Reviewerly/exHarmony huggingface dataset:
Random embedded calibrated statistical blind steganalysis using cross validated support vector machine and support vector machine with particle swarm optimization

The evolvement in digital media and information technology over the past decades have purveyed the internet to be an effectual medium for the exchange of data and communication. With the advent of technology, the data has become susceptible to mismanagement and exploitation. This led to the emergence of Internet Security frameworks like Information hiding and detection. Examples of domains of Information hiding and detection are Steganography and steganalysis respectively. This work focus on addressing possible security breaches using Internet security framework like Information hiding and techniques to identify the presence of a breach. The work involves the use of Blind steganalysis technique with the concept of Machine Learning incorporated into it. The work is done using the Joint Photographic Expert Group (JPEG) format because of its wide use for transmission over the Internet. Stego (embedded) images are created for evaluation by randomly embedding a text message into the image. The concept of calibration is used to retrieve an estimate of the cover (clean) image for analysis. The embedding is done with four different steganographic schemes in both spatial and transform domain namely LSB Matching and LSB Replacement, Pixel Value Differencing and F5. After the embedding of data with random percentages, the first order, the second order, the extended Discrete Cosine Transform (DCT) and Markov features are extracted for steganalysis.The above features are a combination of interblock and intra block dependencies. They had been considered in this paper to eliminate the drawback of each one of them, if considered separately. Dimensionality reduction is applied to the features using Principal Component Analysis (PCA). Block based technique had been used in the images for better accuracy of results. The technique of machine learning is added by using classifiers to differentiate the stego image from a cover image. A comparative study had been during with the classifier names Support Vector Machine and its evolutionary counterpart using Particle Swarm Optimization. The idea of cross validation had also been used in this work for better accuracy of results. Further parameters used in the process are the four different types of sampling namely linear, shuffled, stratified and automatic and the six different kernels used in classification specifically dot, multi-quadratic, epanechnikov, radial and ANOVA to identify what combination would yield a better result.
Fast Conversion of Three‐Particle Dicke States to Four‐Particle Dicke States with Rydberg Superatoms

Abstract Dicke states play an important role in quantum information processing and quantum computing. Here, by virtue of quantum Zeno dynamics and shortcuts to adiabaticity, a scheme for fast conversion of three‐particle symmetric Dicke states to four‐particle symmetric Dicke states () with Rydberg superatoms is proposed. This scheme can be completed within one step through adjusting the Rabi frequencies of classical fields. Moreover, the effect of decoherence induced by atomic spontaneous emission and leakage of the cavity on fidelities are considered. The numerical simulation demonstrates that this scheme has high fidelities and robustness against decoherence.
Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models

The aim of this study is to apply and characterize eXplainable AI (XAI) to assess the quality of synthetic health data generated using a data augmentation algorithm. In this exploratory study, several synthetic datasets are generated using various configurations of a conditional Generative Adversarial Network (GAN) from a set of 156 observations related to adult hearing screening. A rule-based native XAI algorithm, the Logic Learning Machine, is used in combination with conventional utility metrics. The classification performance in different conditions is assessed: models trained and tested on synthetic data, models trained on synthetic data and tested on real data, and models trained on real data and tested on synthetic data. The rules extracted from real and synthetic data are then compared using a rule similarity metric. The results indicate that XAI may be used to assess the quality of synthetic data by (i) the analysis of classification performance and (ii) the analysis of the rules extracted on real and synthetic data (number, covering, structure, cut-off values, and similarity). These results suggest that XAI can be used in an original way to assess synthetic health data and extract knowledge about the mechanisms underlying the generated data.
Natural language watermarking via paraphraser-based lexical substitution

Although powerful pretrained language models generate high-quality output text, they bring new concerns about the potential misuse of such models for malicious purposes. Natural language watermarking (NLW) is a technique that is desgined to help tracing the provenance of texts for againsting possible attacks, where the watermark signals are embedded into cover texts using synonym substitutions. The up-to-date BERT-based NLW methods have made remarkable progress on performance improvement of watermarking through generating substitutes for a masked target word. Yet, the BERT-based NLWs focus on the context of texts rather than the meaning of target words, which might make the capacity of watermark embeddings being lower. To address the limitations, this study proposes a novel NLW method by incorporating a paraphraser-based lexical substitution method. Under the promise of paraphrase preservation, the proposed NLW method utilizes the knowledge of paraphrase modeling to generate the substitute candidates to replace the words in original sentences capable of carrying the watermark signal in local contexts. We empirically show that our NLW method not only has a better meaning-preserved, but improves the payload more than 2 times compared with the BERT-based NLW method. Besides, compared with previous state-of-the-art method Compared with other state-of-the-art baselines, the experimental results show that the proposed LS method improves the Precision@1 score from 51.7% to 58.3% and from 50.5% to 62.6% on LS07 and CoInCo benchmarks, respectively.
FPGANeedle

FPGA as general-purpose accelerators can greatly improve system efficiency and performance in cloud and edge devices alike. However, they have recently become the focus of remote attacks, such as fault and side-channel attacks from one to another user of a part of the FPGA fabric. In this work, we consider system-on-chip platforms, where an FPGA and an embedded processor core are located on the same die. We show that the embedded processor core is vulnerable to voltage drops generated by the FPGA logic. Our experiments demonstrate the possibility of compromising the data transfer from external DDR memory to the processor cache hierarchy. Furthermore, we were also able to fault and skip instructions executed on an ARM Cortex-A9 core. The FPGA based fault injection is shown precise enough to recover the secret key of an AES T-tables implementation found in the mbedTLS library.
Rationality in Mathematical Proofs

Mathematical proofs are not sequences of arbitrary deductive steps—each deductive step is, to some extent, rational. This paper aims to identify and characterize the particular form of rationality at play in mathematical proofs. The approach adopted consists in viewing mathematical proofs as reports of proof activities—that is, sequences of deductive inferences—and in characterizing the rationality of the former in terms of that of the latter. It is argued that proof activities are governed by specific norms of rational planning agency, and that a deductive step in a mathematical proof qualifies as rational whenever the corresponding deductive inference in the associated proof activity figures in a plan that has been constructed rationally. It is then shown that mathematical proofs whose associated proof activities violate these norms are likely to be judged as defective by mathematical agents, thereby providing evidence that these norms are indeed present in mathematical practice. We conclude that, if mathematical proofs are not mere sequences of deductive steps, if they possess a rational structure, it is because they are the product of rational planning agents.
Email-Based Cyberstalking Detection On Textual Data Using Multi-Model Soft Voting Technique Of Machine Learning Approach

In the virtual world, many internet applications are used by a mass of people for several purposes. Internet applications are the basic needs of people in the modern days of lifestyle which are also making habitual society. Like social media, e-mail technology is also more prevalent among people of different categories for personal and official communications. The widespread use of e-mail-based communication is also raising various types of cybercrimes, including cyberstalking. Cyberstalkers also use an e-mail-based approach to harass the victim in the form of cyberstalking. Cyberstalkers utilize several content-wise and intent-wise approaches to target the victim, such as spamming, phishing, spoofing, malicious, defamatory, e-mail bombing, and non-spam e-mails, including sexism, racism, and threatening, and finally, trying to hack the account over e-mail technology. This paper proposed an EBCD model for automatic cyberstalking detection on textual data of e-mail using the multi-model soft voting technique of the machine learning approach. Initially, experimental works were performed to train, test, and validate all classifiers of three model sets on three different labeled datasets. Dataset D1 contains spam, fraudulent, and phishing e-mail subject, dataset D2 contains spam e-mail body text, while dataset D3 contains harassment-related data. After that, trained, tested, and validated classifiers of all model sets were applied as a combined approach to automatically classify the unlabeled e-mails from the user’s mailbox using the multi-model soft voting technique. The proposed EBCD model successfully classifies the e-mails from the user’s mailbox into cyberstalking e-mails, suspicious e-mails (spam and fraudulent), and normal e-mails. In each model set of the EBCD model, several classifiers, namely support vector machine, random forest, naïve bayes, logistic regression, and soft voting, were used. The final decision in classifying the e-mails from the user’s mailbox was taken by the soft voting technique of each model set. The TF-IDF feature extraction method was used with the entire applied machine learning model sets to obtain the feature vectors from the data. Experimental results show that the soft voting technique not only enhances the performance of the e-mail classification task but also supports making the right decision to avoid the wrong classification. Overall performance of the soft voting technique was better than other classifiers, although the performance of the support vector machine was also notable. As per experimental results, the soft voting technique obtained an accuracy of 97.7%, 97.7%, 98.9%, a precision of 97%, 98.3%, 98.6%, recall of 98.3%, 96.5%, 99.1%, f-score of 97.6%, 97.4%, 98.9%, and AUC of 99.4%, 99.7%, 99.9% on dataset D1, D2, and D3 respectively. The average performance of soft voting of each model set on classified e-mails from the user’s mailbox was also notable, with an accuracy of 96.3%, precision of 98.1%, recall of 94%, f-score of 95.9%, and AUC of 96.8%.
The <i>Sacra Infermeria</i>—a focus group evaluation of an augmented reality cultural heritage experience

The digital representation of our past has long been an important tool in the interpretation of cultural heritage in museums. The recent rise in the use of Augmented Reality (AR) has seen various approaches to adding dynamic information to existent artefacts. The challenge is even greater when uncertainty further complexifies the represented history. This paper presents a critical analysis of an AR installation in the Sacra Infermeria museum in Valletta, Malta. After a description of the AR configuration of the installation, we present a thematic analysis carried out from a multidisciplinary focus group of 11 researchers in the field of Interactive Digital Narratives (IDN), from three perspectives: the technological implementation of the AR experience, the historical accuracy, gamification and the influence of social media-centred design, and the representation of the complexity arising from the uncertainty of history. In the light of the results of the multidisciplinary focus group, we provide a list of recommendations and heuristics.
Transitioning to virtual reality learning in 5E learning model: pedagogical practices for science learning

Virtual Reality (VR) has been widely adopted in educational research; however, its implementation in the classroom is still not as well known as in the research field. In other words, there is a gap between researchers and practitioners regarding the pedagogical issue. This study aimed to explore how VR could be applied in science classrooms using the 5E learning model approach, thus providing an overview of VR implementation from teachers' perspectives. The method was a qualitative study in which it was validated using methodological triangulation, including open-ended questions, video-recording observations, and field notes. The finding revealed that learning science using VR for visualizing abstract concepts was suitable to be integrated with the 5E learning model, which provided an authentic opportunity for students to become curious and engage in the inquiry process. However, teachers should guide students to avoid misconceptions during the learning process. Moreover, this finding provides practical insights where an elaboration prompt is required before using a VR headset to turn the potential of VR into actual in-depth learning. Finally, 5E learning implementation can be integrated with other learning approaches, such as situated learning and guided inquiry learning, to suit the contents and teachers' pedagogical knowledge.
Towards an understanding of meme marketing: conceptualisation and empirical evidence

Meme marketing taps into the potential of digital culture. Despite considerable practitioner interest, meme marketing is under-researched. The present study aims to fulfil two purposes. First, to conceptualise meme marketing, we draw on speech act theory and the theory of consumption values to define meme marketing. On this basis, we differentiate meme marketing from relevant digital marketing constructs, including e-WOM, digital content marketing, and viral marketing. Second, to validate the proposed definition, we empirically perform a qualitative content analysis to taxonomize meme marketing speech acts. Based on the content analysis of 699 meme marketing posts, we found that meme marketing serves as a rich communication source, sending intentions of the brand through single (i.e. assertive, directive, expressive) as well as combined (i.e. assertive entailing expressive, assertive entailing directive) speech acts that provide customers with epistemic, emotional, and social values. Important theoretical and managerial implications for academic researchers and marketers are also discussed.
Block-A-City: An Agricultural Application Framework Using Blockchain for Next-Generation Smart Cities

Conventional lifestyles and working methods are motivated by rising trends and new technologies to form smart cities. Distributed chains are growing to computerize with a highly complicated framework. It is becoming an essential source of additional advantages in today's digital and smart city. Customers or users of smart cities are highly concerned about the quality of food and farm products. On the other hand, tracking the source information is challenging and, preserving its provenance throughout the supply chain network. Traditional supply chain networks are centralized, where a third party does the trading. These centralized systems lack of quality transparency and distribution accountability. Therefore, we provide a promising approach with a unique strategy for converting conventional agriculture to smart agriculture, concerning smart cities, considering blockchain technology and the Internet of Things (IoT) features. The blockchain technique is responsible for maintaining the immutability of the existing network. Still, it faces problems resolving fundamental issues in the distribution chain, like the trustworthiness of the different involved members, transaction responsibility, and real-time tracking. As a result, a trustworthy organization with traceability, conviction, confirmed transportation, and transparent mechanisms in the e-farm supply chain is proposed. Ethereum is used to deploy the proposed smart contract. Testing with the Hyperledger caliper measuring tool shows performance evaluation of several factors such as latency, throughput, resource consumption transaction per second, and so on are assessed. The findings support the effectiveness of the recommended strategy.
The effects of knowledge mechanisms on employees' information security threat construal

Abstract Organisations implement a variety of knowledge mechanisms such as information security education, training and awareness (SETA) programs and information security policies, to influence employees' secure behaviour. Despite increased efforts to provide information systems (IS) security knowledge to employees, data breaches and other security incidents resulting from insider behaviour continue. Recent IS security research, primarily grounded on assumptions of employees' rational assessment of numerous factors, has yielded inconsistent results. Challenging this paradigm, we model secure behaviour on security knowledge mechanisms, which focuses on the multidimensional nature of security knowledge breadth, depth and finesse to represent the full array of managerial levers. We further draw on construal level theory to conceptualise users' perceptual judgements of security messages. Two studies support our model, with the second building on the first. Study 1, an experiment with 312 participants, focused on validating the treatments. Study 2, a survey with 219 participants, validated the entire model. Results showed that our model has significantly more explanatory and predictive power than the orthodox paradigm. Our results have practical implications for optimising the organisation of knowledge mechanisms by emphasising the personal relevance of threats and defining the factors that lead to secure behaviour. We also contribute to the discourse on information security research and provide a template for integrating theories, thus opening new avenues for future research.
Robots make no effort! Service evaluation and consumer mindset

Abstract Considering the growing acceptance of humanoid robots in the service industry, this study aimed to examine their negative impact on service evaluation, as well as the underlying mechanism of perceived effort and the moderating role of consumer mindset. Three experiments that used different service scenarios revealed that humanoid service robots negatively affected service evaluation compared to human employees, and this effect was mediated by decreased perceived effort. Furthermore, this negative impact was attenuated when consumers had a concrete mindset compared to abstract. This work contributes to both consumer service and robot literature by elaborating on the possible adverse influence of replacing human employees with humanoid service robots. It also offers managerial implications for how and when to adopt a robot service in this machine age.
Transformers-Based Encoder Model for Forecasting Hourly Power Output of Transparent Photovoltaic Module Systems

Solar power generation is usually affected by different meteorological factors, such as solar radiation, cloud cover, rainfall, and temperature. This variability has shown a negative impact on the large-scale integration of solar energy into energy supply systems. For successful integration of solar energy into the electrical grid, it is necessary to predict the accurate power generation by solar panels. In this work, solar power generation forecasting for two types of solar system (non-transparent and transparent panels) was configured by the smart artificial intelligence (AI) modelling. For deep learning models, the dataset obtained from the target value of electricity generation in kWh and other features, such as weather conditions, solar radiance, and insolation. In PV power generation values from non-transparent and transparent solar panels were collected from 1 January to 31 December 2021 with an hourly interval. To prove the efficiency of the proposed model, several deep learning approaches RNN models, such as LSTM, GRU, and transformers models, were implemented. Transformers model for forecasting power generation expressed the best model for non-transparent and transparent solar panels with lower error rates for MAE 0.05 and 0.04, and RMSE 0.24 and 0.21, respectively. The proposed model showed efficient performance and proved effective in forecasting time-series data.
Beyond presence: Creating attractive online retailing stores through the cool <scp>AR</scp> technology

Abstract Augmented reality (AR) is increasingly coming into the spotlight for its potential to improve the consumer experience through the creation of presence. This research aims to explore the theoretical mechanisms through which AR‐based product presentation influences online store attractiveness and whether the effects differ in the purchasing contexts of hedonic and utilitarian product types. From the perspective of consumers' bidimensional experience, we find that AR increases online store attractiveness by creating perceived coolness (intrinsic attribute) and spatial presence experience (extrinsic attribute), which would further have a positive impact on consumers' purchase intention. The mediating mechanisms are different in purchasing contexts of various product types: for hedonic products, perceived coolness and spatial presence are parallel mediating factors leading to the improvement of online store attractiveness; while for utilitarian products, only the mediation effect of spatial presence presents. Our findings enrich the literature on AR marketing by proposing an insightful mediating force (i.e., perceived coolness) to complement the effect of presence, and explore the different purchasing contexts. We also provide managerial guidance for e‐retailers to differentiate AR interface design for diverse product types to apply AR technology effectively.
Wavefit: an Iterative and Non-Autoregressive Neural Vocoder Based on Fixed-Point Iteration

Denoising diffusion probabilistic models (DDPMs) and generative adversarial networks (GANs) are popular generative models for neural vocoders. The DDPMs and GANs can be characterized by the iterative denoising framework and adversarial training, respectively. This study proposes a fast and high-quality neural vocoder called WaveFit, which integrates the essence of GANs into a DDPM-like iterative framework based on fixed-point iteration. WaveFit iteratively denoises an input signal, and trains a deep neural network (DNN) for minimizing an adversarial loss calculated from intermediate outputs at all iterations. Subjective (side-by-side) listening tests showed no statistically significant differences in naturalness between human natural speech and those synthesized by WaveFit with five iterations. Furthermore, the inference speed of WaveFit was more than 240 times faster than WaveRNN. Audio demos are available at google.github.io/df-conformer/wavefit/.