Reviewerly/exHarmony huggingface dataset:
In employees we Trust: Employee fraud in small businesses

Abstract This paper examines how and why employees used online computer access to commit fraud in New Zealand small businesses. Drawing on data from 18 court documents between 2006 and 2020, we use document analysis to examine the pressure, opportunity, rationalization, and capability elements using the fraud diamond framework. We provide three major findings. First, the employee frauds were motivated by vice and family circumstances. The combination of opportunity and capability had a devastating effect on the length of the fraud and the amount of financial loss. Second, the frauds were mostly perpetrated by middle-aged women in both managerial and nonmanagerial positions who displayed unusual behaviour but had no prior convictions. Third, small businesses are vulnerable to fraud in their billing, accounts payable, and payroll systems; thus, relevant prevention strategies are recommended. Overall, we conclude that the tendency for fraud is heightened in small businesses where trusted employees: have multiple responsibilities; have an occupational position that provides them with opportunity; are capable of manipulating online access; and have external pressures of addictions or adverse family circumstances. Our multiple cases approach facilitates a better understanding of the employee fraud contexts, including the motivation and the methods employed to commit such fraud in New Zealand.
ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples

An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.As an emerging defense method to defend against adversarial examples, generative adversarial networks-based defense methods have recently been studied.However, the performance of the state-of-the-art generative adversarial networks-based defense methods is limited because the target deep neural network models with generative adversarial networks-based defense methods are robust against adversarial examples but make a false decision for legitimate input data.To solve the accuracy degradation of the generative adversarial networksbased defense methods for legitimate input data, we propose a new generative adversarial networks-based defense method, which is called Adversarially Robust Generative Adversarial Networks(ARGAN).While converting input data to machine learning models using the two-step transformation architecture, ARGAN learns the generator model to reflect the vulnerability of the target deep neural network model against adversarial examples and optimizes parameter values of the generator model for a joint loss function.From the experimental results under various datasets collected from diverse applications, we show that the accuracy of ARGAN for legitimate input data is good-enough while keeping the target deep neural network model robust against adversarial examples.We also show that the accuracy of ARGAN outperforms the accuracy of the state-of-the-art generative adversarial networks-based defense methods.
Explaining adversarial vulnerability with a data sparsity hypothesis

Despite many proposed algorithms to provide robustness to deep learning (DL) models, DL models remain susceptible to adversarial attacks. We hypothesize that the adversarial vulnerability of DL models stems from two factors. The first factor is data sparsity which is that in the high dimensional input data space, there exist large regions outside the support of the data distribution. The second factor is the existence of many redundant parameters in the DL models. Owing to these factors, different models are able to come up with different decision boundaries with comparably high prediction accuracy. The appearance of the decision boundaries in the space outside the support of the data distribution does not affect the prediction accuracy of the model. However, it makes an important difference in the adversarial robustness of the model. We hypothesize that the ideal decision boundary is as far as possible from the support of the data distribution. In this paper, we develop a training framework to observe if DL models are able to learn such a decision boundary spanning the space around the class distributions further from the data points themselves. Semi-supervised learning was deployed during training by leveraging unlabeled data generated in the space outside the support of the data distribution. We measured adversarial robustness of the models trained using this training framework against well-known adversarial attacks and by using robustness metrics. We found that models trained using our framework, as well as other regularization methods and adversarial training support our hypothesis of data sparsity and that models trained with these methods learn to have decision boundaries more similar to the aforementioned ideal decision boundary. We show that the unlabeled data generated by noise in our framework is almost as effective on adversarial robustness as unlabeled data sourced from existing datasets or generated by synthesis algorithms. The code for our training framework is available online.
Measuring the Accessibility of Domain Name Encryption and Its Impact on Internet Filtering

Most online communications rely on DNS to map domain names to their hosting IP address(es). Previous work has shown that DNS-based network interference is widespread due to the unencrypted and unauthenticated nature of the original DNS protocol. In addition to DNS, accessed domain names can also be monitored by on-path observers during the TLS handshake when the SNI extension is used. These lingering issues with exposed plaintext domain names have led to the development of a new generation of protocols that keep accessed domain names hidden. DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH) hide the domain names of DNS queries, while Encrypted Server Name Indication (ESNI) encrypts the domain name in the SNI extension. We present DNEye, a measurement system built on top of a network of distributed vantage points, which we used to study the accessibility of DoT/DoH and ESNI, and to investigate whether these protocols are tampered with by network providers (e.g., for censorship). Moreover, we evaluate the efficacy of these protocols in circumventing network interference when accessing content blocked by traditional DNS manipulation. We find evidence of blocking efforts against domain name encryption technologies in several countries, including China, Russia, and Saudi Arabia. At the same time, we discover that domain name encryption can help with unblocking more than 55% and 95% of censored domains in China and other countries where DNS-based filtering is heavily employed.
EPPDP: An Efficient Privacy-Preserving Data Possession With Provable Security in Cloud Storage

With the advancement in technology and the field of cloud computing, many enterprises prefer outsourcing their storage in a public cloud to minimize the maintenance and burden of storing data. Therefore, the data are being stored on remote storage servers without keeping a local copy of the data to perform their core business. However, in the absence of any local copy of data with the data owner, the physical control over the data and possession of data is restrained. Hence, it is essential to achieve privacy in the stored data. Further, while the data user would like to access the data for future use, it needs to preserve its integrity. In this article, we have proposed a low-cost and provably secure data possession scheme in a cloud store.
Gender Identification Using Keras Model Through Detection of Face

Gender identification is a critical topic in which research is still ongoing. Many gender identification systems have been developed utilizing various designs. With the help of the Raspberry Pi 4 Model B and Raspberry Pi Camera Module V2, this paper provides a real-time system for gender identification from images. Gender identification from face images has become a significant issue in recent years. In computer vision, various practical techniques are being explored to address such a difficult challenge. The face characteristic acquired is sent into the neural network as input or test data. The neural network was created to extract features and to function as a classifier to detect genders. However, the majority of these methods fall short of great precision and accuracy. With Python as the programming language, several functions such as OpenCV, Keras, and TensorFlow were utilized to assess the effectiveness of the design. A thousand samples were tested for foreign and Filipino datasets, yielding a training accuracy of nearly 90 percent and less than 1 percent loss accuracy. As a result, the system is a reliable device for determining a user’s gender.
An ANOVA Method to Rapidly Assess Information Leakage Near Cryptographic Modules

A measurement method based on the analysis of variance <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> -statistic is presented to rapidly evaluate cryptographic modules’ vulnerability to fine-grained EM side-channel analysis (SCA) attacks. The proposed method assumes that evaluators can control the device under test to set carefully chosen inputs to computations of interest and to repeat measurements as many times as needed. It identifies optimal measurement configurations—that minimize the marginal cost for repeated attacks to extract the data of interest—in three stages. In the first two stages, the variances in observed fields are analyzed using specially designed test cases and low <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-</i> value measurement configurations susceptible to noise are eliminated. In the third stage, the data of interest are extracted via a correlation-analysis attack using the remaining, high <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F-</i> value, configurations. The method is used to evaluate nine Advanced Encryption Standard (AES) implementations, seven of which were hardened against EM SCA attacks. The test cases for the first two stages are constructed by generating extreme AES encryption keys and input plaintexts. The least/most effective countermeasures are found to increase the marginal cost of EM SCA attacks by ∼1.1×/>30×; the proposed method could evaluate the vulnerabilities of hardened AES modules using ∼1.5–37× fewer measurements than alternatives.
Suitability of Different Metric Choices for Concept Drift Detection

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. Many unsupervised approaches for drift detection rely on measuring the discrepancy between the sample distributions of two time windows. This may be done directly, after some preprocessing (feature extraction, embedding into a latent space, etc.), or with respect to inferred features (mean, variance, conditional probabilities etc.). Most drift detection methods can be distinguished in what metric they use, how this metric is estimated, and how the decision threshold is found. In this paper, we analyze structural properties of the drift induced signals in the context of different metrics. We compare different types of estimators and metrics theoretically and empirically and investigate the relevance of the single metric components. In addition, we propose new choices and demonstrate their suitability in several experiments.
Explaining Local Path Plans Using LIME

As robots are becoming a more significant part of humans' daily life, there is a challenge to bridge the gap between robots' actions and humans' understanding of what robots are doing and how they make their decisions. We present an approach to local navigation explanation based on Local Interpretable Model-agnostic Explanations (LIME), a popular approach from the Explainable Artificial Intelligence (XAI) community for explaining individual predictions of black-box models. We show how LIME can be applied to a robot's local path planner. We experimentally evaluate the explanation method's runtime, quality, and robustness, and discuss implications for the robotic domain.
EVA2.0: Investigating Open-domain Chinese Dialogue Systems with Large-scale Pre-training

Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems. However, previous works mainly focus on showing and evaluating the conversational performance of the released dialogue model, ignoring the discussion of some key factors towards a powerful human-like chatbot, especially in Chinese scenarios. In this paper, we conduct extensive experiments to investigate these under-explored factors, including data quality control, model architecture designs, training approaches, and decoding strategies. We propose EVA2.0, a large-scale pre-trained open-domain Chinese dialogue model with 2.8 billion parameters, and will make our models and codes publicly available. Automatic and human evaluations show that EVA2.0 significantly outperforms other open-source counterparts. We also discuss the limitations of this work by presenting some failure cases and pose some future research directions on large-scale Chinese open-domain dialogue systems.
Unsupervised Low-Light Image Enhancement by Extracting Structural Similarity and Color Consistency

A novel structure-aware unsupervised network is proposed to deal with low-light image enhancement issues based on the inspiration of Retinex theory and self-supervised perceptual loss. It comprises four main components, namely the original structural similarity module, the novel color consistency module, the attentional enhancement module, and the naturalness discriminator module. Specially for the structural similarity module, an embedded structural feature extractor (SFE) model capable of generating structure correspondence is well designed and pre-trained by employing the contrastive learning technique, and a multi-scale structural similarity distance is introduced to optimize the SFE network. Besides, a self-supervised color consistency module is established by using a degraded estimation algorithm for recovering the missing colors. The whole enhancement framework operates in unsupervised manners and finally obtains the best naturalness image quality evaluator metric. Experimental results demonstrate that the proposed unsupervised network is able to recover natural structure and color images more effectively, which would also help to enlarge the practical application without collecting paired datasets in advance.
Paradigm Shift from Paper Contracts to Smart Contracts

The ambiguity and complexity of the traditional legal contracts have motivated the study and exploration of a better and advanced contract known as blockchain-based smart contracts. A smart contract is a self-executable contract where the terms of the agreement between the involved parties are directly written into the lines of code that resides in the distributed ledger technology known as the blockchain. Obtaining a better understanding of smart contracts to overcome the fundamental issues of traditional legal contracts is vital for the successful and faster dispute settlement process without the intervention of any third-party mediators like courts, banks, lawyers, etc. In this paper, we present a comprehensive overview of the key features of the paradigm shift from traditional paper contracts to smart contracts. In addition, we also discuss why smart contracts are necessary to be legally enforced and the crucial conditions that are required for them to be legally enforceable. Furthermore, we outline recent trends and emerging technologies such as Natural Language Processing, Machine Learning, and the Internet of Things that have been combined together with smart contracts.
Sign Language Recognition Using Convolutional Neural Network

Sign language is learned by deaf and dumb, and usually, it is not known to normal people, so it becomes a challenge for communication between a normal and hearing-impaired person. Thus, we decided to bridge the gap between hearing impaired and normal people and make the conversation easier. Sign language is one of the oldest and most natural forms of language for communication, but since most people do not know sign language and interpreters are very difficult to come by we have come up with a real-time method using neural networks for finger-spelling-based American sign language. In this research, a hand image is first passed through a filter and after the filter has applied the image is passed through a classifier that predicts the class of the hand gesture. Our method provides above 98% accuracy for the 26 letters of the alphabet. The paper implements that a functional real-time vision-based American sign language recognition for D&M people has been developed for ASL alphabets.
Digital Literacy of Digital Natives

Digital transformationDigital transformation is quickly progressing in all spheres of human lifeHuman life and new generationsGenerations have to adjust to changesChanges that the digital ageDigital age caused in the economy and society. Consequently, digital literacyDigital literacy is becoming one of the crucial skills of today’s students. Therefore, the education system needs to understand the growing significance of adjusting and modifying its educational methods to support the needs and specific characteristics of generationsGenerations living in the digitalDigital era. Students born and raised in the strongly developed digital environment are presented as digital nativesDigital natives. This chapter provides a theoretical overview of the context of digital nativesDigital natives as well as the methodology and results of an empirical research conducted among two generationsGenerations of digital nativesDigital natives, one in 2015 and one in 2020. The main aim of the named research is to assess the attitudes of students towards learning information technologyInformation Technology (IT), frequency of their usage of modern tolls such as Google tools, e-mail, and Microsoft Office tools as well as the levels of their digital literacyDigital literacy.
A Low-Complexity Modified ThiNet Algorithm for Pruning Convolutional Neural Networks

ThiNet is a recent method for pruning convolutional neural networks. This method uses a norm of a subset of the components of the output resulting from the convolutional layer succeeding the layer from which the filters are to be removed for pruning the network. The ThiNet algorithm is very time-consuming, in view of the fact that the filters for removal are selected one by one iteratively. In this paper, we propose a modified version of ThiNet, in which the same information on the output of the same convolutional layer as used by ThiNet is employed to select all the filters together in a single step, for pruning the network. The proposed modified algorithm is shown to have a time-complexity that is only a small fraction of that of ThiNet or any other state-of-the-art algorithm and that the pruned network has almost the same reduction in its accuracy as that of the network pruned by ThiNet.
Utility Fairness for the Differentially Private Federated-Learning-Based Wireless IoT Networks

Federated learning (FL) allows predictive model training on the sensed data in a wireless Internet of Things (IoT) network evading data collection cost in terms of energy, time, and privacy. In this article, for an FL setting, we model the learning gain achieved by an IoT device against its participation cost as its utility. The local model quality and the associated cost differ from device to device due to the device heterogeneity, which could be time varying. We identify that this results in utility unfairness because the same global model is shared among the devices. In the vanilla FL setting, the master is unaware of devices’ local model computation and transmission costs, thus, it is unable to address the utility unfairness problem. In addition, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby boost its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy (DP) to curtail global model divulgence based on the learning contribution. Furthermore, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99% in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103.
A human learning optimization algorithm with reasoning learning

Human Learning Optimization (HLO) is a simple yet powerful meta-heuristic developed based on a simplified human learning model. Many cognitive activities of humans contain an element of reasoning, and with reasoning, humans can gain deeper information on problems to boost learning performance. Inspired by this fact, this paper proposes a novel human learning optimization algorithm with reasoning learning (HLORL), in which a social reasoning learning operator (SRLO) is developed by using multiple social information sources to improve the global search ability of the algorithm. A parameter study is performed to give the recommended values of the control parameters. It also analyzes and discusses the role and function of the social reasoning learning operator. Finally, the proposed HLORL is applied to solve the CEC14 benchmark functions and 0-1 knapsack problems. The performance of HLORL is compared with the previous HLO variants and other state-of-art meta-heuristics. The experimental results demonstrate that the proposed HLORL has significant advantages over the compared algorithms.
Supervised segmentation with domain adaptation for small sampled orbital CT images

Abstract Deep neural networks have been widely used for medical image analysis. However, the lack of access to a large-scale annotated dataset poses a great challenge, especially in the case of rare diseases or new domains for the research society. Transfer of pre-trained features from the relatively large dataset is a considerable solution. In this paper, we have explored supervised segmentation using domain adaptation for optic nerve and orbital tumour, when only small sampled CT images are given. Even the lung image database consortium image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed domain adaptation method improved the performance of attention U-Net for the segmentation in public optic nerve dataset and our clinical orbital tumour dataset by 3.7% and 13.7% in the Dice score, respectively. The code and dataset are available at https://github.com/cmcbigdata.