Reviewerly/exHarmony huggingface dataset:
An Improved Absolute-Cosine Chaotification Model and Its Simple Application in PRNG

Chaotic systems are widely used in various aspects such as information security, signal processing, and synchronous control. The structural complexity and the chaotic behavior of chaotic systems are two significant factors affecting their practical applications. In this paper, we propose a universal two-dimensional (2D) absolute-cosine chaotic model (ACCM). The 2D-ACCM is composed of a nonlinear bounded cosine function and an absolute value function. It can construct new chaotic maps with simple structures and complex chaotic behaviors on the basis of existing chaotic systems. To verify the effectiveness of the proposed system, we first choose two existing one-dimensional (1D) chaotic maps and one existing 2D chaotic map as the seed maps of the 2D-ACCM to generate two new maps, respectively. The results of chaotic behavior analysis show that these two new maps have more complex chaotic behavior and wider chaotic ranges than seed maps and some advanced chaotic maps. Then a hardware experiment platform based on a field-programmable gate array (FPGA) is used for the hardware implementation of the new maps. Finally, a simple chaos-based pseudo-random number generator (PRNG) is introduced to show the practical application. The experimental results show that the new maps can be easily implemented on the FPGA and the chaos-PRNGs can generate pseudo-random numbers with excellent randomness.
NASTyLinker: NIL-Aware Scalable Transformer-Based Entity Linker

Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.
YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.
Particle Swarm Optimization

In this chapter, a new artificial intelligence optimization tool, a detailed introduction for particle swarm optimization (PSO) is presented here. The basic structure and the main characteristics of PSO algorithm and the multi-objective PSO (MOPSO) algorithm is described and elaborated, and some standard numerical examples for PSO and MOPSO are tested.
Implementing Jastrow-Gutzwiller operators on a quantum computer using the cascaded variational quantum eigensolver algorithm

A Jastrow-Gutzwiller operator adds many-body correlations to a quantum state. However, the operator is nonunitary, making it difficult to implement directly on a quantum computer. We present an implementation of the Jastrow-Gutzwiller operator using the cascaded variational quantum eigensolver algorithm. We demonstrate the method on IBM Q Lagos for a Hubbard model.
Treating Role Assertions as First-class Citizens in Repair and Error-tolerant Reasoning

Errors in Description Logic (DL) ontologies are often detected when reasoning yields unintuitive consequences. The question is then how to repair the ontology in an optimal way, i.e., such that the unwanted consequences are removed, but a maximal set of the unobjected consequences is kept. Error-tolerant reasoning does not commit to a single optimal repair: brave reasoning asks whether the consequence is entailed by some repair and cautious reasoning whether it is entailed by all repairs. Previous research on repairing ABoxes w.r.t. TBoxes formulated in the DL EL has developed methods for computing optimal repairs, and has recently also determined the complexity of error-tolerant reasoning: brave reasoning is in P and cautious reasoning is in coNP. However, in this work the unwanted consequences were restricted to being EL instance assertions. In the present paper, we show that the mentioned results can be extended to a setting where also role assertions can be required to be removed. Our solution is based on a two-stage approach where first the unwanted role assertions and then the unwanted concept assertions are removed. We also investigate the complexity of error-tolerant reasoning w.r.t. classical repairs, which are maximal subsets of the ABox that do not have the unwanted consequences, and show that, in this setting, brave reasoning is NP-complete and cautious reasoning is coNP-complete.
On-Demand Security Framework for 5GB Vehicular Networks

Building accurate Machine Learning (ML) attack detection models for 5G and Beyond (5GB) vehicular networks requires collaboration between Vehicle-to-Everything (V2X) nodes. However, while operating collaboratively, ensuring the ML model's security and data privacy is challenging. To this end, this article proposes a secure and privacy-preservation on-demand framework for building attack-detection ML models for 5GB vehicular networks. The proposed framework emerged from combining 5GB technologies, namely, Federated Learning (FL), blockchain, and smart contracts to ensure fair and trusted interactions between FL servers (edge nodes) with FL workers (vehicles). Moreover, it also provides an efficient consensus algorithm with an intelligent incentive mechanism to select the best FL workers that deliver highly accurate local ML models. Our experiments demonstrate that the framework achieves higher accuracy on a well-known vehicular dataset with a lower blockchain consensus time than related solutions. Specifically, our framework enhances the accuracy by 14 percent and decreases the consensus time, at least by 50 percent, compared to related works. Finally, this article discusses the framework's key challenges and potential solutions.
Multi-Label Feature Selection Via Adaptive Label Correlation Estimation

In multi-label learning, each instance is associated with multiple labels simultaneously. Multi-label data often have noisy, irrelevant, and redundant features of high dimensionality. Multi-label feature selection has received considerable attention as an effective means for dealing with high-dimensional multi-label data. Many multi-label feature selection methods exploit label correlations to help select features. However, finding label correlations and selecting features in existing multi-label feature selection methods are often two separate processes, the existence of noises and outliers in training data makes the label correlations exploited from label space less reliable. Therefore, the learned label correlations may mislead the feature selection process and result in the selection of less informative features. This article proposes a novel algorithm named ROAD, i.e., multi-label featuRe selectiOn via ADaptive label correlation estimation. ROAD jointly performs adaptive label correlation exploration and feature selection with alternating optimization to obtain reliable estimation of label correlations, which can more effectively reveal the intrinsic manifold structure among labels and lead to the selection of a more proper feature subset. Comprehensive experiments on several frequently used datasets validate the superiority of ROAD against the state-of-the-art multi-label feature selection algorithms.
Utilizing business intelligence and digital transformation and leadership to enhance employee job satisfaction and business added value in greater Amman municipality

The goal of this study was to find out how business intelligence systems, AI, and digital leadership affect how satisfied employees are with their jobs and how much value they add to companies in the Greater Amman Municipality. After the study samples were taken and looked at, a total of 246 samples were approved to be used in the PLS software-based analysis. The results of this study showed that putting in place business intelligence tools, artificial intelligence, and digital leadership all made employees happier with their jobs and gave businesses more value. The research showed that there are four key parts to digital leadership: commander, communicator, collaborator, and co-creator. The main parts of business intelligence are Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. Findings show that digital transformation is made up of three key parts: changing processes, developing business models, and changing domains. The results also show that an employee's level of job satisfaction, which includes things like business success, work commitment, and job thinking, is linked to how much value they add to the company. Intriguingly, the current results go against those of earlier studies, which said that the variables of interest have no effect on how happy employees are with their jobs or how much value companies add for their customers. When the results of this study are looked at as a whole, they say that businesses should start doing things that make employees happier at work and increase the value of the business. The current study is innovative because it focuses on the most important parts of business intelligence, artificial intelligence, and digital leadership in order to improve employee satisfaction at work and the quality of business learning with added value in Greater Amman Municipality.
Improving Inconspicuous Attributes Modeling for Person Search by Language

Person search by language aims to retrieve the interested pedestrian images based on natural language sentences. Although great efforts have been made to address the cross-modal heterogeneity, most of the current solutions suffer from only capturing salient attributes while ignoring inconspicuous ones, being weak in distinguishing very similar pedestrians. In this work, we propose the Adaptive Salient Attribute Mask Network (ASAMN) to adaptively mask the salient attributes for cross-modal alignments, and therefore induce the model to simultaneously focus on inconspicuous attributes. Specifically, we consider the uni-modal and cross-modal relations for masking salient attributes in the Uni-modal Salient Attribute Mask (USAM) and Cross-modal Salient Attribute Mask (CSAM) modules, respectively. Then the Attribute Modeling Balance (AMB) module is presented to randomly select a proportion of masked features for cross-modal alignments, ensuring the balance of modeling capacity of both salient attributes and inconspicuous ones. Extensive experiments and analyses have been carried out to validate the effectiveness and generalization capacity of our proposed ASAMN method, and we have obtained the state-of-the-art retrieval performance on the widely-used CUHK-PEDES and ICFG-PEDES benchmarks.
Dual Distillation Discriminator Networks for Domain Adaptive Few-Shot Learning

Domain Adaptive Few-Shot Learning (DA-FSL) aims at accomplishing few-shot classification tasks on a novel domain with the aid of a large number of source-style samples and several target-style samples. It is essential for DA-FSL to transfer task knowledge from the source domain to the target domain and overcome the asymmetry amount of labeled data in both domains. To this end, we propose Dual Distillation Discriminator Networks (D3Net) from the perspective of the lack of labeled target domain style samples in DA-FSL. Specifically, we employ the idea of distillation discrimination to avoid the over-fitting caused by the unequal number of samples in the target and source domains, which trains the student discriminator by the soft labels from the teacher discriminator. Meanwhile, we design the task propagation stage and the mixed domain stage respectively from the level of feature space and instances to generate more target-style samples, which apply the task distributions and the sample diversity of the source domain to enhance the target domain. Our D3Net realizes the distribution alignment between the source domain and the target domain and constraints the FSL task distribution by prototype distributions on the mixed domain. Extensive experiments on three DA-FSL benchmark datasets, i.e., mini-ImageNet, tiered-ImageNet, and DomainNet, demonstrate that our D3Net achieves competitive performance.
Conversational AI: An Explication of Few-Shot Learning Problem in Transformers-Based Chatbot Systems

With the recent advancements in conversational artificial intelligence (AI), the practical applications of chatbots have risen significantly in diverse domains such as healthcare, education, e-government, customer support systems, social platforms, and entertainment. The chatbot converses with humans in natural language and responds to their queries with precise and relevant answers. The relevant literature presents several methods for intent classification and slot mapping for chatbot question answering. However, the existing chatbot architectures still suffer from few-shot learning problem (imbalance ratio of class labels over samples) and ineffective dialog management to retain the context and slots mapping. This study aims to introduce the architecture of chatbot by focusing few-shot learning problem and context management in dialog-based conversations. First, to mitigate the few-shot learning problem with chatbots, we propose a novel hybrid intent and slots transformers (HIST) model. The HIST chatbot architecture utilizes transformers and self-attention mechanisms along with bigated recurrent unit and combines conditional random field algorithms for intent classification and slot extraction. Second, to address dialog management, we introduce a hybrid interaction strategy for slots mapping and effective conversational context management. To validate the proposed model’s effectiveness, comprehensive empirical analysis is carried out using three benchmark datasets including airline travel information system (ATIS), banking77, and conversational language interface for natural conversation 150 (CLINC150). The results show that HIST outperforms against the state-of-the-art existing methods with a clear margin and obtained an accuracy of 94.89% and 96.17% for intent classification and slots extraction, respectively. The empirical results confirm the effectiveness of the HIST chatbot for resolving the few-shot learning problem with effective dialog management in chatbot systems.
GlassesValidator: A data quality tool for eye tracking glasses

Abstract According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills.
HOW SHOULD WE PROTOTYPE? ESTABLISHING THE AFFORDANCES OF PROTOTYPING MEDIA AND APPROACHES

Abstract The breadth of media and approaches used when prototyping are vast, with each holding inherent properties that vary their suitability for a given prototyping activity. While several have established classifications of types and purposes of prototypes, there is little by way of guidance for designers on how select and strategise prototyping given their activity needs, or how the prototype chosen may influence their process, success or efficiency. This paper presents nine affordances of prototypes derived from literature, together characterising the properties of prototyping media or approaches that affect their suitability across prototyping activities. The affordances are illustrated through application to physical and digital classes of prototypes and four real prototype cases, showing descriptive capability, inherent differences between the media, and enabling direct and consistent comparison. By mapping affordances across many media and approaches, this work enables better method selection to align with activity needs, better description and comparison of media and approaches, and the ability the broadly interrogate and direct future development of prototyping technologies.
Digital Hate Speech Experiences Across Age Groups and Their Impact on Well-Being: A Nationally Representative Survey in Switzerland

The growing challenge of digital hate speech requires an understanding of its complexity, scale, and impact. Research on experiencing digital hate speech has so far been limited to the roles of personal victim, observer, and perpetrator, with a focus on young people. However, research on hate crimes suggests that vicarious victimization may also be relevant due to its negative impacts. In addition, the lack of knowledge about the older generation neglects the fact that older people are increasingly seen as vulnerable to digital risks. Therefore, this study introduces vicarious victimization as an additional role in research on digital hate speech. Prevalence rates for the four roles are examined across the life span, using a nationally representative sample of adult Internet users in Switzerland. Additionally, all roles are correlated with life satisfaction and loneliness, two stable indicators of subjective well-being. The results show that in this national population, personal victimization and perpetration are less common (<7 percent), whereas observation and vicarious victimization are more common (>40 percent). Prevalence decreases with age in all roles. As expected, multivariate analyses show that both forms of victimization are negatively related to life satisfaction and positively related to loneliness, with these effects being stronger for personal victimization. Similarly, being an observer and being a perpetrator correlate negatively, but not significantly, with well-being. This study contributes to a theoretical and empirical distinction between personal and vicarious victims and provides insight into their effects on well-being in a population largely unexplored in terms of age and national representativeness.
Deep learning assisted non-contact defect identification method using diffraction phase microscopy

Reliable detection of defects from optical fringe patterns is a crucial problem in non-destructive optical interferometric metrology. In this work, we propose a deep-learning-based method for fringe pattern defect identification. By attributing the defect information to the fringe pattern’s phase gradient, we compute the spatial phase derivatives using the deep learning model and apply the gradient map to localize the defect. The robustness of the proposed method is illustrated on multiple numerically synthesized fringe pattern defects at various noise levels. Further, the practical utility of the proposed method is substantiated for experimental defect identification in diffraction phase microscopy.
Multicore fiber beacon system for reducing back-reflection in satellite quantum key distribution

Optical beaconing is an important part of the acquisition, pointing and tracking system necessary for free-space quantum key distribution (QKD). However, uplink beacon back-reflections from the receiver architecture can result in noise. Wavelength- and time-division multiplexing has been used, but neither is yet sufficient to make back-reflection negligible. The use of additional telescopes increases complexity and pointing error. Here, we propose the use of a 2-by-2 multicore fiber, to act as an optical uplink beacon source. This spatially separates the QKD channel and optical uplink beacon. Up to 50 dB improvement in noise rejection over a purely spectrally divided system was demonstrated. The route to further improvements through greater fiber core separation is described. Beacon systems designed in this way could provide a combination of reduced complexity and improved noise performance to free-space and satellite QKD and optical communications.
Ultrafast Temporal SU(1,1) Interferometer

Interferometers are highly sensitive to phase differences and are utilized in numerous schemes. Of special interest is the quantum SU(1,1) interferometer which is able to improve the sensitivity of classical interferometers. We theoretically develop and experimentally demonstrate a temporal SU(1,1) interferometer based on two time lenses in a $4f$ configuration. This temporal SU(1,1) interferometer has a high temporal resolution, imposes interference on both time and spectral domains, and is sensitive to the phase derivative which is important for detecting ultrafast phase changes. Therefore, this interferometer can be utilized for temporal mode encoding, imaging, and studying the ultrafast temporal structure of quantum light.