Reviewerly/exHarmony huggingface dataset:
Lithological mapping of geological remote sensing via adversarial semi-supervised segmentation network

Geological remote sensing interpretation (GRSI), which aims to recognize multiple geological elements based on their characteristics on satellite remote sensing images, is vital in large-scale regional lithological mapping. However, due to the influence of long-term geological movements, the spatial distribution of geological elements (such as lithology, glaciers, and soils) on the image is often complex and highly fragmented. In addition, the characteristics of high inter-class similarity and severe homogenization make the annotation of geological element samples require significant cost and expert knowledge. These lead to insufficient interpretation accuracy and limited annotation samples in GRSI. To alleviate the dependence of labeled samples and promote the performance of GRSI, we propose an adversarial semi-supervised segmentation network with object-context and global-attention (AdvSemi-OCGNet) for the GRSI, which achieves effective segmentation results in the case of limited labeled samples. Under the architecture of adversarial learning, a proposed baseline network OCGNet and a full convolution discriminator (FCD) are integrated to conduct semi-supervised segmentation. OCGNet, as the generator, aims to confuse FCD by predicting probability maps. Then FCD selects trustworthy regions with high-confidence of unlabeled sample to generate pseudo-labels for semi-supervised segmentation of OCGNet. Iterative adversarial learning is employed to simulate the process of expert interpretation of geological elements through continuous discrimination and correction. Finally, a fully connected conditional random field eliminates holes and isolated areas of segmented results caused by misclassification. Two study areas are selected, which include various geological elements such as multiple lithologies, soils, rock glaciers, and surface water. Numerous experiments have revealed the superiority of the proposed model in GRSI with limited annotation samples.
Internet resources for foreign language education in primary school: challenges and opportunities

The paper explores the challenges and opportunities of developing professional competence of primary school teachers in teaching foreign languages according to the New Ukrainian School concept. The paper analyzes and describes various Internet resources that can facilitate and enhance foreign language learning outcomes in primary school. The paper argues that Internet resources can help modernize foreign language education in primary school and align it with the New Ukrainian School concept. The paper also discusses the importance of training primary school teachers in the methods of organizing distance learning, which is a priority for higher education institutions in the context of continuous education.
Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems

The perception system is a safety-critical component that directly impacts the overall safety of autonomous driving systems (ADSs). It is imperative to ensure the robustness of the deep-learning model used in the perception system. However, studies have shown that these models are highly vulnerable to the adversarial perturbation of input data. The existing works mainly focused on studying the impact of these adversarial attacks on classification rather than regression models. Therefore, this paper first introduces two generalized methods for perturbation-based attacks: (1) We used naturally occurring noises to create perturbations in the input data. (2) We introduce a modified square, HopSkipJump, and decision-based/boundary attack to attack the regression models used in ADSs. Then, we propose a deep-autoencoder-based adversarial attack detector. In addition to offline evaluation metrics (e.g., F1 score and precision, etc.), we introduce an online evaluation framework to evaluate the robustness of the model under attack. The framework considers the reconstruction loss of the deep autoencoder that validates the robustness of the models under attack in an end-to-end fashion at runtime. Our experimental results showed that the proposed adversarial attack detector could detect square, HopSkipJump, and decision-based/boundary attacks with a true positive rate (TPR) of 93%.
A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach

Soil is a complex and spatially variable material that has a demonstrated potential as a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain police search areas and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0–5 cm depth; 1 sample per 1 km2) was carried out over Canberra, Australia, aiming to document the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) of the fused XRF beads were obtained from the survey’s 685 topsoil samples (plus 138 additional quality control samples and six “Blind” simulated evidentiary samples). Using those “Blind” samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between the two Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid. In addition, an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material was demonstrated. Our results show this collective procedure generates more consistent and robust provenance maps when applied to two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), with different grid placements (e.g., grid shifts to the north or east) and by different theoretical users (e.g., different computer systems, or forensic geoscientists).
Time-Dependent Hamiltonian Reconstruction Using Continuous Weak Measurements

Continuous weak measurements enable reconstruction of time-dependent Hamiltonians, enhancing the time resolution of quantum gate errors beyond what is revealed by standard tomographic and benchmarking techniques.
Enhancing Reliability and User Experience in Conversational Agents

Conversational agents are in place in a variety of domains and tasks such as sales and customer support services in business, student counselling in education and medical services in healthcare. There is abundant data available for modelling dialogs because online chat has been a popular way of communication between humans already for several decades. There are also innumerable other valuable digital resources that can be exploited when building a conversational agent, including pretrained large language models. We tested and evaluated several ways of preprocessing and modelling of chat dialogs in Finnish. As a result, we found out that the best accuracy is achieved using uncasing and spell-checking in the preprocessing phase and a BERT model pretrained with Finnish in the modelling phase. Despite the extensive use of conversational agents, there are still many open research questions. One example is the effect of the interaction style of the agent on user experience and emotions. Our initial study suggests that chatbots including small talk are less likely to elicit negative emotions, whereby emojis and emotional statements issued by chatbots do not play a significant role on the user’s emotional responses. We also discuss how medical expert work may be partially automated and made more interesting as input for routine conversation is handled by a chatbot. Special attention is paid on the requirements for trustworthiness and reliability for conversational agents acting in different tasks and domains.
Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering

This innovative study introduces a novel enhancement to recommendation systems through a synergistic integration of Collaborative Filtering (CF) and Content-Based Filtering (CBF) techniques, termed the hybrid CF-CBF approach. By seamlessly amalgamating the strengths of CF's user interaction insights and CBF's content analysis prowess, this approach pioneers a more refined and personalized recommendation paradigm. The research encompassed meticulous phases, including comprehensive data acquisition, efficient storage management, meticulous data refinement, and the skillful application of CF and CBF methodologies. The findings markedly highlight the prowess of the hybrid approach in generating recommendations that exhibit enhanced diversity and precision, surpassing the outcomes obtained from either technique in isolation. Remarkably, the hybrid CF-CBF approach effectively addresses the inherent shortcomings of individual methods, such as CF's vulnerability to the "cold start" problem and CBF's limitation in fostering recommendation diversity. By fostering a harmonious synergy, this novel approach transcends these limitations and provides a holistic solution. Furthermore, the interplay of CF and CBF augments the recommender system's cognitive grasp of user preferences, subsequently enriching the quality of recommendations provided. In conclusion, this research stands as a pioneering contribution to the evolution of recommendation systems by championing the hybrid CF-CBF approach. By ingeniously fusing two distinct techniques, the study engenders a breakthrough in personalized recommendations, thereby propelling the advancement of more sophisticated and effective recommendation systems.
Representation of Imprecision in Deep Neural Networks for Image Classification

Quantification and reduction of uncertainty in deep-learning techniques have received much attention but ignored how to characterize the imprecision caused by such uncertainty. In some tasks, we prefer to obtain an imprecise result rather than being willing or unable to bear the cost of an error. For this purpose, we investigate the representation of imprecision in deep-learning (RIDL) techniques based on the theory of belief functions (TBF). First, the labels of some training images are reconstructed using the learning mechanism of neural networks to characterize the imprecision in the training set. In the process, a label assignment rule is proposed to reassign one or more labels to each training image. Once an image is assigned with multiple labels, it indicates that the image may be in an overlapping region of different categories from the feature perspective or the original label is wrong. Second, those images with multiple labels are rechecked. As a result, the imprecision (multiple labels) caused by the original labeling errors will be corrected, while the imprecision caused by insufficient knowledge is retained. Images with multiple labels are called imprecise ones, and they are considered to belong to meta-categories, the union of some specific categories. Third, the deep network model is retrained based on the reconstructed training set, and the test images are then classified. Finally, some test images that specific categories cannot distinguish will be assigned to meta-categories to characterize the imprecision in the results. Experiments based on some remarkable networks have shown that RIDL can improve accuracy (AC) and reasonably represent imprecision both in the training and testing sets.
Establishing an early indicator for data sharing and reuse

Abstract Funders, publishers, scholarly societies, universities, and other stakeholders need to be able to track the impact of programs and policies designed to advance data sharing and reuse. With the launch of the NIH data management and sharing policy in 2023, establishing a pre‐policy baseline of sharing and reuse activity is critical for the biological and biomedical community. Toward this goal, we tested the utility of mentions of research resources, databases, and repositories (RDRs) as a proxy measurement of data sharing and reuse. We captured and processed text from Methods sections of open access biological and biomedical research articles published in 2020 and 2021 and made available in PubMed Central. We used natural language processing to identify text strings to measure RDR mentions. In this article, we demonstrate our methodology, provide normalized baseline data sharing and reuse activity in this community, and highlight actions authors and publishers can take to encourage data sharing and reuse practices.
A tripartite evolutionary game analysis of sports data rights protection from the perspective of stakeholder

The amount of data in the globe is increasing at an exponential rate, and the role of digital technology and data elements in the modern growth of China’s sports business is becoming more significant. Disputes over sports data rights have grown in the international arena, with a special focus on how to preserve the sports data rights of key stakeholders. This study begins from the standpoint of stakeholders, constructs a tripartite evolutionary game model of the government, sports enterprises, and the public around the issue of sports data rights protection, investigates the behavioral evolutionary characteristics of each stakeholder and its stabilization strategies, simulates and analyzes the evolutionary stabilization strategies of the stakeholders in various situations using MATLAB, and proposes a compatible institution. According to the findings of this study, the cost of sports data governance, the cost of sports data sharing, the degree of sports data compliance use, the government incentive system, and the government regulation mechanism are the important elements impacting each stakeholder’s behavioral decision-making. Based on the foregoing, the study proposes corresponding institutional strategies for governments to respond to the issue of sports data rights, which will be more conducive to the safe use of data for development and humanitarian action, as well as contribute to global sustainable development.
StableFace: Analyzing and Improving Motion Stability for Talking Face Generation

While previous methods for speech-driven talking face generation have shown significant advances in improving the visual and lip-sync quality of the synthesized videos, they have paid less attention to lip motion jitters which can substantially undermine the perceived quality of talking face videos. What causes motion jitters, and how to mitigate the problem? In this paper, we conduct systematic analyses to investigate the motion jittering problem based on a state-of-the-art pipeline that utilizes 3D face representations to bridge the input audio and output video, and implement several effective designs to improve motion stability. This study finds that several factors can lead to jitters in the synthesized talking face video, including jitters from the input face representations, training-inference mismatch, and a lack of dependency modeling in the generation network. Accordingly, we propose three effective solutions: 1) a Gaussian-based adaptive smoothing module to smooth the 3D face representations to eliminate jitters in the input; 2) augmented erosions added to the input data of the neural renderer in training to simulate the inference distortion to reduce mismatch; 3) an audio-fused transformer generator to model inter-frame dependency. In addition, considering there is no off-the-shelf metric that can measures motion jitters of talking face video, we devise an objective metric (Motion Stability Index, MSI) to quantitatively measure the motion jitters. Extensive experimental results show the superiority of the proposed method on motion-stable talking video generation, with superior quality to previous systems.
Managing Cybersecurity Threats and Increasing Organizational Resilience

Cyber security is high up on the agenda of senior managers in private and public sector organizations and is likely to remain so for the foreseeable future. [...]
A model of stochastic memoization and name generation in probabilistic programming: categorical semantics via monads on presheaf categories

Stochastic memoization is a higher-order construct of probabilistic programming languages that is key in Bayesian nonparametrics, a modular approach that allows us to extend models beyond their parametric limitations and compose them in an elegant and principled manner. Stochastic memoization is simple and useful in practice, but semantically elusive, particularly regarding dataflow transformations. As the naive implementation resorts to the state monad, which is not commutative, it is not clear if stochastic memoization preserves the dataflow property -- i.e., whether we can reorder the lines of a program without changing its semantics, provided the dataflow graph is preserved. In this paper, we give an operational and categorical semantics to stochastic memoization and name generation in the context of a minimal probabilistic programming language, for a restricted class of functions. Our contribution is a first model of stochastic memoization of constant Bernoulli functions with a non-enumerable type, which validates data flow transformations, bridging the gap between traditional probability theory and higher-order probability models. Our model uses a presheaf category and a novel probability monad on it.
Research on Internet public sentiment and social governance based on big data technology

On the basis of introducing the connotation of online public sentiment and its influence on government governance, this article explores the practical dilemma of online public sentiment governance from the perspective of big data, and puts forward that the government's real-time monitoring, analysis and pre-alarm ability of online public sentiment should be effectively improved by establishing and improving the government's online public sentiment big data supervision system.
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models

The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and time-intensive. While existing design exploration and automation tools can partially alleviate the need for extensive human involvement, they still demand substantial hardware expertise, posing a barrier to non-experts and stifling AI accelerator development. Motivated by the astonishing potential of large language models (LLMs) for generating high-quality content in response to human language instructions, we embark on this work to examine the possibility of harnessing LLMs to automate AI accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework intended to democratize AI accelerator design by leveraging human natural languages instead of domain-specific languages. Specifically, we first perform an in-depth investigation into LLMs' limitations and capabilities for AI accelerator design, thus aiding our understanding of our current position and garnering insights into LLM-powered automated AI accelerator design. Furthermore, drawing inspiration from the above insights, we develop a framework called GPT4AIGChip, which features an automated demo-augmented prompt-generation pipeline utilizing in-context learning to guide LLMs towards creating high-quality AI accelerator design. To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation. Accordingly, we anticipate that our insights and framework can serve as a catalyst for innovations in next-generation LLM-powered design automation tools.
Resilience: another advantage of openly-licensed content

As you may have seen in the news, the British Library has been affected by a significant cyberattack. Many of the digital services it provides have gone down and stayed down for many weeks now, whilst investigations take place. I have a lot of sympathy for the BL staff. As has been observed, public services can be a relatively easy target.
ROBOTICS EDUCATION: A COMPARATIVE STUDY OF TEACHING APPROACHES

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 6982-6988ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1738Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
Pemanfaatan Google Site Sebagai Media Pembelajaran Siswa Pada SMKN 3 Kota Bengkulu

Google Sites is a personal or professional website service that doesn't charge anything or is free. This service is a service created by the Google company. users can take advantage of Google Sites because they are easy to create and maintain for novice users. There are a number of uses that are owned by Google Sites that can be utilized by internet users. One of them is for learning. Google Sites can be utilized by teaching staff, such as teachers and lecturers who don't have time to provide online learning systems. Google Sites is a popular site builder tool because it has a number of advantages. First, this tool is free or free. secondly, the process of creating a site using Google Sites is very easy to build. third, these tools allow users to collaborate in their use. fourth, this tool provides 100 MB of free online storage. Fifth, easy to search using the Google search engine. The digital portfolio can be used to document the achievements that have been obtained from the journey during the service period, as well as a means to record personal development.
Collaborative Playalong Practices in Online Music Lessons: the MusiCoLab Toolset

In the realm of music education, backing tracks serve as accompaniments that complement instrument performance, by adding depth and context to the music being practiced. For genres like jazz, blues and ethnic that are inherently improvisational, backing tracks become indispensable tools acting as a canvas upon which musicians can freely paint their melodies, riffs, and solos. This article presents the development of PAT, a versatile web application that seamlessly integrates practices for synchronous collaboration and asynchronous teacher/student interaction. The application offers capabilities for the automatic analysis of backing tracks to retrieve their rhythmic and harmonic content, for playing-along the backing track and layering the solo recordings of teacher/student performances (overdubbing), for allowing to produce a mix-down of selected tracks and finally for using the recorded performances or the mix-down as the newly backing track. The article presents our efforts for collecting the requirements of music instructors for play-along music practices, our design and implementation approach, as well as an informative pilot assessment guiding our ongoing and future developments.