Reviewerly/exHarmony huggingface dataset:
Double layer steganography technique using DNA sequences and images

Information security has become increasingly challenging as a result of massive advancements in information and communication technologies. Due to the necessity of exchanging private information and the open nature of the network, there is an increased risk of various types of attacks. Consequently, data security is an essential component of data communication. One of the most effective methods used to achieve secrecy is steganography. This method hides data within a cover object without raising suspicion. The level of security is improved when two steganography methods are combined. This approach is known as multilevel steganography, which hides sensitive data in two cover objects in order to provide a two-level security system. Accordingly, we developed a technique that focuses on protecting secrecy while also being robust to attacks. The new technique uses a multi-layer steganography mechanism by using DNA sequences and images as carriers for sensitive data. The technique intends to hide secret messages in the DNA using the substation algorithm, and then the fake DNA is embedded in an image utilizing the discrete cosine transform (DCT) method. Eventually, the stego image is sent to the intended recipient. Different types of images with different sizes and lengths of messages and DNA sequences were used during the experiments. The results show that the proposed mechanism is resistant to histogram and chi-square attacks. The maximum mean value observed was 0.05, which means the histograms of the original and stego images are nearly identical, and the stego image does not raise any suspicion regarding the existence of secret information. In addition, the imperceptibility ratios were good, as the highest PSNR and MSE values were 0.078 and 72.2, respectively. Finally, the PNG and BMP images show excellent results. On the other hand, the JPG images failed to meet the expected ratio of imperceptibility and security.
Self-Sovereign Identity for Consented and Content-Based Access to Medical Records Using Blockchain

Electronic health records (EHRs) and medical data are classified as personal data in every privacy law, meaning that any related service that includes processing such data must come with full security, confidentiality, privacy, and accountability. Solutions for health data management, as in storing it, sharing and processing it, are emerging quickly and were significantly boosted by the COVID-19 pandemic that created a need to move things online. EHRs make a crucial part of digital identity data, and the same digital identity trends—as in self-sovereign identity powered by decentralized ledger technologies like blockchain, are being researched or implemented in contexts managing digital interactions between health facilities, patients, and health professionals. In this paper, we propose a blockchain-based solution enabling secure exchange of EHRs between different parties powered by a self-sovereign identity (SSI) wallet and decentralized identifiers. We also make use of a consortium IPFS network for off-chain storage and attribute-based encryption (ABE) to ensure data confidentiality and integrity. Through our solution, we grant users full control over their medical data and enable them to securely share it in total confidentiality over secure communication channels between user wallets using encryption. We also use DIDs for better user privacy and limit any possible correlations or identification by using pairwise DIDs. Overall, combining this set of technologies guarantees secure exchange of EHRs, secure storage, and management along with by-design features inherited from the technological stack.
Activism online: Exploring how crises are communicated visually in activism campaigns

Abstract During the past years, activist movements have increasingly turned to social media to raise awareness and critically discuss current development and future scenarios. As a contribution to the discussion of new social movements and activism in the digital age, this study aims to explore and critically discuss how environmental risks and crisis are visually communicated in activism campaigns on Instagram, through a case study on Extinction Rebellion Sweden (XRsv). The study is delimited to the first 334 posts on XRsv's Instagram between November 14, 2018 and March 1, 2020. Methods used are interview and content analysis. In conclusion, XRsv has adopted aspects of design activism including visual tactics of Informing, Activating and Explaining. These three visual aspects are used by XRsv in an online context enabling interactivity and participatory actions, which in turn forms the core of design activism.
How applicable are attribute-based approaches for human-centered ranking creation?

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future.
DAC: Discriminative Associative Classification

Abstract In this paper, discriminative associative classification is proposed as a new classification technique based on class discriminative association rules (CDARs). These rules are defined based on discriminative itemsets. The discriminative itemset is frequent in one data class and has much higher frequencies compared with the same itemset in other data classes. The CDAR is a class associative rule (CAR) in one data class that has higher support compared with the same rule in other data classes. Compared to associative classification, there are additional challenges as the Apriori property of the subset is not applicable. The proposed algorithm is designed particularly based on well-defined distinguishing characteristics of the rules, to improve the accuracy and efficiency of the classification in data classes. A novel compact prefix-tree structure is defined for holding the rules in data classes. The empirical analysis shows the effectiveness and efficiency of the proposed method on small and large real datasets.
Unitary Evolutions Sourced By Interacting Quantum Memories: Closed Quantum Systems Directing Themselves Using Their State Histories

We propose, formulate and examine novel quantum systems and behavioral phases in which momentary choices of the system's memories interact in order to source the internal interactions and unitary time evolutions of the system. In a closed system of the kind, the unitary evolution operator is updated, moment by moment, by being remade out of the system's `experience', that is, its quantum state history. The `Quantum Memory Made' Hamiltonians (QMM-Hs) which generate these unitary evolutions are Hermitian nonlocal-in-time operators composed of arbitrarily-chosen past-until-present density operators of the closed system or its arbitrary subsystems. The time evolutions of the kind are described by novel nonlocal nonlinear von Neumann and Schr\"odinger equations. We establish that nontrivial Purely-QMM unitary evolutions are `Robustly Non-Markovian', meaning that the maximum temporal distances between the chosen quantum memories must exceed finite lower bounds which are set by the interaction couplings. After general formulation and considerations, we focus on the sufficiently-involved task of obtaining and classifying behavioral phases of one-qubit pure-state evolutions generated by first-to-third order polynomial QMM-Hs made out of one, two and three quantum memories. The behavioral attractors resulted from QMM-Hs are characterized and classified using QMM two-point-function observables as the natural probes, upon combining analytical methods with extensive numerical analyses. The QMM phase diagrams are shown to be outstandingly rich, having diverse classes of unprecedented unitary evolutions with physically remarkable behaviors. Moreover, we show that QMM interactions cause novel purely-internal dynamical phase transitions. Finally, we suggest independent fundamental and applied domains where the proposed `Experience Centric' Unitary Evolutions can be applied natuarlly and advantageously.
Source-Free Multidomain Adaptation With Fuzzy Rule-Based Deep Neural Networks

Unsupervised domain adaptation deals with a task from an unlabeled target domain by leveraging the knowledge gained from labeled source domain(s). Fuzzy system is adopted in domain adaptation to better tackle the uncertainty caused by information scarcity in the transfer. Most existing fuzzy and non-fuzzy domain adaptation methods depend on data-level distribution matching to eliminate domain shift. However, data sharing can trigger privacy concerns. This situation results in the unavailability of source data, wherein most domain adaptation methods cannot be applied. Source-free domain adaptation is then proposed to handle this problem. But existing source-free domain adaptation methods rarely deal with any soft information component due to data imprecision. Besides, fewer methods handle multiple source domains which provide richer transfer information. Thus, in this paper, we propose source-free multi-domain adaptation with fuzzy rule-based deep neural networks (SF-FDN), which takes advantage of a fuzzy system to handle data uncertainty in domain adaptation without source data. To learn source private models with high generality, which is important to collect low noisy pseudo target labels, auxiliary tasks are designed by jointly training source models from multiple domains which share source parameters and fuzzy rules while protecting source data. To transfer fuzzy rules and fit source private parameters to the target domain, self-supervised learning and anchor-based alignment are built to force target data to source feature spaces. Experiments on real-world datasets under both homogeneous and heterogeneous label space scenarios are carried out to validate the proposed method. The results indicate the superiority of the proposed fuzzy rule-based source-free multi-domain adaptation method.
APNet: An All-Frame-Level Neural Vocoder Incorporating Direct Prediction of Amplitude and Phase Spectra

This paper presents a novel neural vocoder named APNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra directly. The APNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a residual convolution network which predicts frame-level log amplitude spectra from acoustic features. The PSP also adopts a residual convolution network using acoustic features as input, then passes the output of this network through two parallel linear convolution layers respectively, and finally integrates into a phase calculation formula to estimate frame-level phase spectra. Finally, the outputs of ASP and PSP are combined to reconstruct speech waveforms by inverse short-time Fourier transform (ISTFT). All operations of the ASP and PSP are performed at the frame level. We train the ASP and PSP jointly and define multilevel loss functions based on amplitude mean square error, phase anti-wrapping error, short-time spectral inconsistency error and time domain reconstruction error. Experimental results show that our proposed APNet vocoder achieves an approximately 8x faster inference speed than HiFi-GAN v1 on a CPU due to the all-frame-level operations, while its synthesized speech quality is comparable to HiFi-GAN v1. The synthesized speech quality of the APNet vocoder is also better than that of several equally efficient models. Ablation experiments also confirm that the proposed parallel phase estimation architecture is essential to phase modeling and the proposed loss functions are helpful for improving the synthesized speech quality.
Trustworthy Sensor Fusion Against Inaudible Command Attacks in Advanced Driver-Assistance Systems

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this article, we propose the multimodal fusion framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio–vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio–vision tasks reveal the model’s uncertainty. Using expected calibration errors, we measure calibration errors and Monte Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for advanced driver assistance systems.
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization With Gaussian Mutation

Human activity discovery aims to cluster human activities without any prior knowledge of what defines each activity. However, most existing methods for human activity recognition are supervised, relying on labeled inputs for training. In reality, it is challenging to label human activity data due to its large volume and the diversity of human activities. To address this issue, this article proposes an unsupervised framework for human activity discovery in 3-D skeleton sequences. The framework includes a data preprocessing step that selects important frames based on kinetic energy and extracts relevant features, such as joint displacement, statistical displacement, angles, and orientation. To reduce the dimensionality of the extracted features, the framework uses principle component analysis. Unlike many other methods for human activity discovery, the proposed framework is fully unsupervised and does not rely on presegmented videos. To segment the time series of activities, the framework uses a sliding time window with some overlapping. The hybrid particle swarm optimization (PSO) with Gaussian mutation and K-means algorithm is then proposed to discover the activities. PSO is chosen for its powerful global search capability and simple implementation. To further improve the convergence rate of PSO, K-means is applied to the outcome centroids from each iteration of PSO. The experimental results on five datasets demonstrate that the proposed framework has superior performance in discovering activities compared to other state-of-the-art methods. The framework achieves an average increase in accuracy of at least 4%.
Features of professional activity of an academic staff in the conditions of a digital educational environment

The fundamental positions of modern education are standardization and modernization, focused on solving the problem of increasing the state competitiveness through the training of competent per- sonnel for the development of the digital economy. Education, as a sector of the economy and social activity, is focused on the widespread use of Internet resources, the expansion of the capabilities of academic staff in the use of digital resources, communication in the digital educational environment, taking into account real economic trends. In the course of theoretical analysis and study of the ed- ucational practice of universities, objective contradictions have been revealed between the require- ments of legal acts regulating the creation of a digital educational environment in universities, and the insufficient development of essential characteristics of professional activity of academic staff in a university digital educational space. The search for a solution to the identified contradiction allows us to formulate the research problem: what are the features of the professional activity of academic staff in a digital educational environment that affect its effectiveness? The purpose of the research is to theoretically substantiate the main areas of professional activity of academic staff in a digital educational environment. The research methodology was based on the study of scientific information and the experience of pedagogical activity in foreign and domestic literature, the use of theoretical and empirical research methods, such as analysis, generalization, deduction. The results of the research are as follows the features of the digital educational environment of a modern university have been substantiated; the structural and content characteristics of the activities of academic staff in the digital space of the university have been disclosed. Key conclusions are: the key role of digital technologies lies in the innovative development of a higher education, namely, in the competence of modern university teachers to work in a dig- ital educational environment; structural and content characteristics of the activity of academic staff in the digital space of a university include a number of areas: interaction with the subjects of education in the electronic information and educational environment of a university, creation of electronic methodological tools for readable disciplines, the use of various forms of organizing training sessions in the electronic educational environment, the creation of a digital portfolio, etc. All this significantly expands the horizons of organizing educational activities in the digital educational space.
Social Media Content Compilation of Online Banking Scams in the Philippines: A Literature Review

Online banking has been proven to be an effective and convenient way of providing banking services anywhere and has been a necessity since the COVID-19 pandemic has affected the Philippines. Alongside this fact, people have been victims of scams involving these online services. Therefore, this paper aimed to analyze reports of online banking scams reported on social media using content analysis. The 26 filtered posts are labeled with 27 codes generated, and the top six were discussed. Specifically, scam (88.46%) was the most common code, followed by phone calls (57.69%) and impersonation (53.84%). Benefits, verifying personal information, and social engineering all had 46.15% occurrences. It is recommended for potential online banking scams to verify with the bank before giving away information, and to raise awareness on the possible technique's scammers use.
The Construction and Validation of Online Project-based Learning Teaching Model

In recent years, with the continuous development of modern information technology, online teaching has become more and more widespread in various schools around the world. In the current online learning environment, students are mostly in independent learning mode, and this online teaching mode is of great significance to carry out online learning in an organized manner, complete learning tasks, and improve the efficiency of online learning. However, there is a lack of emotional communication, cooperative communication, and thinking collision between students and teachers, which is not conducive to the development of students' ability of collaboration and innovation and leads to poor learning effect. Project-based learning is an important way to cultivate students and core competencies in the 21st century. In the process of project-based learning, students' collaborative skills, innovative thinking and critical thinking can be developed to make up for the shortcomings of online teaching mode. Therefore, this study combines junior high school IT curriculum, under the guidance of constructivism, multiple intelligences and other theories, and uses research methods such as survey research and experimental research to construct and verify the online project-based learning teaching model in junior high school IT curriculum in order to enhance students' collaboration, innovation and other abilities, and thus enhance the learning effect.
Efficient and Robust: A Cross-Modal Registration Deep Wavelet Learning Method for Remote Sensing Images

Deep convolutional networks are powerful for local feature learning and have shown advantages in image matching and registration. However, the significant differences between cross-modal images increase the challenge of image registration. The deep network should extract modality-invariant features to identify the matching samples and discriminative features to separate the nonmatching samples. The deep network can extract features invariant to the image modality changes by multiple nonlinear mapping layers. However, it does not inevitably lose rich details and affect the discrimination of features, degrading registration performances. This article proposes a novel deep wavelet learning network (DW-Net) for local feature learning. It incorporates spectral information into deep convolutional features for improving cross-modal image matching and registration. Specifically, this article aims to learn the multiresolution wavelet features through multilevel wavelet transform (WT) and the convolutional network. The cross-modal images are divided into low-frequency and high-frequency parts through WT. DW-Net can adaptively extract the shared features from the low-frequency part and useful details from the high-frequency part, which can enhance the modality invariance and discrimination of features. Additionally, the multiresolution wavelet features contain multiscale information and contribute to improving the matching accuracy. Extensive experiments demonstrate the significant advantages in terms of the accuracy and robustness of DW-Net on cross-modal remote sensing image registration. DW-Net can increase the image patch matching accuracy by 3.7% and improve image registration probability by 12.1%. Moreover, DW-Net shows strong generalization performances from low resolution to high resolution and from optical– synthetic aperture radar to other cross-modal image registration.