Reviewerly/exHarmony huggingface dataset:
The Effect of Participation in a Extra-curricula Activity Program on the Core Competencies of Social Welfare Majors: with a Focus on the Respect for Life Education Instructor Training Program

The purpose of this study was to examine the changes in core competencies of social work majors after they participated in a life respect education instructor training program as a extracurricular activity in addition to the regular curriculum. A pretest-posttest experimental design was used with 20 undergraduate social work majors at a community college, and the results of the study were as follows. First, the core competencies of university students who participated in the life-respect education instructor training program increased from before to after the program, indicating that the program was effective in enhancing the core competencies of university students. Second, the mean scores of the core competencies were higher in both the overall mean and sub-scores of the core competencies when theoretical training and on-site lecture practice were combined than when only theoretical training was provided in the comparison activity. Based on these findings, suggestions were made for the development, implementation, and methods of various co-curricular programs for social work students.
The HMC Information Portal for Enhanced Metadata Collaboration in the Helmholtz FAIR Data Space

The Helmholtz Metadata Collaboration (HMC) platform was launched in late 2019 to turn FAIR (Findable, Accessible, Interoperable, Reusable) research data into reality within the Helmholtz Association and beyond. The Information Portal was initiated to enable the structured cartography of metadata and FAIR landscape of Helmholtz, providing information for multi-level decision-making and creating a curated knowledge base for research data managers, scientists and other stakeholders.&#x0D; Developed through a top-down approach, 18 categories, and associated metadata schemas were defined and aligned by an HMC taskforce. Data curation followed, with resources collected from different domains based on the aligned metadata schema. The Information Portal is a web application for capturing FAIR data practices across all Helmholtz domains, offering a unified user interface for collecting and exploring results.&#x0D; Built using state-of-the-art technologies, including Python and Docker, the Information Portal leverages GitLab as a database. It offers a public / central read-only version for stakeholders and a personal instance for curation - synchronized to a GitLab repository. Git-based systems offer advantages, such as raw data accessibility, flexible data curation, easy synchronization, and customizable repositories.&#x0D; The single-page web application is user-friendly and developed in multiple iterations for an intuitive and flexible interface. The Information Portal is crucial for creating a sustainable, distributed, semantically enriched Helmholtz data space, promoting seamless data sharing and reuse.
Object-Related Research Data Workflows Within NFDI4Objects and Beyond

NFDI4Objects (N4O) represents a broad community dealing with material remains of human history from around 3 million years and involves numerous disciplines from the humanities, cultural studies and natural sciences with an archaeological and historical focus [1]. The objects examined include potsherds of common ware, artworks such as sculptures or jewellery, serially produced objects such as coins, organic remains such as wood, bones or pollen, inscribed clay tablets, papyri and stones, architectural remains, as well as human-modified landscapes. Modern research materials such as plaster casts, analogue photographs and drawings, archival documents, books and raw digital data are equally relevant.
Linked Open Research Data for Social Science

The re-use of research data is an integral part of research practice in the social and economic sciences. To find relevant data, researchers need adequate search facilities. However, a comprehensive, thematic search for research data is made more difficult by inconsistent or missing semantic indexing of data at the level of social science concepts (e.g., representing the theory language). Either the data is not documented at a granular level, or primary investigators use their ad-hoc terminology to describe their data. Consequently, researchers have to make great efforts to find relevant or comparable data. From the user's perspective, the lack of theory language in data documentation impedes effective data searches and thus significantly limits the research potential of existing data collections. Because there is currently no semantic model for indexing the data content, the specific challenge for improving data search lies in establishing concept-based indexing of research data. Research infrastructures need technology for the harmonized semantic indexing of their research data. The LORD concept registry aims at closing this gap by developing a registry of sociological and economic concepts and, following the FAIR principles, making this concept registry generally available to the scientific community. As a first step, we developed a basic data model for the Concept Registry using United Modeling Language (UML). All links between are created and managed in the form of so-called RDF triples. An annotation application allows for linking specific questions/variables to concepts. The application also includes the two SKOS-compliant thesauri, "Thesaurus Social Sciences" (TheSoz) and "Standard Thesaurus Economics" (STW) but could be extended to other resources like ELSST.&#x0D; We illustrate the application of the LORD concept registry with examples from three large-scale survey programmes (German Socio-Economic Panel, German General Social Survey, National Academics Panel Study). The initial focus is on variables and questions with overlapping content in the three survey programmes, as they form a sound basis for cross-linking with concepts.
Joanna Nowotny &amp; Julian Reidy. Memes – Formen und Folgen eines Internetphänomens

Memes – Formen und Folgen eines Internetphänomens [Memes – Forms and Consequences of an Internet Phenomenon] is a monograph in the German language authored by Joanna Nowotny and Julian Reidy. In the introduction, five main chapters, and a conclusion, the authors tackle a relatively young, but booming phenomenon of digital culture and communication: Internet memes. The book provides valuable insights and perspectives on the ever-evolving dynamics of memes and meme culture, and it successfully provides a well-executed counterpoint to often overly romanticized meme literature by shedding light on the “darker” aspects of meme communication. While many previous publications tend to idealize memes, this book takes a courageous step in examining the potential negative implications and consequences associated with their spread. By focusing on the less explored, sometimes problematic, and even controversial aspects of meme culture, the book presents a comprehensive view of the impact of memes.
Methods and means of static and dynamic code analysis

The purpose of the article is to explore the methods and tools used to analyze software code in order to identify errors and potential problems. Static and dynamic code analysis are key processes in software development, as they allow you to detect errors in the early stages of development, reduce the risk of problems later and ensure high quality of the software product. The article discusses various methods and means of static and dynamic code analysis. For each method and tool, examples of their use and advantages and disadvantages are described.&#x0D; The article will be useful for software developers who want to improve the quality of their products and reduce the risk of problems. It will provide readers with an in-depth understanding of code analysis techniques and tools and help them choose the most appropriate tool for their needs.
LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMs

Question-answering (QA) is a significant application of Large Language Models (LLMs), shaping chatbot capabilities across healthcare, education, and customer service. However, widespread LLM integration presents a challenge for small businesses due to the high expenses of LLM API usage. Costs rise rapidly when domain-specific data (context) is used alongside queries for accurate domain-specific LLM responses. One option is to summarize the context by using LLMs and reduce the context. However, this can also filter out useful information that is necessary to answer some domain-specific queries. In this paper, we shift from human-oriented summarizers to AI model-friendly summaries. Our approach, LeanContext, efficiently extracts $k$ key sentences from the context that are closely aligned with the query. The choice of $k$ is neither static nor random; we introduce a reinforcement learning technique that dynamically determines $k$ based on the query and context. The rest of the less important sentences are reduced using a free open source text reduction method. We evaluate LeanContext against several recent query-aware and query-unaware context reduction approaches on prominent datasets (arxiv papers and BBC news articles). Despite cost reductions of $37.29\%$ to $67.81\%$, LeanContext's ROUGE-1 score decreases only by $1.41\%$ to $2.65\%$ compared to a baseline that retains the entire context (no summarization). Additionally, if free pretrained LLM-based summarizers are used to reduce context (into human consumable summaries), LeanContext can further modify the reduced context to enhance the accuracy (ROUGE-1 score) by $13.22\%$ to $24.61\%$.
MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation

This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the video editing process into two stages: appearance editing and motion-aware appearance propagation. In the first stage, MagicProp selects a single frame from the input video and applies image-editing techniques to modify the content and/or style of the frame. The flexibility of these techniques enables the editing of arbitrary regions within the frame. In the second stage, MagicProp employs the edited frame as an appearance reference and generates the remaining frames using an autoregressive rendering approach. To achieve this, a diffusion-based conditional generation model, called PropDPM, is developed, which synthesizes the target frame by conditioning on the reference appearance, the target motion, and its previous appearance. The autoregressive editing approach ensures temporal consistency in the resulting videos. Overall, MagicProp combines the flexibility of image-editing techniques with the superior temporal consistency of autoregressive modeling, enabling flexible editing of object types and aesthetic styles in arbitrary regions of input videos while maintaining good temporal consistency across frames. Extensive experiments in various video editing scenarios demonstrate the effectiveness of MagicProp.
WSIC: a Python package and command-line interface for fast whole slide image conversion

Abstract Summary Whole slide images (WSIs) are multi-gigapixel images of tissue sections, which are used in digital and computational pathology workflows. WSI datasets are commonly heterogeneous collections of proprietary or niche specialized formats which are challenging to handle. This note describes an open-source Python application for efficiently converting between WSI formats, including common, open, and emerging cloud-friendly formats. WSIC is a software tool that can quickly convert WSI files across various formats. It has a high performance and maintains the resolution metadata of the original images. WSIC is ideal for pre-processing large-scale WSI datasets with different file types. Availability and implementation Source code is available on GitHub at https://github.com/John-P/wsic/ under a permissive licence. WSIC is also available as a package on PyPI at https://pypi.org/project/WSIC/.
A Multimodal Analysis of Influencer Content on Twitter

Influencer marketing involves a wide range of strategies in which brands collaborate with popular content creators (i.e., influencers) to leverage their reach, trust, and impact on their audience to promote and endorse products or services. Because followers of influencers are more likely to buy a product after receiving an authentic product endorsement rather than an explicit direct product promotion, the line between personal opinions and commercial content promotion is frequently blurred. This makes automatic detection of regulatory compliance breaches related to influencer advertising (e.g., misleading advertising or hidden sponsorships) particularly difficult. In this work, we (1) introduce a new Twitter (now X) dataset consisting of 15,998 influencer posts mapped into commercial and non-commercial categories for assisting in the automatic detection of commercial influencer content; (2) experiment with an extensive set of predictive models that combine text and visual information showing that our proposed cross-attention approach outperforms state-of-the-art multimodal models; and (3) conduct a thorough analysis of strengths and limitations of our models. We show that multimodal modeling is useful for identifying commercial posts, reducing the amount of false positives, and capturing relevant context that aids in the discovery of undisclosed commercial posts.
Disarming Steganography Attacks Inside Neural Network Models

Similar to the revolution of open source code sharing, Artificial Intelligence (AI) model sharing is gaining increased popularity. However, the fast adaptation in the industry, lack of awareness, and ability to exploit the models make them significant attack vectors. By embedding malware in neurons, the malware can be delivered covertly, with minor or no impact on the neural network's performance. The covert attack will use the Least Significant Bits (LSB) weight attack since LSB has a minimal effect on the model accuracy, and as a result, the user will not notice it. Since there are endless ways to hide the attacks, we focus on a zero-trust prevention strategy based on AI model attack disarm and reconstruction. We proposed three types of model steganography weight disarm defense mechanisms. The first two are based on random bit substitution noise, and the other on model weight quantization. We demonstrate a 100\% prevention rate while the methods introduce a minimal decrease in model accuracy based on Qint8 and K-LRBP methods, which is an essential factor for improving AI security.
Uncertainty Quantification in Deep Learning Based Kalman Filters

Various algorithms combine deep neural networks (DNNs) and Kalman filters (KFs) to learn from data to track in complex dynamics. Unlike classic KFs, DNN-based systems do not naturally provide the error covariance alongside their estimate, which is of great importance in some applications, e.g., navigation. To bridge this gap, in this work we study error covariance extraction in DNN-aided KFs. We examine three main approaches that are distinguished by the ability to associate internal features with meaningful KF quantities such as the Kalman gain (KG) and prior covariance. We identify the differences between these approaches in their requirements and their effect on the training of the system. Our numerical study demonstrates that the above approaches allow DNN-aided KFs to extract error covariance, with most accurate error prediction provided by model-based/data-driven designs.
Serving Time: Real-Time, Safe Motion Planning and Control for Manipulation of Unsecured Objects

A key challenge to ensuring the rapid transition of robotic systems from the industrial sector to more ubiquitous applications is the development of algorithms that can guarantee safe operation while in close proximity to humans. Motion planning and control methods, for instance, must be able to certify safety while operating in real-time in arbitrary environments and in the presence of model uncertainty. This paper proposes Wrench Analysis for Inertial Transport using Reachability (WAITR), a certifiably safe motion planning and control framework for serial link manipulators that manipulate unsecured objects in arbitrary environments. WAITR uses reachability analysis to construct over-approximations of the contact wrench applied to unsecured objects, which captures uncertainty in the manipulator dynamics, the object dynamics, and contact parameters such as the coefficient of friction. An optimization problem formulation is presented that can be solved in real-time to generate provably-safe motions for manipulating the unsecured objects. This paper illustrates that WAITR outperforms state of the art methods in a variety of simulation experiments and demonstrates its performance in the real-world.
Quid Manumit -- Freeing the Qubit for Art

This paper describes how to `Free the Qubit' for art, by creating standalone quantum musical effects and instruments. Previously released quantum simulator code for an ARM-based Raspberry Pi Pico embedded microcontroller is utilised here, and several examples are built demonstrating different methods of utilising embedded resources: The first is a Quantum MIDI processor that generates additional notes for accompaniment and unique quantum generated instruments based on the input notes, decoded and passed through a quantum circuit in an embedded simulator. The second is a Quantum Distortion module that changes an instrument's raw sound according to a quantum circuit, which is presented in two forms; a self-contained Quantum Stylophone, and an effect module plugin called 'QubitCrusher' for the Korg Nu:Tekt NTS-1. This paper also discusses future work and directions for quantum instruments, and provides all examples as open source. This is, to the author's knowledge, the first example of embedded Quantum Simulators for Instruments of Music (another QSIM).
My Art My Choice: Adversarial Protection Against Unruly AI

Generative AI is on the rise, enabling everyone to produce realistic content via publicly available interfaces. Especially for guided image generation, diffusion models are changing the creator economy by producing high quality low cost content. In parallel, artists are rising against unruly AI, since their artwork are leveraged, distributed, and dissimulated by large generative models. Our approach, My Art My Choice (MAMC), aims to empower content owners by protecting their copyrighted materials from being utilized by diffusion models in an adversarial fashion. MAMC learns to generate adversarially perturbed "protected" versions of images which can in turn "break" diffusion models. The perturbation amount is decided by the artist to balance distortion vs. protection of the content. MAMC is designed with a simple UNet-based generator, attacking black box diffusion models, combining several losses to create adversarial twins of the original artwork. We experiment on three datasets for various image-to-image tasks, with different user control values. Both protected image and diffusion output results are evaluated in visual, noise, structure, pixel, and generative spaces to validate our claims. We believe that MAMC is a crucial step for preserving ownership information for AI generated content in a flawless, based-on-need, and human-centric way.
Amortizing Pragmatic Program Synthesis with Rankings

In program synthesis, an intelligent system takes in a set of user-generated examples and returns a program that is logically consistent with these examples. The usage of Rational Speech Acts (RSA) framework has been successful in building \emph{pragmatic} program synthesizers that return programs which -- in addition to being logically consistent -- account for the fact that a user chooses their examples informatively. However, the computational burden of running the RSA algorithm has restricted the application of pragmatic program synthesis to domains with a small number of possible programs. This work presents a novel method of amortizing the RSA algorithm by leveraging a \emph{global pragmatic ranking} -- a single, total ordering of all the hypotheses. We prove that for a pragmatic synthesizer that uses a single demonstration, our global ranking method exactly replicates RSA's ranked responses. We further empirically show that global rankings effectively approximate the full pragmatic synthesizer in an online, multi-demonstration setting. Experiments on two program synthesis domains using our pragmatic ranking method resulted in orders of magnitudes of speed ups compared to the RSA synthesizer, while outperforming the standard, non-pragmatic synthesizer.
Scale-Score: Food Label to Support Nutritious and Sustainable Online Grocery Shopping

To empower online grocery shoppers in making nutritionally and environmentally informed decisions, we investigate the efficacy of the Scale-Score, a label combining nutritional and environmental information to highlight a product's benefit to both the consumer's and the planet's health, without obscuring either information. We conducted an experimental study in a mock online grocery environment, and assessed label efficacy. We find that the Scale-Score supports nutritious purchases, yet needs improving regarding sustainability support. Our research shows first insights into design considerations and performance of a combined yet disjoint food label.
Testing properties of distributions in the streaming model

We study distribution testing in the standard access model and the conditional access model when the memory available to the testing algorithm is bounded. In both scenarios, the samples appear in an online fashion and the goal is to test the properties of distribution using an optimal number of samples subject to a memory constraint on how many samples can be stored at a given time. First, we provide a trade-off between the sample complexity and the space complexity for testing identity when the samples are drawn according to the conditional access oracle. We then show that we can learn a succinct representation of a monotone distribution efficiently with a memory constraint on the number of samples that are stored that is almost optimal. We also show that the algorithm for monotone distributions can be extended to a larger class of decomposable distributions.
ViewMix: Augmentation for Robust Representation in Self-Supervised Learning

Joint Embedding Architecture-based self-supervised learning methods have attributed the composition of data augmentations as a crucial factor for their strong representation learning capabilities. While regional dropout strategies have proven to guide models to focus on lesser indicative parts of the objects in supervised methods, it hasn't been adopted by self-supervised methods for generating positive pairs. This is because the regional dropout methods are not suitable for the input sampling process of the self-supervised methodology. Whereas dropping informative pixels from the positive pairs can result in inefficient training, replacing patches of a specific object with a different one can steer the model from maximizing the agreement between different positive pairs. Moreover, joint embedding representation learning methods have not made robustness their primary training outcome. To this end, we propose the ViewMix augmentation policy, specially designed for self-supervised learning, upon generating different views of the same image, patches are cut and pasted from one view to another. By leveraging the different views created by this augmentation strategy, multiple joint embedding-based self-supervised methodologies obtained better localization capability and consistently outperformed their corresponding baseline methods. It is also demonstrated that incorporating ViewMix augmentation policy promotes robustness of the representations in the state-of-the-art methods. Furthermore, our experimentation and analysis of compute times suggest that ViewMix augmentation doesn't introduce any additional overhead compared to other counterparts.