Reviewerly/exHarmony huggingface dataset:
SemAttack: Natural Textual Attacks via Different Semantic Spaces

Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks.However, existing attack methods either suffer from low attack success rates or fail to search efficiently in the exponentially large perturbation space.We propose an efficient and effective framework SemAttack to generate natural adversarial text by constructing different semantic perturbation functions.In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e.g., WordNet), contextualized semantic space (e.g., the embedding space of BERT clusterings), or the combination of these spaces.Thus, the generated adversarial texts are more semantically close to the original inputs.Extensive experiments reveal that state-of-the-art (SOTA) large-scale LMs (e.g., DeBERTa-v2) and defense strategies (e.g., FreeLB) are still vulnerable to SemAttack.We further demonstrate that SemAttack is general and able to generate natural adversarial texts for different languages (e.g., English and Chinese) with high attack success rates.Human evaluations also confirm that our generated adversarial texts are natural and barely affect human performance.Our code is publicly available at https://github.com/AI-secure/SemAttack.
Technology, Pedagogy and Content (TPACK framework): Systematic Literature Review

The TPACK framework, which has been translated as Technological, Pedagogical and Content Knowledge is an essential theoretical reference model in initial training and teacher professional development programs for the integration of technology. This article seeks to reveal the keys, trends and recommendations of the incorporation of ICT in the teaching-learning process based on the findings of a systematic literature review on TPACK. This review has followed the PRISMA protocol with a default search equation. It is concluded that the TPACK framework is a valuable theoretical reference for the effectiveness of the integration of technology in the teaching-learning process and therefore, for its transformation and change. In addition, the training actions must place the teacher as a leading, active and participatory actor in decision-making for the integration of ICT. It is recommended to promote empirical research in various contexts and educational levels on ICT to establish the best training routes according to educational needs and characteristics.
Share-slicing: Friend or Foe?

Masking is a well loved and widely deployed countermeasure against side channel attacks, in particular in software. Under certain assumptions (w.r.t. independence and noise level), masking provably prevents attacks up to a certain security order and leads to a predictable increase in the number of required leakages for successful attacks beyond this order. The noise level in typical processors where software masking is used may not be very high, thus low masking orders are not sufficient for real world security. Higher order masking however comes at a great cost, and therefore a number techniques have been published over the years that make such implementations more efficient via parallelisation in the form of bit or share slicing. We take two highly regarded schemes (ISW and Barthe et al.), and some corresponding open source implementations that make use of share slicing, and discuss their true security on an ARM Cortex-M0 and an ARM Cortex-M3 processor (both from the LPC series). We show that micro-architectural features of the M0 and M3 undermine the independence assumptions made in masking proofs and thus their theoretical guarantees do not translate into practice (even worse it seems unpredictable at which order leaks can be expected). Our results demonstrate how difficult it is to link theoretical security proofs to practical real-world security guarantees.
Multi-criteria clustering-based recommendation using Mahalanobis distance

There have been significant advances made in the research of recommender systems over the past decades and have been implemented in both industry and academia. Recently, multi-criteria ratings are being incorporated into traditional recommender systems to further improve their quality, especially to handle the data sparsity and cold start issues. However, incorporation of multi-criteria ratings have improved the performance of the recommendation, but at the same time, multi-dimensionality issue also arises. This paper presents a clustering-based recommendation approach which is used for dealing with the multi-dimensionality issue in multi-criteria recommender systems. Here, we cluster the users based on their individual criteria ratings using K-means clustering and the intra-cluster similarity is computed using Mahalanobis distance measure for neighbourhood set generation. This improves the recommendations quality and predictive accuracy of both traditional and clusteringbased collaborative recommendations. The Yahoo! Movies dataset was used for testing the approach and the experiment conducted shows promising results.
New colour fusion deep learning model for large-scale action recognition

In this work we propose a fusion methodology that takes advantage of multiple deep convolutional neural network (CNN) models and two colour spaces RGB and oRGB to improve action recognition performance on still images. We trained our deep CNNs on both the RGB and oRGB colour spaces, extracted and fused all the features, and forwarded them to an SVM for classification. We evaluated our proposed fusion models on the Stanford 40 Action dataset and the People Playing Musical Instruments (PPMI) dataset using two metrics: overall accuracy and mean average precision (mAP). Our results prove to outperform the current state-of-the-arts with 84.24% accuracy and 83.25% mAP on Stanford 40 and 65.94% accuracy and 65.85% mAP on PPMI. Furthermore, we also evaluated the individual class performance on both datasets. The mAP for top 20 individual classes on Stanford 40 lies between 97% and 87%, on PPMI the individual mAP class performance lies between 87% and 34%.
Genetic algorithm-based oversampling approach to prune the class imbalance issue in software defect prediction

Class imbalance is the potential problem that has been existent in machine learning, which hinders the performance of the classification algorithm when applied in real-world applications such as electricity pilferage, fraudulent transactions, anomaly detection, and prediction of rare diseases. Class imbalance refers to the problem where the distribution of the sample is skewed or biased toward one particular class. Due to its intrinsic nature the software fault prediction dataset falls into the same category where the software modules contain fewer defective modules compared to the non-defective modules. The majority of the oversampling techniques that has been proposed is to address the issue by generating synthetic samples of minority class to balance the dataset. But the synthetic samples generated are near duplicates that also results in over-generalization issue. We thus propose a novel oversampling approach to introduce synthetic samples using genetic algorithm (GA). GA is a form of evolutionary algorithm that employs biologically inspired techniques such as inheritance, mutation, selection, and crossover. The proposed algorithm generates synthetic sample of minority class based on the distribution measure and ensures that the samples are diverse within the class and are efficient. The proposed oversampling algorithm has been compared with SMOTE, BSMOTE, ADASYN, random oversampling, MAHAKIL, and no sampling approach with 20 defect prediction datasets from the promise repository and five prediction models. The results indicate that the genetic algorithm oversampling approach improves the fault prediction performance and reduced false alarm rate.
An AES Double–Layer Based Message Security Scheme

Nowadays, the Achilles heel of any software system that deals with data communication and/or storage on a cloud is the security of information being handled. However, modern cryptographic methods were proven vulnerable to brute force and side channel attacks, and thus were dubbed insufficient to be used in a secure system. Security is effected by using the cryptographic methods in proactive contexts, one of which is the use of a cryptography/steganography hybrid multi-layer system. Cryptography would be responsible for the secrecy of data whereas steganography would be responsible for the secrecy of communication. In this work, we propose two possible hierarchies which are common in that they are both double-layered and that they employ AES-256 for the cryptographic layer. As for the steganographic layer, we propose using the least significant bit (LSB) embedding by means of color-plane cycling in bitmap images. The cycling follows a prime number sequence in one method and the Fibonacci sequence in the other. We compute the mean squared error (MSE) and the peak signal to noise ratio (PSNR) for both techniques for comparison and we show the superiority of the Fibonacci sequence. We also compare our results with the results reported in the literature for some of the most commonly used LSB steganographic methods in hybrid systems and show the superiority of our proposed message security scheme.
IoT Forensics

Abstract This chapter provides an overview of research opportunities and issues in IoT forensics. It gives a quick introduction to forensics and digital forensics. Key specifics of IoT forensics are explained. Issues that arise from IoT related challenges in all phases of a forensic investigation are presented. Some opportunities that IoT brings to forensics are pointed out. An example of an IoT forensics case is provided. A detailed research overview is given, providing information on the main research directions with a brief overview of relevant papers. The chapter concludes with some ideas for future research.
An effective hashing method using W-Shaped contrastive loss for imbalanced datasets

The extraction of informative features from medical images and the retrieving of similar images from data repositories is vital for clinical decision support systems. Unlike general tasks such as medical image classification and segmentation, retrieval is more reliable in terms of interpretability. However, this task is quite challenging due to the multimodal and imbalanced nature of medical images. Because traditional retrieval methods use hand-crafted feature extraction guided approximate hashing functions, they often have problems capturing the latent characteristics of images. Deep learning based retrieval methods can eliminate drawbacks of hand-crafted feature extraction methods. However, in order for a deep architecture to produce high performance, large-scale datasets containing labeled and balanced samples are required. Since most medical datasets do not have these properties, existing hashing methods are not powerful enough to model patterns in medical images, which have a similar general appearance but subtle differences. In this study, a novel W-shaped contrastive loss (W-SCL) is proposed for skin lesion image retrieval on a dataset whose visual difference between classes is relatively low. We considerably improve the traditional contrastive loss (CL) performance by including label information for very similar skin lesion images. We use two benchmark datasets consisting of general images and two benchmark skin lesion datasets to test the proposed W-SCL performance. In addition, experiments are carried out using various pre-trained CNN and shallow CNN architectures. These extensive experiments reveal that the proposed method improves the mean average precision (mAP) performance by approximately 7% for general image datasets and approximately 12% for skin lesion datasets.
Impact of activation functions and number of layers on detection of exudates using circular Hough transform and convolutional neural networks

• First study to examine the effect of activation function for exudate detection. • Swish was more successful than Rectified Linear Unit for exudate detection. • Image processing and deep learning were used in a hybrid way. • Decision support system has been developed to assist experts. Convolutional neural networks (CNN), which are used for object detection, are widely used in image recognition and segmentation fields due to their good performance. In this study, a method is proposed for the detection of exudate from DR lesions based on image processing and CNN model with different activation functions and layer numbers. Activation functions are one of the main reasons why the CNN model is complicated in hierarchical structure. CNN models have real artificial intelligence capabilities, especially thanks to non-linear activation functions. Although ReLU is the most commonly used activation function in CNN models, it has some shortcomings in practice. The most important shortcoming is that if the input value is negative, the learning process will slow down due to the inability to take the derivative. In order to solve this problem, the effect of the activation function in the CNN model on a real world problem and its image was investigated in this study. By applying circular Hough transform, one of the image processing methods, the OD region, which has a similar structure to the exuding regions, was determined and removed from the image, and then the detection of the exuding regions was carried out using the CNN model. Exudate detection was performed using ReLU, ELU, Leaky ReLU, Softplus and Swish activation functions in the CNN model and the results were compared. Experimental results in DiaretDB0 and DiaretDB1 databases show that the Swish activation function has a better performance than the others.
Modular ontology modeling

Reusing ontologies for new purposes, or adapting them to new use-cases, is frequently difficult. In our experiences, we have found this to be the case for several reasons: (i) differing representational granularity in ontologies and in use-cases, (ii) lacking conceptual clarity in potentially reusable ontologies, (iii) lack and difficulty of adherence to good modeling principles, and (iv) a lack of reuse emphasis and process support available in ontology engineering tooling. In order to address these concerns, we have developed the Modular Ontology Modeling (MOMo) methodology, and its supporting tooling infrastructure, CoModIDE (the Comprehensive Modular Ontology IDE – “commodity”). MOMo builds on the established eXtreme Design methodology, and like it emphasizes modular development and design pattern reuse; but crucially adds the extensive use of graphical schema diagrams, and tooling that support them, as vehicles for knowledge elicitation from experts. In this paper, we present the MOMo workflow in detail, and describe several useful resources for executing it. In particular, we provide a thorough and rigorous evaluation of CoModIDE in its role of supporting the MOMo methodology’s graphical modeling paradigm. We find that CoModIDE significantly improves approachability of such a paradigm, and that it displays a high usability.
Audit data analytics, machine learning, and full population testing

Emerging technologies like data analytics and machine learning are impacting the accounting profession. In particular, significant changes are anticipated in audit and assurance procedures because of those impacts. One such potential change is audit sampling. As audit sampling only provides a small snapshot of the entire population, it starts to lose some of its meaning in this big data era. One feasible solution is the usage of audit data analytics and machine learning to enable an analysis of the entire population rather than a sample of the transactions. This paper presents an approach for applying audit data analytics and machine learning to full population testing and discusses related challenges.
Identifying predictors of digital competence of educators and their impact on online guidance

In the current socio-health situation, new educational challenges have emerged, such as the need to implement a virtual tutorial action. Therefore, this study has three objectives: (1) to investigate the level of digital competence that early childhood and primary school teachers possess to carry out quality online tutorial actions; (2) to analyse whether there are differences in use at both education stages; and (3) to identify which variables significantly affect the development of this competence at each educational stage. For this purpose, an ex post facto design was used, based on the survey technique, by means of non-probabilistic purposive sampling. The final sample consisted of a total of 1,069 educators working at the early childhood and primary education stages, from all over Spain. The results showed acceptable digital competence, with higher scores in the primary education stage, which may be due to characteristics of the students and the education stage itself, rather than to teachers' digital competences. On the other hand, it was found that the virtual tutorial action tasks are significantly influenced in the early childhood education stage by blogs, WhatsApp, Facebook, and number of tutoring hours per month with families, while for the primary stage they are influenced by blogs, WhatsApp, Twitter, ClassDojo, Moodle, tutoring hours, number of tutoring hours per month with families, and sex. Based on these results, there is an obvious need for educational institutions to continue to develop teacher training in relation to the use of resources to carry out adequate tutoring actions and thus increase the diversification in the use of resources.
Enabling Cost-Effective and Secure Minor Medical Teleconsultation Using Artificial Intelligence and Blockchain

While the onset of the COVID-19 pandemic has increased the popularity of home-based consultations, worries over privacy, high consultations costs, slow response times, and the burden on doctors due to the overwhelming number of COVID-19 cases have made current in-person and online models ineffective. In this study, we present an advanced, privacy-protected, artificial intelligence and blockchain-based consultation framework for minor medical conditions. Patients can post their medical queries anonymously on the blockchain network, which may be answered by any available medical professionals. The queries are sorted into their respective domains using naive Bayes and logistic regression. The consultations provided by medical specialists are evaluated based on their reputation, expertise, detail orientation, and the use of supporting documents, and rewards are given in accordance with the evaluation scheme. This fair and incentivized system provides cheaper and more accessible healthcare to patients, which is the need of the hour.
An improved categorical cross entropy for remote sensing image classification based on noisy labels

In recent years, volunteered geographic information (VGI) is widely used to train deep convolutional neural networks (DCNNs) for high-resolution remote sensing image classification. However, noisy labels are often included in the training samples generated by VGI, which inevitably affects the performance of DCNNs. To solve this problem, the negative effect of noisy labels on remote sensing image classification by DCNNs is analyzed. Then, an improved categorical cross-entropy (ICCE) is proposed to address the issue of noisy labels. The ICCE improves the robustness of DCNN to noisy labels by revisiting the sample weighting scheme so that much attention is paid to the clean samples instead of the noisy samples. Besides, the error bound of ICCE is derived with strict mathematical proof under different types of noisy labels, which ensures the advantages of ICCE from theory. Additionally, extensive experiments are conducted on three remote sensing image datasets with simulated and real noisy labels to quantitatively evaluate the performance of ICCE. • This paper addresses the noisy label issue in remote sensing images classification. • An improved categorical cross entropy (ICCE) is proposed to handle noisy labels. • The theoretical error bound of ICCE is given with strict mathematical proof. • We demonstrate the effectiveness of ICCE on three remote sensing image datasets.
Experimental Side-Channel-Secure Quantum Key Distribution

Quantum key distribution can provide unconditionally secure key exchange for remote users in theory. In practice, however, in most quantum key distribution systems, quantum hackers might steal the secure keys by observing the side channels in the emitted photons, such as the photon frequency spectrum, emission time, propagation direction, spatial angular momentum, and so on. It is hard to prevent such kinds of attacks because side channels may exist in many dimensions of the emitted photons. Here we report an experimental realization of a side-channel-secure quantum key distribution protocol which is not only measurement-device independent, but also immune to all side-channel attacks to the photons emitted from Alice's and Bob's labs. We achieve a secure key rate of 1.73×10^{-6} per pulse through 50 km fiber spools.
A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting

The integration of renewable energy generation presented an important development around the globe and conveys countless financial, commercial, and environmental assistance. However, the random and non-linear nature of solar power generation, the significant perception of solar system may challenge the comprehension and operation of the existing power system. Nowadays, a smart energy forecasting system is important for solar energy generation to provide high-quality energy for end-users to enhance the power system's reliability and operation. Inspired by the performance and advancements of deep learning approaches in the field of energy, a CNN-assisted deep echo state network (CNN-DeepESN) with principal components analysis (PCA) method is proposed for generating short-term solar energy forecasting. In this study, we designed a hybrid deep learning method to utilized one-hour solar forecasting ahead with five-minute time intervals. The CNN is used to prepare to compose the initial tensor of input solar time-series data and fed to the DeepESN model to compute the features with a high dimensional space by reservoir that avoid the model complexity due to its untrained and sparse nature. Therefore, we adopt the PCA for dimensionality reduction to minimize the computational cost since it offers significant advantages. We conducted extensive experiments to demonstrate and evaluate the proposed system performance by using authentic data collected from the Australian city, Alice Springs. Additionally, conducted a comparative analysis with other state-of-the-art models to illustrate the superiority of the proposed system. Three main evaluation matrices such as MAE, MAPE, and RMSE are used and achieves 0.0381, 3.3313, and 0.3101 score, respectively to show the model robustness and significance in energy forecasting and generation.