Reviewerly/exHarmony huggingface dataset:
Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs

Turkish small-and medium-sized enterprises (SMEs) are exposed to fraud risks and creditor banks are facing big challenges to deal with financial accounting fraud.This study explores effectiveness of machine learning classifiers in detecting financial accounting fraud assessing financial statements of 341 Turkish SMEs from 2013 to 2017.The data are obtained from one of the leading creditor banks of Turkey.Highly imbalanced classes of 1384 nonfraudulent cases and 321 fraudulent cases (by 122 firms) are detected thus sampling techniques are used to mitigate class imbalance problem.Research methodology consists of two stages.First stage is data preprocessing wherein financial ratio calculation, feature selection methods for defining financial ratios with the greatest impact on fraudulent financial statements and two sampling methods of Synthetic Minority Oversampling Technique (SMOTE) as oversampling and undersampling are performed, respectively.Second stage is performance evaluation and comparison of classifiers wherein seven different classifiers (support vector machine, Naive Bayes, artificial neural network, K-nearest neighbor, random forest, logistic regression, and bagging) are executed and compared by using performance metrics.Classifiers are also compared without using any feature selection and/or sampling techniques.Results reveal that random forestwithout feature selection-oversampling model outperforms all other models.
A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.
Introducing augmented reality in early childhood literacy learning

Augmented reality (AR) as an emerging technology has gradually been incorporated into educational contexts; however, the cases that incorporate AR into early childhood contexts are underrepresented and especially scant in the literacy domain. Aiming to measure the impact of AR on early childhood learning and motivation in the literacy domain, this study brought an application into six pre-kindergarten classrooms by introducing three experimental classrooms to an AR centre while others engaged with a two-dimensional (2D) version of the same material. Bayesian analysis revealed that rapid letter naming rates grew for all children involved in the study. It increased by 6.28% among children in the experimental AR group and 3.35% in the control 2D group. Growth in rates of motivation was similar among experimental (11.5%) and control (10.9%) groups. These findings suggest that three-dimensional images of letters might help with rapid letter naming skills, and animations available in both versions may be the reason of increases in motivation. Teacher interviews presented positive views towards AR, and instructional implications were provided by teachers for incorporating the technology into early childhood classrooms.
ROAD: The Road Event Awareness Dataset for Autonomous Driving

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.
Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight has critical limitations. Among those limitations, visual focus is one of the most critical when dealing with surveillance. For example, in a surveillance room, a crime can occur in a different screen segment or on a distinct monitor, and the surveillance staff may overlook it. Our proposal focuses on shoplifting crimes by analyzing situations that an average person will consider as typical conditions, but may eventually lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior—the one that may occur before the build-up phase of a crime—by detecting precise segments of a video with a high probability of containing a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily action and shoplifting samples. The results are encouraging as the model correctly classifies suspicious behavior in most of the scenarios where it was tested. For example, when classifying suspicious behavior, the best model generated in this work obtains precision and recall values of 0.8571 and 1 in one of the test scenarios, respectively.
COVID-19 and Cybersecurity: Finally, an Opportunity to Disrupt?

COVID-19 has challenged cybersecurity to meet the ultimate need of guaranteeing the privacy and security of human beings. Although personal and sensitive health data are needed to better understand, detect, and control the disease, many related cybersecurity challenges and vulnerabilities require further analysis and proper discussion. The aims of this viewpoint are to explore the consequences of COVID-19 on cybersecurity and health care as well as to foster awareness regarding the need for a change in paradigm on how cybersecurity is approached. Education and information technology literacy are important when they are suitably provided; however, they are certainly not a complete solution. Disruption needs to occur at the core of human-device interactions. Building trust, providing novel means to interact with technology (eg, digital humans), and supporting people—the most important cybersecurity asset—are only some of the recommendations for a more human and resilient approach to cybersecurity, during or after the pandemic.
Challenges for Education during the Pandemic: An Overview of Literature

This article overviews studies exploring the COVID‑19 pandemic’s impact on education systems and their responses to lockdown restrictions, comparing available findings with international statistics based on continuous education system monitoring. Global organizations acknowledge disruption of classical educational processes and emergency transition to distance learning during the pandemic. Scientific literature examines accessibility of online education, alternative forms of distance learning, and the pandemic-induced financial constraints on universities inhibiting new construction, social support for students, scholarship application, professional development of faculty members, and research growth. The pandemic illuminated the issue of inequality in education, which worsened as a result of emergency transition to online studies. In particular, researchers focus on the most vulnerable groups of students, such as children from low-income families, children from migrant backgrounds, and students with disabilities.Projects aimed at studying the digitalization of education account for the biggest chunk of research inspired by the new pandemic reality. A number of studies discuss not just a formal transition to distance learning but a major technological turn that allows using the unique opportunities provided by digital technologies, which is especially important when teaching medical students.Theoretical inquiry is a distinctive feature of scientific discourse, as compared to the discourse of international expert and analytical reports on the problems of education in the context of the COVID‑19 pandemic. Research on changes to the learning process makes it possible to reconstruct the direct and indirect, as well as latent, threats of the pandemic.
A Novel Performance Measure for Machine Learning Classification

Machine learning models have been widely used in numerous classification problems and performance measures play a critical role in machine learning model development, selection, and evaluation. This paper covers a comprehensive overview of performance measures in machine learning classification. Besides, we proposed a framework to construct a novel evaluation metric that is based on the voting results of three performance measures, each of which has strengths and limitations. The new metric can be proved better than accuracy in terms of consistency and discriminancy.
Safe systems programming in Rust

The promise and the challenges of the first industry-supported language to master the trade-off between safety and control.
New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition

For the effective application of thriving human-assistive technologies in healthcare services and human–robot collaborative tasks, computing devices must be aware of human movements. Developing a reliable real-time activity recognition method for the continuous and smooth operation of such smart devices is imperative. To achieve this, light and intelligent methods that use ubiquitous sensors are pivotal. In this study, with the correlation of time series data in mind, a new method of data structuring for deeper feature extraction is introduced herein. The activity data were collected using a smartphone with the help of an exclusively developed iOS application. Data from eight activities were shaped into single and double-channels to extract deep temporal and spatial features of the signals. In addition to the time domain, raw data were represented via the Fourier and wavelet domains. Among the several neural network models used to fit the deep-learning classification of the activities, a convolutional neural network with a double-channeled time-domain input performed well. This method was further evaluated using other public datasets, and better performance was obtained. The practicability of the trained model was finally tested on a computer and a smartphone in real-time, where it demonstrated promising results.
BERTese: Learning to Speak to BERT

Large pre-trained language models have been shown to encode large amounts of world and commonsense knowledge in their parameters, leading to substantial interest in methods for extracting that knowledge.In past work, knowledge was extracted by taking manuallyauthored queries and gathering paraphrases for them using a separate pipeline.In this work, we propose a method for automatically rewriting queries into "BERTese", a paraphrase query that is directly optimized towards better knowledge extraction.To encourage meaningful rewrites, we add auxiliary loss functions that encourage the query to correspond to actual language tokens.We empirically show our approach outperforms competing baselines, obviating the need for complex pipelines.Moreover, BERTese provides some insight into the type of language that helps language models perform knowledge extraction.
A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).
Masked Accelerators and Instruction Set Extensions for Post-Quantum Cryptography

Side-channel attacks can break mathematically secure cryptographic systems leading to a major concern in applied cryptography. While the cryptanalysis and security evaluation of Post-Quantum Cryptography (PQC) have already received an increasing research effort, a cost analysis of efficient side-channel countermeasures is still lacking. In this work, we propose a masked HW/SW codesign of the NIST PQC finalists Kyber and Saber, suitable for their different characteristics. Among others, we present a novel masked ciphertext compression algorithm for non-power-of-two moduli. To accelerate linear performance bottlenecks, we developed a generic Number Theoretic Transform (NTT) multiplier, which, in contrast to previously published accelerators, is also efficient and suitable for schemes not based on NTT. For the critical non-linear operations, masked HW accelerators were developed, allowing a secure execution using RISC-V instruction set extensions. With the proposed design, we achieved a cycle count of K:214k/E:298k/D:313k for Kyber and K:233k/E:312k/D:351k for Saber with NIST Level III parameter sets. For the same parameter sets, the masking overhead for the first-order secure decapsulation operation including randomness generation is a factor of 4.48 for Kyber (D:1403k)and 2.60 for Saber (D:915k).
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning

While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.In this paper, we formulate the zeroshot relation extraction problem by incorporating the text description of seen and unseen relations.We propose a novel multi-task learning model, zero-shot BERT (ZS-BERT), to directly predict unseen relations without handcrafted attribute labeling and multiple pairwise classifications.Given training instances consisting of input sentences and the descriptions of their relations, ZS-BERT learns two functions that project sentences and relation descriptions into an embedding space by jointly minimizing the distances between them and classifying seen relations.By generating the embeddings of unseen relations and newcoming sentences based on such two functions, we use nearest neighbor search to obtain the prediction of unseen relations.Experiments conducted on two well-known datasets exhibit that ZS-BERT can outperform existing methods by at least 13.54% improvement on F1 score.
From Label Smoothing to Label Relaxation

Regularization of (deep) learning models can be realized at the model, loss, or data level. As a technique somewhere in-between loss and data, label smoothing turns deterministic class labels into probability distributions, for example by uniformly distributing a certain part of the probability mass over all classes. A predictive model is then trained on these distributions as targets, using cross-entropy as loss function. While this method has shown improved performance compared to non-smoothed cross-entropy, we argue that the use of a smoothed though still precise probability distribution as a target can be questioned from a theoretical perspective. As an alternative, we propose a generalized technique called label relaxation, in which the target is a set of probabilities represented in terms of an upper probability distribution. This leads to a genuine relaxation of the target instead of a distortion, thereby reducing the risk of incorporating an undesirable bias in the learning process. Methodically, label relaxation leads to the minimization of a novel type of loss function, for which we propose a suitable closed-form expression for model optimization. The effectiveness of the approach is demonstrated in an empirical study on image data.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling

Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.However, the state-of-the-art joint models heavily rely on autoregressive approaches, resulting in two issues: slow inference speed and information leakage.In this paper, we explore a non-autoregressive model for joint multiple intent detection and slot filling, achieving more fast and accurate.Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware graph interaction layer is proposed to model slot dependency for alleviating uncoordinated slots problem while a global intentslot graph interaction layer is introduced to model the interaction between multiple intents and all slots in the utterance.Experimental results on two public datasets show that our framework achieves state-of-the-art performance while being 11.5 times faster.
The good, the bad and the missing: A Narrative review of cyber-security implications for australian small businesses

Small businesses (0–19 employees) are becoming attractive targets for cyber-criminals, but struggle to implement cyber-security measures that large businesses routinely deploy. There is an urgent need for effective and suitable cyber-security solutions for small businesses as they employ a significant proportion of the workforce. In this paper, we consider the small business cyber-security challenges not currently addressed by research or products, contextualised via an Australian lens. We also highlight some unique characteristics of small businesses conducive to cyber-security actions. Small business cyber-security discussions to date have been narrow in focus and lack re-usability beyond specific circumstances. Our study uses global evidence from industry, government and research communities across multiple disciplines. We explore the technical and non-technical factors negatively impacting a small business’ ability to safeguard itself, such as resource constraints, organisational process maturity, and legal structures. Our research shows that some small business characteristics, such as agility, large cohort size, and piecemeal IT architecture, could allow for increased cyber-security. We conclude that there is a gap in current research in small business cyber-security. In addition, legal and policy work are needed to help small businesses become cyber-resilient.
Meta-Learning to Compositionally Generalize

Natural language is compositional; the meaning of a sentence is a function of the meaning of its parts.This property allows humans to create and interpret novel sentences, generalizing robustly outside their prior experience.Neural networks have been shown to struggle with this kind of generalization, in particular performing poorly on tasks designed to assess compositional generalization (i.e.where training and testing distributions differ in ways that would be trivial for a compositional strategy to resolve).Their poor performance on these tasks may in part be due to the nature of supervised learning which assumes training and testing data to be drawn from the same distribution.We implement a meta-learning augmented version of supervised learning whose objective directly optimizes for out-of-distribution generalization.We construct pairs of tasks for meta-learning by sub-sampling existing training data.Each pair of tasks is constructed to contain relevant examples, as determined by a similarity metric, in an effort to inhibit models from memorizing their input.Experimental results on the COGS and SCAN datasets show that our similaritydriven meta-learning can improve generalization performance.
Multi-View Information-Bottleneck Representation Learning

In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised multi-view representation learning model termed Collaborative Multi-View Information Bottleneck Networks (CMIB-Nets), which comprehensively explores the common latent structure and the view-specific intrinsic information, and discards the superfluous information in the data significantly improving the generalization capability of the model. Specifically, our proposed model relies on the information bottleneck principle to integrate the shared representation among different views and the view-specific representation of each view, prompting the multi-view complete representation and flexibly balancing the complementarity and consistency among multiple views. We conduct extensive experiments (including clustering analysis, robustness experiment, and ablation study) on real-world datasets, which empirically show promising generalization ability and robustness compared to state-of-the-arts.