates compared to state-of-the-art methods. In addition, our approach is able to detect some of the vulnerabilities recently released by CVE.
Fall Detection with Event-Based Data: A Case Study

Fall detection systems are relevant in our aging society aiming to support efforts towards reducing the impact of accidental falls. However, current solutions lack the ability to combine low-power consumption, privacy protection, low latency response, and low payload. In this work, we address this gap through a comparative analysis of the trade-off between effectiveness and energy consumption by comparing a Recurrent Spiking Neural Network (RSNN) with a Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). By leveraging two pre-existing RGB datasets and an event-camera simulator, we generated event data by converting intensity frames into event streams. Thus, we could harness the salient features of event-based data and analyze their benefits when combined with RSNNs and LSTMs. The compared approaches are evaluated on two data sets collected from a single subject; one from a camera attached to the neck (N-data) and the other one attached to the waist (W-data). Each data set contains 469 video samples, of which 213 are four types of fall examples, and the rest are nine types of non-fall daily activities. Compared to the CNN, which operates on the high-resolution RGB frames, the RSNN requires $$200\times $$ less trainable parameters. However, the CNN outperforms the RSNN by 23.7 and 17.1% points for W- and N-data, respectively. Compared to the LSTM, which operates on event-based input, the RSNN requires $$5\times $$ less trainable parameters and $$2000\times $$ less MAC operations while exhibiting a 1.9 and 8.7% points decrease in accuracy for W- and N-data, respectively. Overall, our results show that the event-based data preserves enough information to detect falls. Our work paves the way to the realization of high-energy efficient fall detection systems.
Low-Voltage Electrical Product Quality Problem-Solving Based on Improved Deep Structured Semantic Model

This paper focuses on the issue of low-efficient product quality problem-solving in low-voltage electrical products manufacturing, where the analysis results are easily prone to errors. Defect product features have many varieties and complex relationships. A large number of historical defect cases have been accumulated, however, these defects and their root causes are described in a Chinese-English-mixed manner, containing domain-specific words as well. Inconsistent wording also exists. Therefore, historical defect cases have not been effectively utilized to support quality problem-solving. To address this issue, we propose an improved Deep Structured Semantic Model (DSSM)-based quality problem-solving method. It uses word embedding algorithms to represent the defect description and defect cause, and parallelly uses graph embedding algorithms to represent the constructed product feature knowledge graph from product feature. The resultant word embedding vectors and graph embedding vectors will then be fused as feature embedding to support the root cause analysis based on the improved DSSM. An industrial case study has been conducted to validate the proposed method.
Incidence of the use of digital tools as a teaching strategy at the unified general baccalaureate level of the Ecuadorian system

The present study focuses on analyzing the use of digital tools as a pedagogical strategy at the high school level in Ecuador. A methodology with a theoretical approach and a descriptive-explanatory nature was applied, field research and the application of surveys were carried out. Reflective reliability analysis Cronbach's alpha was 0.875 and McDonald's Wwas 0.901. The evaluation statistics show an association between the variables of 0.61, 0.76 and 085. A trend is determined towards the adoption of technology and the use of digital educational tools and software, with the mobile phone being the device most used by both teachers and students. . . The existing digital divide between these two groups stands out, with teachers qualified as digital migrants and students as digital natives, the lack of training and the low availability of resources reflects a challenge to achieve the effective integration of technology in the teaching of the Ecuadorian unified general high school.