Reviewerly/exHarmony huggingface dataset:
“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field

&lt;p&gt;In 2019, Competition Bureau Canada (“the Bureau”) sent letters to approximately 100 brands and marketing agencies that engage in influencer marketing—advising them to ensure that their marketing practices are in compliance with the law. Against this background, this research uses semi-structured interviews to examine the regulatory compliance efforts of 21 influencer intermediaries who liaise between brands and social media influencers. The research assesses these intermediaries’ disclosure practices together with their thoughts on the Bureau’s targeted outreach and law enforcement in the field. Additionally, the research explores how the intermediaries adjust their regulatory compliance efforts to technological innovation on social media platforms.&lt;/p&gt; &lt;p&gt;This research contributes four main findings. First, intermediaries consign regulatory expectations for disclosure to a secondary level of importance when working to meet the needs of brands and influencers. Second, there is limited intermediary knowledge of the Bureau’s legal standing and activities in the Canadian influencer field. Third, there are various algorithmic (e.g., algorithmic deprioritization) and non-algorithmic (e.g., lengthy disclosure processes) challenges to maintaining high standards of compliance in evolving digital environments. Finally, intermediaries have access to forms of social, cultural, and technical power that can be maximized to influence high standards of compliance. This research contributes to a growing body of scholarship focused on the perspectives of professionals who manage influencer marketing collaborations.&lt;/p&gt;
Predicting Student Retention via Expectancy Value Theory Using Data Gathered before the Semester Begins

Prior to joining UofL, he worked as a Lecturer in his home country (Brazil) for three years, teaching and mentoring low-income, first-generation STEM students from rural communities.Additionally, Dr. Dourado worked as an instructor at the University of Central Florida for two years, primarily serving Hispanic first-generation
Board 166: Experiences from ImageSTEAM Workshop for the Middle School (Work In Progress)

The rise of computing and artificial intelligence (AI) will transform our society and it is clear that students will be forced to engage with AI in their careers.Currently, the United States lacks adequate infrastructure or capacity to support the teaching of AI in the K-12 curriculum.To address these challenges, we introduce the use of visual media as a key bridge technology to engage students in grades 6-8 with AI topics, through a recent NSF funded ITEST program, labeled ImageSTEAM.Specifically, we focus on the idea of a computational camera, which rethinks the sensing interface between the physical world and intelligent machines and enables students to ponder how sensors and perception fundamentally will augment science and technology in the future.Two workshops have been conducted, the first in summer 2021, and the second in summer 2022.The first workshop was delivered virtually due to the persistent COVID-19 environment at the time.The second workshop had one week conducted in-person and the second week was conducted virtually.Teachers and students participated in the workshops and their experiences will be shared and discussed at the conference.In addition, teacher use of the skills and knowledge learned from the workshops will be shared.
Optimization of Multi-target Continuous Dynamic Trajectory for Unmanned Aerial Vehicles

In the problem of trajectory optimization for unmanned aerial vehicles (UAVs) passing through multiple target points, continuous velocity and acceleration in the process of optimization are essential for maintaining a feasible trajectory. This paper proposes a multi-segment holistic Bezier shaping method (MHBSM) for dynamic trajectory optimization. The MHBSM not only achieves holistic optimization of UAVs passing through a multi-target trajectory but also ensures high-order continuity at the junctions of trajectory segments. Unlike methods that generate non-dynamic trajectories by connecting arcs and straight lines (such as Dubins curves) without considering speed changes, MHBSM optimizes the trajectory while taking into account the dynamics of UAVs. The method optimizes the differential flat output space of a fixed-wing UAV dynamic model and transforms the dynamic optimization problem into a three-dimensional position optimization problem. The problem is then further transformed into an optimization of control points using the Bezier curve. The resulting trajectory satisfies the dynamic constraints. In comparison to the Gaussian pseudospectral method (GPM), MHBSM achieves similar optimization results with only a 2.58% difference, while requiring approximately 1.36% of the computational time on average. The MHBSM demonstrates excellent convergence performance while satisfying the constraints of continuous acceleration and obstacle avoidance.
Protecting quantum correlations of negative quantum states using weak measurement under non-Markovian noise

Abstract The weak measurement (WM) and quantum measurement reversal (QMR) are crucial in protecting the collapse of quantum states. The idea of WM and QMR has recently been used to protect and enhance quantum correlations and universal quantum teleportation (UQT) protocols. Here, we study the quantum correlations, maximal fidelity, and fidelity deviation of the two-qubit negative quantum states developed using discrete Wigner functions with(without) WM and QMR. To take into account the effect of a noisy environment, we evolve the states via non-Markovian amplitude damping and random telegraph noise quantum channels. To benchmark the performance of negative quantum states, we calculate their success probability. We compare our results with the two-qubit maximally entangled Bell state. Interestingly, we observe that some negative quantum states perform better with WM and QMR than the Bell state for different cases under evolution via noisy quantum channels.
Entanglement-based quantum digital signatures over deployed campus network

The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today's digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.
Managing and Billing Software for Hypermarket

It previews the whole system design while in the free demo session and clear your queries by experiencing Optech’s digital dashboard. In the navigation system, we optimize the UI to be straightforward and comprehend for all sets of people so the employees grasp this entire operation. The Optech software has been developed with VB.net and MY SQL Server. The system is efficient in generating reports, which will help in the maintenance of the hypermarket easily. This Optech software can store a lot of records in the database. It’s a user-friendly software, which can be used for service, sales and report. It has an unlimited storage and application can be used in both online and offline. Reports for billing can be generated by data wise or pricewise.
Emerging Innovations and Global Challenges on Curriculum Design: Case Study of Teaching Security in Embedded System Design

Abstract Emerging innovations and global challenges are not only overwhelming human health and the environment but also complicating the training of the next generations of the workforce that will shape the future of the global economy. In particular, a topic of immediate concern is the security of embedded systems. Embedded systems are ubiquitous and are found in medical devices, nuclear plants, and chemical processing systems to name a few applications. The ever-increasing complexity of modern embedded systems has raised the potential for security breaches that may compromise the integrity and functions of these products. Although embedded systems design has been a core curriculum in most electrical and computer engineering programs, incorporating security-training skills at the undergraduate level remains challenging. Despite the enormity of security challenges, they are often ignored during the initial design and development period of an embedded system, thus leaving many devices vulnerable. We postulate that this design shortcoming is perhaps due, in part, to the teaching inadequacy of embedded security in early engineering education. In this paper we propose a framework to design an undergraduate curriculum that will incorporate security concepts in embedded systems courses. The curriculum will create new learning materials and teaching strategies that will focus on security concepts in early phases of embedded systems design. Because it is almost impossible to add security to an existing embedded system, the significance of this paper is to enforce life-learning awareness of security concerns in embedded systems design in early training of undergraduate students. The undergraduate curriculum will integrate security concerns, challenges, and concepts in all aspects of embedded systems design. We propose new learning materials and teaching strategies that will focus on securing the hardware and software programming of the central processing unit and its peripherals at the low-level layer allowing the upper-level layer to implement security standards with a greater efficiency. We will also provide advanced concepts that mitigate security breaches either by low-level encryption solutions or dead-end program destruction. The long-term goal of this paper is to promote security awareness in embedded systems at the undergraduate level.
Cybersecurity Issues in Crowdsourcing Engineering Initiatives

Abstract Cybersecurity Issues in Crowdsourcing Engineering Initiatives As crowdsourced engineering-related projects become more wide-spread and global, we must pay attention to cybersecurity issues that can emerge. Sharing data is fundamental to crowdsourcing, and that requires attention to protecting privacy. This issue is exacerbated if participants use common technologies, e.g., cell phones, that are used for other purposes and may be subject to cybersecurity vulnerabilities. Cybersecurity issues can arise during recruitment, by which social media is a prevalent tool, to projects in that bad actors can solicit input into malicious and false projects. Furthermore, the results of crowdsourced engineering projects may provide foundations for policy and legislation; thus, integrity must be maintained and bad actors prevented from manipulating data and/or results. Governance of crowdsourced projects must include attention to cybersecurity issues, regardless of their scope and scale. The exploitation of cybersecurity vulnerabilities are often result from ethical lapses. Just as engineers respect engineering professional ethics, so must citizens who participate in gathering data. In this paper, we will share a taxonomy that identifies how professional engineering ethics are represented and brought to life in 21st century crowdsourced engineering projects, which include commercial initiatives from Anheuser-Busch, Nokia, and FedEx as well as research initiatives like the Digital Humanitarian Network, the Humanitarian OpenStreetMap Team, and KeralaRescue.
Improving the Learning Experience of Neurodiverse Students in a Fluid Mechanics Course During the COVID-19 Pandemic

Abstract The goals of this study were (a) to assess whether the adoption of a comprehensive suite of inclusive academic interventions within a large engineering course (Fluid Mechanics) would increase the engagement and learning outcomes of neurodivergent students enrolled in the course and (b) to observe student perceptions of program activities with the end goal of determining whether or not students perceived the program interventions to be helpful. The course interventions were conceived and implemented within the broader context of a research project that aims to transform the academic practices within an engineering department in the Northeast United States to embrace the unique strengths of neurodiverse students and improve the educational experience for all students. Over the course of the semester, three surveys were handed out to students on a voluntary basis. Of a class of 52, 50, 48, and 39 participant responses were received for the three surveys, respectively. Each survey included several multiple-choice (or T/F) and open-ended questions, including aspects related to the instructor effectiveness, the course activities and assessments, and the student experience. Students were also asked whether they identify as neurodivergent during the first survey. After the course was completed, the analysis of survey responses was conducted using a qualitative research protocol. First, mapping of survey questions with the goals of the study was done, then student responses were organized in a matrix and underwent various rounds of coding. The process of coding is an iterative process that results in a refinement of label codes which can result in the identification of emerging themes. Some of the emerging themes that were identified by the students as barriers were the difficulty of exams, advanced mathematics content, and course mode of delivery that was hybrid due to the pandemic. Themes identified as supports were alternate exam modalities, course content and delivery, lecture videos, TA/recitation videos, lab videos, and live-streamed lectures. It was found that on several occasions that students who identify as neurodivergent reported supports and challenges differently than the remaining students who responded to the survey. At the beginning of the semester, only 29% of neurodiverse student responses expressed a positive outlook on how effective the learning experience would be in this course. Interestingly, as the course progressed, 71% of neurodiverse student responses indicated that the course was providing an effective learning experience for them.
Sentiment Analysis Through the Application of Machine Learning Algorithms

Sentiment analysis, also called opinion mining. It is a natural language processing (NLP) task that involves fixing the sentiment tone expressed in a piece of text including a review, tweet, post, comment, or news article, using machine learning algorithms. There are various social networking sites where people express their views, opinions, and emotions freely. All such posts are recorded and analyzed to determine emotions of the people. Present study recovers posts and finds emotions and polarity of posts. To determine emotions and polarity of posts, various techniques are used.
Comparison and optimization of different routing methods for meshed QKD networks using trusted nodes

Quantum key distribution (QKD) appears as a promising technique for encrypted communication, preserving security even in the presence of a future powerful quantum computer. At the same time, communication infrastructure becomes increasingly complex, and the exponentially increasing traffic volume makes the application of QKD a challenging task. In addition, current hardware for QKD modules is still subject to research to gain maturity, to guarantee secret key rates, and to mitigate key rate fluctuations and instabilities. Our study analyzes and optimizes five routing algorithms to efficiently use the secret keys as a resource in meshed national-wide networks. By fulfilling distinct security and performance levels, the algorithms are compared with respect to key performance indicators and optimized for blocking probabilities, load balancing, or the management traffic amount. Depending on the application, our study allows operators to choose a well-suited algorithm and gives performance estimates, including an assessment of the performance gap to globally optimized algorithms.
Hubble Meets Webb: Image-to-Image Translation in Astronomy

This work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data. Comparative analysis encompasses the Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette methodologies, assessing the criticality of image registration in astronomy. While the focus of this study is not on the scientific evaluation of model fairness, we note that the techniques employed may bear some limitations and the translated images could include elements that are not present in actual astronomical phenomena. To mitigate this, uncertainty estimation is integrated into our methodology, enhancing the translation's integrity and assisting astronomers in distinguishing between reliable predictions and those of questionable certainty. The evaluation was performed using metrics including MSE, SSIM, PSNR, LPIPS, and FID. The paper introduces a novel approach to quantifying uncertainty within image translation, leveraging the stochastic nature of DDPMs. This innovation not only bolsters our confidence in the translated images but also provides a valuable tool for future astronomical experiment planning. By offering predictive insights when JWST data are unavailable, our approach allows for informed preparatory strategies for making observations with the upcoming JWST, potentially optimizing its precious observational resources. To the best of our knowledge, this work is the first attempt to apply image-to-image translation for astronomical sensor-to-sensor translation.
Blind watermarking algorithm with 2D-SCCM chaotic system encryption based on SURF and RHFMs

Abstract In this paper, a digital watermarking algorithm was proposed based on local feature regions and radial harmonic Fourier moments. It used a quantized modulation algorithm to embed the watermark information into the frequency domain information of the image. Compared with traditional watermark directedly embedded into the pixel values, the algorithm enhanced the invisibility of the watermark. Many existing digital watermarking algorithms neglected the security of the embedding process, because of more attention on the effect of post-embedding and extraction. To overcome this problem, we proposed a two-dimensional hyperchaotic system named 2D-SCCM chaotic system, which used classical sine mapping and cubic mapping for coupling. Before embedding, the correlation among the pixels of the watermarked image was disrupted, resulting in a full process security enhancement. Combining the clustering algorithm with feature point extraction, the obtained local feature region was more stable and suitable for watermark embedding. Results showed that the improved watermark embedding algorithm was more effective for resisting geometric attacks such as cropping attacks. Moreover, the PSNR values of the final watermark-embedded images were all greater than 50 dB, indicating a good balance between imperceptibility and robustness.&amp;#xD;
The Bounded Gaussian Mechanism for Differential Privacy

The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.
Quantum differential privacy under noise channels

Abstract Quantum differential privacy is a way to protect privacy in quantum computing. It perturbs the final state of quantum computing by a specific quantum noise channel and makes the output useless to the attackers, so as to achieve privacy protection. In this paper, we study the quantum differential privacy under three channels: bit flip channel, phase flip channel and depolarizing channel. We calculate the privacy parameters for these three channels based on the proportional distance. We show the privacy protection capability by calculating the decay rates of the trace distances of two arbitrary quantum states before and after passing through the channels. Under the same privacy budget, we compare the fidelities, the trace distances between the real outputs and the outputs after entering the channels, and analyze the privacy protection performance of different quantum channels in detail.
Programming Computational Electromagnetic Applications Assisted by Large Language Models [Em Programmer’s Notebook]

This article discusses the possibilities and limitations of using large language models (LLMs) in software development with applications in computational electromagnetics (EM). Three tasks are discussed: code translation, code generation, and code description. The tests showed that LLMs are generally very useful. Even when errors occurred, they could provide useful hints to find a solution.
Nichilismo storiografico e nuovi media nella RPC Ricerca accademica e controllo politico

This paper provides an overview of the phenomenon of ‘online historical nihilism’ ( wangluo lishi xuwuzhuyi 网络历史虚无主义) in the People’s Republic of China. This research maps the political and academic discussion on the new form of nihilism through the analysis of Chinese official documents and scholarly debates. The first part of the article highlights the political and academic relevance of historical nihilism in China, emphasizing its relationship with new media. The second part focuses on the Chinese academic discussion of online historical nihilism. The final section examines the norms and ad hoc initiatives promoted by state-run online portals and private online platforms aimed at limiting forms of online historical nihilism.
Correction: Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

[This corrects the article DOI: 10.1371/journal.pone.0296107.].