Reviewerly/exHarmony huggingface dataset:
A lightweight convolutional neural network to reconstruct deformation in BOS recordings

Abstract We introduce a Convolutional Neural Network (CNN) that is specifically designed and trained to post-process recordings obtained by Background Oriented Schlieren (BOS), a popular technique to visualize compressible and convective flows. To reconstruct BOS image deformation, we devised a lightweight network () that has comparatively fewer parameters to train than the CNNs that have been previously proposed for optical flow. To train , we introduce a novel strategy based on the generation of synthetic images from random-irrotational deformation fields, which are intended to mimic those provided by real BOS recordings. This allows us to generate a large number of training examples at minimal computational cost. To assess the accuracy of the reconstructed displacements, we consider test cases consisting of synthetic images with sinusoidal displacement as well as images obtained in the experimental studies of a hot plume in air and a flow past and inside a heated hollow hemisphere. By comparing the reconstructed deformation fields using the or conventional post-processing techniques used in Direct Image Correlation () or conventional image cross-correlation, we show that is more accurate and robust in the synthetic test case. When applied to experimental BOS recordings, all methods provide similar and consistent deformation fields. As is capable of achieving a comparable or better accuracy at a fraction of the computational costs, it represents a valuable alternative to conventional post-processing techniques for BOS experiments.
ADE-CycleGAN: A Detail Enhanced Image Dehazing CycleGAN Network

The preservation of image details in the defogging process is still one key challenge in the field of deep learning. The network uses the generation of confrontation loss and cyclic consistency loss to ensure that the generated defog image is similar to the original image, but it cannot retain the details of the image. To this end, we propose a detail enhanced image CycleGAN to retain the detail information during the process of defogging. Firstly, the algorithm uses the CycleGAN network as the basic framework and combines the U-Net network's idea with this framework to extract visual information features in different spaces of the image in multiple parallel branches, and it introduces Dep residual blocks to learn deeper feature information. Secondly, a multi-head attention mechanism is introduced in the generator to strengthen the expressive ability of features and balance the deviation produced by the same attention mechanism. Finally, experiments are carried out on the public data set D-Hazy. Compared with the CycleGAN network, the network structure of this paper improves the SSIM and PSNR of the image dehazing effect by 12.2% and 8.1% compared with the network and can retain image dehazing details.
Hate speech on social media against German mayors: Extent of the phenomenon, reactions, and implications

Abstract Drawing on an online survey of mayors from the German federal state of Baden‐Wuerttemberg this article analyses (1) to what extent social media is used abusively at the municipal level, (2) how mayors react to hate speech and (3) whether the experience of such insulting comments and threats is correlated with their perception of social media as a tool for political communication. The analysis shows that hateful comments are part of the social media experience on the local level. 53.9% of the mayors have at least once experienced personal insults and hostilities on social media in their role as politicians. The majority of them (56.7%) ignored the hate speech without reaction. Significantly fewer reacted more actively: 29.8% responded to the insulting message, 5.4% blocked the hater, and 8.2% filed a judicial complaint. The statistical analysis shows that mayors who use social media in a more professional way are more likely to react to hate speech in such an active manner. Moreover, the experience of hate speech and particularly an active type of reacting are found to be positively related to mayors' perceptions of how useful they consider social media to be for political communication.
FIRST-ORDER RELEVANT REASONERS IN CLASSICAL WORLDS

Abstract Sedlár and Vigiani [18] have developed an approach to propositional epistemic logics wherein (i) an agent’s beliefs are closed under relevant implication and (ii) the agent is located in a classical possible world (i.e., the non-modal fragment is classical). Here I construct first-order extensions of these logics using the non-Tarskian interpretation of the quantifiers introduced by Mares and Goldblatt [12], and later extended to quantified modal relevant logics by Ferenz [6]. Modular soundness and completeness are proved for constant domain semantics, using non-general frames with Mares–Goldblatt truth conditions. I further detail the relation between the demand that classical possible worlds have Tarskian truth conditions and incompleteness results in quantified relevant logics.
Implicit Bias of Deep Learning in the Large Learning Rate Phase: A Data Separability Perspective

Previous literature on deep learning theory has focused on implicit bias with small learning rates. In this work, we explore the impact of data separability on the implicit bias of deep learning algorithms under the large learning rate. Using deep linear networks for binary classification with the logistic loss under the large learning rate regime, we characterize the implicit bias effect with data separability on training dynamics. From a data analytics perspective, we claim that depending on the separation conditions of data, the gradient descent iterates will converge to a flatter minimum in the large learning rate phase, which results in improved generalization. Our theory is rigorously proven under the assumption of degenerate data by overcoming the difficulty of the non-constant Hessian of logistic loss and confirmed by experiments on both experimental and non-degenerated datasets. Our results highlight the importance of data separability in training dynamics and the benefits of learning rate annealing schemes using an initial large learning rate.
RNS processor using moduli sets of the form 2<sup><i>n</i></sup>±1

Summary In recent years, research on residue number systems (RNS) has targeted larger dynamic ranges to explore their inherent parallelism further. In this paper, we start from the traditional 3‐moduli set , with an equivalent 3 ‐bit dynamic range, and propose balanced horizontal extensions by using modulus of the form and to scale the dynamic range and enhance the parallelism according to the requirements. A method to design the reverse conversion by using it is also presented. A case study of multiplier‐accumulator (MACs) in cascade for filtering applications has been analyzed to validate the RNS processor using the proposed moduli set and reverse conversion architecture. Experimental results show that the proposed approaches achieve a significant speedup compared with the state‐of‐the‐art cases for the same dynamic range purposes.
Investigating the Individual Performances of Coupled Superconducting Transmon Qubits

The strong requirement for high-performing quantum computing led to intensive research on novel quantum platforms in the last decades. The circuital nature of Josephson-based quantum superconducting systems powerfully supports massive circuital freedom, which allowed for the implementation of a wide range of qubit designs, and an easy interface with the quantum processing unit. However, this unavoidably introduces a coupling with the environment, and thus to extra decoherence sources. Moreover, at the time of writing, control and readout protocols mainly use analogue microwave electronics, which limit the otherwise reasonable scalability in superconducting quantum circuits. Within the future perspective to improve scalability by integrating novel control energy-efficient superconducting electronics at the quantum stage in a multi-chip module, we report on an all-microwave characterization of a planar two-transmon qubits device, which involves state-of-the-art control pulses optimization. We demonstrate that the single-qubit average gate fidelity is mainly limited by the gate pulse duration and the quality of the optimization, and thus does not preclude the integration in novel hybrid quantum-classical superconducting devices.
Introduction to Special Issue—Qualitative Inquiry in the 20/20s: Exploring Methodological Consequences of Digital Research Workflows

This article offers an overview of a special issue that focuses on the reciprocal relationship between the digital tools and spaces that we use and the methodologies and methods that we take up in designing and carrying out a qualitative research study. In this editorial, we situate the special issue in the methodological literature around technologies and qualitative inquiry. We also provide an overview of the seven included articles, noting how each article offers new perspectives on the consequences of adopting digital research workflows.
Identifikation der Anwendungsmöglichkeiten von KI-Algorithmen in Requirements Engineering Prozessen

Abstract Managing an ever-growing number of requirements is a time consuming and highly complex activity in product development projects. With the rise of Artificial Intelligence (AI) in the last decades, algorithms are now capable of supporting Requirements Engineering (RE) tasks. The algorithm’s ability to intelligently process natural language data as well as the application in RE have been published extensively. In established RE processes however, it is often unclear, where AI algorithms can be utilized. The result is a significant imbalance between state-of-the-art AI algorithms and their application rates in real RE processes. One of the reasons is, that current models of RE processes aren’t able to convey the needed information for the identification of suitable tasks. Therefore, this paper aims to provide a framework of finite and standardized process steps with a data processing-oriented perspective, that can be used to systematically identify the points in the RE processes where AI algorithms can be applied. With the standardized process steps of this framework, established RE processes can be abstracted and expressed in a way that is compatible with the scope of AI algorithms. Due to the finite number of standardized process steps, libraries of suitable AI algorithms for the defined process steps can be built, so that their applicability can be efficiently assessed. The presented framework was developed collaboratively during a research project with industry participation.
Improved Multi-Strategy Harris Hawks Optimization and Its Application in Engineering Problems

In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population is initialized by Sobol sequences to increase the diversity of the population. Second, the elite opposition-based learning strategy is incorporated to improve the versatility and quality of the solution sets. Furthermore, the energy updating strategy of the original algorithm is optimized to enhance the exploration and exploitation capability of the algorithm in a nonlinear update manner. Finally, the Gaussian walk learning strategy is introduced to avoid the algorithm being trapped in a stagnant state and slipping into a local optimum. We perform experiments on 33 benchmark functions and 2 engineering application problems to verify the performance of the proposed algorithm. The experimental results show that the improved algorithm has good performance in terms of optimization seeking accuracy, the speed of convergence, and stability, which effectively remedies the defects of the original algorithm.
shinyNORRRM: A Cross-Platform Software to Calculate the CIPW Norm

Abstract In this paper, a novelty-free software to assess an efficient CIPW Norm (± 0.006 wt.% in differences between input and output data) is presented. The package is available in the official repository for user-contributed R packages (CRAN: Comprehensive R Archive Network). The software is able to handle big data sets and considers minor and trace element compositions. The algorithm can calculate odd minerals in igneous rocks, such as cancrinite and calcite, adjust the Fe +3 /Fe +2 ratio in different standard approaches, and recalculate the compositions of the rocks in an anhydrous basis (100 ± 0.003 wt.% volatile-free adjusted). Furthermore, the package calculates several petrological parameters, and the graphical outputs are displayed following IUGS scheme standards. The prime aspect of shinyNORRRM is the symbiosis of native R functions with the R package’s shiny (Web Application Framework for R) to run the norm in a user-friendly interface. shinyNORRRM can be executed in any operating system and requires no previous programming knowledge, thus promising to be the universal computational program in this matter. The output data are printed in the standard comma-separated values (*.csv) format, which is highly compatible with general spreadsheet editors. In this work, the algorithm of our program is validated using already compiled whole-rock geochemical databases.
SME Performance Through Blockchain Technologies

Blockchain technology has the potential to impact the performance of small-and-medium enterprises (SMEs). A theoretical model using Technology-Organization-Environment framework and Resource-based View was developed to examine Blockchain-driven SME business performance. The model was empirically tested using structural equation modeling techniques on data gathered from 320 practitioners in Nigeria, Ghana and Kenya. The technological and organizational contexts for Blockchain influenced organizational commitment while top management support for Blockchain influenced organizational transparency, both of which influenced Smart SMEs resulting in enhanced performance. Social media visibility moderated the effect of Smart SMEs on performance. The findings offer several implications for research and practice.
Design and Implementation of Smart Wheelchair with Advanced Control Interfaces

As wheelchair users demand support and assistance from others in order to travel, the study intends to assist physically impaired persons with their mobility, particularly those who have lost their capacity to use their legs due to paralysis, an accident, or old age, as well as those who have feeling issues. The proposed project would provide wheelchair users with location-based services using one-finger control, voice control, and GPS tracking. In comparison to the expensive imported wheelchairs now on the market, the technology will be more affordable and user-friendly. With the support of a user command, the intelligent wheelchair gadgets will be able to maneuver themselves. Users will exert less effort because to the effortless mobility. The project will primarily use an E-bike motor and motor driver, an Arduino, a GPS module, and a solar panel. The voice control signal and manual control utilizing a joystick are used by the motor driver to operate. The Arduino includes a voice-activated device for physically challenged persons who are unable to control their hand motions. Family members will be able to trace the precise position in the case of emergencies due to the usage of GPS. The system will use a solar panel in addition to it to charge the battery. As an additional security measure, an emergency power OFF (EPO) switch is included. This switch is intended to turn the system off in an emergency or when it cannot be switched off normally. Using Porteous, the project simulation is carried out.
Potential of eXtended Intelligence (XI) for Extending Human Expression from Digitization of Analog Elements

In recent years, AI technology has developed dramatically and become more versatile. AI technology has been integrated into every aspect of our lives. Many people use AI frequently without being aware that they are using AI. The use of AI has led to the digitization of every analog tool in society, enriching our cultural life. Old antique-like machines are digitized and reproduced as software, and AI algorithms clean up and reproduce analog devices that have slipped in their positions as relics of the past. The proliferation of AI and the digitization of content and tools have rapidly in-creased and expanded the convenience of our lives. On the other hand, digital materials are easy to copy and it is difficult to guarantee originality. It is no exaggeration to say that the value of content is equal to its rarity, which is the presence or absence of guaranteed originality. In this sense, our AI digital society is causing the loss of originality and the decreasing the value of content itself in exchange for convenience. For creators there is no difference in the effort required to produce content, whether digital or analog. However, as long as content is digital, creators’ efforts will not be rewarded. These days, NFTs can provide a guarantee of originality and uniqueness to digital content. However, the daily development of AI technology will lead to the problem of the originality of the AI itself, which produces digital content. Is there any originality in the AI itself that produces content with guaranteed originality? As AI is a computer program, an algorithm, it is ultimately nothing more than source code. It is easy to copy and mass-produce AI that produces content with originality. Given these issues, this paper focuses on XI as a post-AI concept, introduces the authors’ efforts, and discusses XI’s potential and importance.
Object-Based Active Inference

The world consists of objects: distinct entities possessing independent properties and dynamics. For agents to interact with the world intelligently, they must translate sensory inputs into the bound-together features that describe each object. These object-based representations form a natural basis for planning behavior. Active inference (AIF) is an influential unifying account of perception and action, but existing AIF models have not leveraged this important inductive bias. To remedy this, we introduce 'object-based active inference' (OBAI), marrying AIF with recent deep object-based neural networks. OBAI represents distinct objects with separate variational beliefs, and uses selective attention to route inputs to their corresponding object slots. Object representations are endowed with independent action-based dynamics. The dynamics and generative model are learned from experience with a simple environment (active multi-dSprites). We show that OBAI learns to correctly segment the action-perturbed objects from video input, and to manipulate these objects towards arbitrary goals.
Deep Fake Generation and Detection: Issues, Challenges, and Solutions

Detection of fake audio and video is a challenging problem. Deepfake is frequently used for creating fake audios and videos using deep learning techniques. Deepfakes, artificially created audiovisual interpretations can be used in many different ways; such as, damaging the repute of a celebrity, misinformation or hate speech, and it may lead to chaos in the society. Therefore, deepfake detection is of utmost important. This article presents an overview of deepfake detection models and datasets, challenges and opportunities in current methods, and provides some possible solutions. This article is mainly focused on multimodal data modalities to detect audio–visual fakes.
TransH-RA: A Learning Model of Knowledge Representation by Hyperplane Projection and Relational Attributes

The TransE model plays a key role in dealing with data sparsity and promotes the development of knowledge graphs completion. However, TransE has some difficulties in dealing with one-to-many, many-to-one, many-to-many and transmission relationships. In order to solve this problem, this paper proposes a knowledge representation learning model based on hyperplane projection and relational attributes, namely TransH-RA. First of all, we introduce the idea of hyperplane projection based on the TransE model, this idea is inspired by TransH, which makes different entities have different roles in a specific relationship, thus reducing the constraints of TransE translation rules, and map the head entity h and tail entity t to the plane of special relation r; Secondly, considering that it is not easy to identify different similar entities, the neighborhood information of entities is added to learn the neighborhood of entities around different entities; Then, in order to further strengthen the ability to deal with complex relationships, attribute features of relationships are added and attribute knowledge is embedded; Eventually, during the training of the model, the probability method is chosen to replace the head and tail entities. Link prediction experiments are conducted on the public datasets FB15K and WN18, and the triple classification experiments on the datasets WN11, FB13 and FB15K are carried out to analyze and verify the effectiveness of the proposed method. The evaluation results show that our method achieves the better results on MeanRank, Hits@10 and ACC indicators compared with TransE and TransH.
Application of Generalized Inverses in the Minimum-Energy Perfect Control Theory

Application of generalized inverses in solving the inverse model control (IMC)-oriented minimum-energy perfect control design (PCD) problem for linear time-invariant multi-input/multi-output systems governed by the discrete-time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$</tex-math> </inline-formula> -state-space structure is presented in this article. For this reason, an appropriate class of polynomial generalized inverses is investigated. Moreover, it can be stated that the nonunique right <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma$</tex-math> </inline-formula> -inverse, based on properly selected so-called degrees of freedom (DOFs), outperforms the well-known unique Moore–Penrose (MP) minimum-norm right <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$</tex-math> </inline-formula> -inverse in terms of the energy consumption of perfect control (PC) input signals. However, the analytical confirmation of such an intriguing statement has only been established for the special class of the single-delayed plants with a zero reference value. Moreover, because of the complexity of the IMC, the objects with a time delay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d&gt;1$</tex-math> </inline-formula> having a nonzero setpoint have never been analytically explored in regard to the PC energy context until now. Thus, the newly introduced analytical methods defined in this article allow us to designate the proper forms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma$</tex-math> </inline-formula> -inverse-related DOFs that guarantee the minimum-energy PCD for the entire set of LTI multivariable nonsquare systems with the delay <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d \geq 1$</tex-math> </inline-formula> . Moreover, the new original results, supported by numerical examples, strongly contest the well-established control and systems theory canons related to the optimal minimum-energy-originated peculiarity of the MP pseudoinverse.