Reviewerly/exHarmony huggingface dataset:
Virtualization in distributed system: a brief overview

Virtual machines are popular because of their efficiency, ease of use, and flexibility. There has been an increasingdemand for the deployment of a robust distributed network for maximizing the performance of such systemsand minimizing the infrastructural cost. In this study, we have discussed various levels at which virtualizationcan be implemented for distributed computing, which can contribute to increased efficiency and performanceof distributed computing. The study gives an overview of various types of virtualization techniques and theirbenefits. For example, server virtualization helps to create multiple server instances from one physical server. Suchtechniques will decrease the infrastructure costs, make the system more scalable, and help in the full utilization ofavailable resources.
Boon: A Neural Search Engine for Cross-Modal Information Retrieval

Visual-Semantic Embedding (VSE) networks can help search engines better understand the meaning behind visual content and associate it with relevant textual information, leading to more accurate search results. VSE networks can be used in cross-modal search engines to embed image and textual descriptions in a shared space, enabling image-to-text and text-to-image retrieval tasks. However, the full potential of VSE networks for search engines has yet to be fully explored. This paper presents Boon, a novel cross-modal search engine that combines two state-of-the-art networks: the GPT-3.5-turbo large language model, and the VSE network VITR (VIsion Transformers with Relation-focused learning) to enhance the engine's capabilities in extracting and reasoning with regional relationships in images. VITR employs encoders from CLIP that were trained with 400 million image-description pairs and it was fine-turned on the RefCOCOg dataset. Boon's neural-based components serve as its main functionalities: 1) a 'cross-modal search engine' that enables end-users to perform image-to-text and text-to-image retrieval. 2) a 'multi-lingual conversational AI' component that enables the end-user to converse about one or more images selected by the end-user. Such a feature makes the search engine accessible to a wide audience, including those with visual impairments. 3) Boon is multi-lingual and can take queries and handle conversations about images in multiple languages. Boon was implemented using the Django and PyTorch frameworks. The interface and capabilities of the Boon search engine are demonstrated using the RefCOCOg dataset, and the engine's ability to search for multimedia through the web is facilitated by Google's API.
Developing Informational Text Writing Skills of Gifted Students by Leveraging Writing Strategies

Designed as action research, this qualitative study aims to develop the informational text writing skills of gifted students through the strategies of “all about” books and frames for writing biographies. The study group of the research consists of 21 gifted 4th grade students studying in the field of general mental ability in a Science and Art Center in the spring semester of the 2020-2021 academic year. An eight-week action plan was prepared to support the development of these students' ability to write informational texts. The first 4 weeks are planned with our “all about” books, the last 4 weeks with the strategies of biography writing frameworks. The data were collected with student and teacher diaries, observer reports, student products and Informational Text Writing Rubric. Content analysis was used in the analysis of the collected data. The analysis of the data revealed that while the informational text writing skills of the students were at a moderate level at the beginning, they reached a high level at the end of the process. Based on the findings, some suggestions were presented to teachers and researchers to improve students' ability to write informational texts.
Meta-survey on outlier and anomaly detection

The impact of outliers and anomalies on model estimation and data processing is of paramount importance, as evidenced by the extensive body of research spanning various fields over several decades: thousands of research papers have been published on the subject. As a consequence, numerous reviews, surveys, and textbooks have sought to summarize the existing literature, encompassing a wide range of methods from both the statistical and data mining communities. While these endeavors to organize and summarize the research are invaluable, they face inherent challenges due to the pervasive nature of outliers and anomalies in all data-intensive applications, irrespective of the specific application field or scientific discipline. As a result, the resulting collection of papers remains voluminous and somewhat heterogeneous. To address the need for knowledge organization in this domain, this paper implements the first systematic meta-survey of general surveys and reviews on outlier and anomaly detection. Employing a classical systematic survey approach, the study collects nearly 500 papers using two specialized scientific search engines. From this comprehensive collection, a subset of 56 papers that claim to be general surveys on outlier detection is selected using a snowball search technique to enhance field coverage. A meticulous quality assessment phase further refines the selection to a subset of 25 high-quality general surveys. Using this curated collection, the paper investigates the evolution of the outlier detection field over a 20-year period, revealing emerging themes and methods. Furthermore, an analysis of the surveys sheds light on the survey writing practices adopted by scholars from different communities who have contributed to this field. Finally, the paper delves into several topics where consensus has emerged from the literature. These include taxonomies of outlier types, challenges posed by high-dimensional data, the importance of anomaly scores, the impact of learning conditions, difficulties in benchmarking, and the significance of neural networks. Non-consensual aspects are also discussed, particularly the distinction between local and global outliers and the challenges in organizing detection methods into meaningful taxonomies.
BTDSI: A blockchain-based trusted data storage mechanism for Industry 5.0

Industry 5.0 is seen as the next stage of the industrial revolution. However, the centralized management of the vast amount of data in Industry 5.0 poses significant security challenges. Because blockchain can provide a stable decentralized network for data in Industry 5.0, blockchain technology becomes an ideal solution for the security of data in Industry 5.0. However, with the explosion of data in Industry 5.0, the rapidly growing storage requirements are putting unbearable pressure on nodes and users, making it especially important to address the scalability of the blockchain. While using light nodes can alleviate storage pressure on nodes, it can also lead to data availability attacks. To address the above issues, this paper proposes a blockchain-based trusted data storage mechanism for Industry 5.0. The mechanism combines sharding and two-layer Merkle tree structures, and employs random low-density parity codes to code the tree. It enables light nodes to verify the authenticity of data and solve data availability attacks while improving the throughput of the blockchain. In addition, by using erasure codes to construct low-storage blockchain nodes, the network load of the nodes is effectively reduced, which solves the scalability issues of the blockchain.
Communication Pattern Logic: Epistemic and Topological Views

We propose communication pattern logic. A communication pattern describes how processes or agents inform each other, independently of the information content. The full-information protocol in distributed computing is the special case wherein all agents inform each other. We study this protocol in distributed computing models where communication might fail: an agent is certain about the messages it receives, but it may be uncertain about the messages other agents have received. In a dynamic epistemic logic with distributed knowledge and with modalities for communication patterns, the latter are interpreted by updating Kripke models. We propose an axiomatization of communication pattern logic, and we show that collective bisimilarity (comparing models on their distributed knowledge) is preserved when updating models with communication patterns. We can also interpret communication patterns by updating simplicial complexes, a well-known topological framework for distributed computing. We show that the different semantics correspond, and propose collective bisimulation between simplicial complexes.
Effect of Spatial and Spectral Scaling on Joint Characterization of the Spectral Mixture Residual: Comparative Analysis of AVIRIS and WorldView-3 SWIR for Geologic Mapping in Anza-Borrego Desert State Park

A geologic map is both a visual depiction of the lithologies and structures occurring at the Earth’s surface and a representation of a conceptual model for the geologic history in a region. The work needed to capture such multifaced information in an accurate geologic map is time consuming. Remote sensing can complement traditional primary field observations, geochemistry, chronometry, and subsurface geophysical data in providing useful information to assist with the geologic mapping process. Two novel sources of remote sensing data are particularly relevant for geologic mapping applications: decameter-resolution imaging spectroscopy (spectroscopic imaging) and meter-resolution multispectral shortwave infrared (SWIR) imaging. Decameter spectroscopic imagery can capture important mineral absorptions but is frequently unable to spatially resolve important geologic features. Meter-resolution multispectral SWIR images are better able to resolve fine spatial features but offer reduced spectral information. Such disparate but complementary datasets can be challenging to integrate into the geologic mapping process. Here, we conduct a comparative analysis of spatial and spectral scaling for two such datasets: one Airborne Visible/Infrared Imaging Spectrometer—Classic (AVIRIS-classic) flightline, and one WorldView-3 (WV3) scene, for a geologically complex landscape in Anza-Borrego Desert State Park, California. To do so, we use a two-stage framework that synthesizes recent advances in the spectral mixture residual and joint characterization. The mixture residual uses the wavelength-explicit misfit of a linear spectral mixture model to capture low variance spectral signals. Joint characterization utilizes nonlinear dimensionality reduction (manifold learning) to visualize spectral feature space topology and identify clusters of statistically similar spectra. For this study area, the spectral mixture residual clearly reveals greater spectral dimensionality in AVIRIS than WorldView (99% of variance in 39 versus 5 residual dimensions). Additionally, joint characterization shows more complex spectral feature space topology for AVIRIS than WorldView, revealing information useful to the geologic mapping process in the form of mineralogical variability both within and among mapped geologic units. These results illustrate the potential of recent and planned imaging spectroscopy missions to complement high-resolution multispectral imagery—along with field and lab observations—in planning, collecting, and interpreting the results from geologic field work.
PR-YOLO: Improved YOLO for fast protozoa classification and segmentation

Abstract Protozoa, such as Ceratium and Paramecium, play a fundamental role in establishing sustainable ecosystems. The distribution and classification of certain protozoa and their species are informative indicators to evaluate environmental quality. However, protozoa analysis is traditionally performed by molecular biological (DNA, RNA) or morphological methods, which are time-consuming and require an experienced laboratory operator. In this work, we adopt a deep learning-based network to solve the protozoa classification task. This method utilizes microscope images to help researchers analyse the protozoa population and species, reducing the cost of experimental sample storage and relieving the burden on laboratory operators. However, the shape and size of protozoa vary greatly, which places a great burden on the optimization of DCNN feature distillation. It is a great challenge to build a fast and precise protozoa analysis image. We present a new version of YOLO-v5 with better performance and extend it with instance segmentation called PR-YOLO. Building on the original YOLOv5, we added two extra parallel branches to PR-YOLO, which perform different segmentation subtasks: (1) a branch generates a set of prototype masks (images); (2) the other branch predicts a set of mask coefficients corresponding to prototype masks for each instance mask generation. Then, to improve the classification accuracy, we introduced transformer encoding blocks and lightweight Convolution Block Attention Modules (CBAMs) to explore the prediction potential with a self-attention mechanism. To quantitatively evaluate the performance of PR-YOLO, a comprehensive experiment was carried out on the hand-segmented microscopic protozoa images. Our model obtained the best results, with average classification accuracy of 96.83% and mean Average Precision(mAP) of 86.92% with a speed of 25.2 fps, which proves that the method has high robustness in this application field.
PARALLEL DETERMINATION OF THE SPECTRUM OF SYMMETRIC TOEPLITZ MATRIXES WITH LEVINSON-DARBIN TRANSFORMATIONS

Purpose. The paper investigates modern approaches to mathematical modeling of random fields using correlation matrices of superhigh dimensions. Methodology. Techniques for constructing surrogate models focused on reducing the high dimension of stochastic input spaces have been considered. A comparative analysis of modern numerical methods for searching for eigenvalues of filled matrices has been carried out with an assessment of the possibilities of parallelizing the computation process. To determine the range of the distribution spectrum of eigenvalues of symmetric Toeplitz matrices and subsequent localization of eigenvalues, the use of Gershgorin circles has been proposed. As test matrices, we used the generation of random vectors of arbitrary dimensions, followed by the formation of symmetric Toeplitz matrices on such vectors. Results. The studies have been carried out both for serial and parallel implementations of computational processes. When testing, the main tasks were aimed at conducting a comparative analysis of the accuracy of the solutions obtained, at estimating the coefficient of the ratio of the total number of partitioning problems to those that led to obtaining an eigenvalue. Scientific novelty. The use of a parallel procedure for separating and refining all matrix eigenvalues using a priority search for the smallest(s), largest(s) of eigenvalues has been justified. A software application has been developed for separating and refining all eigenvalues of symmetric Toeplitz filled matrices, which are formed in the course of mathematical modeling of random fields. Practical significance. The proposed software product can be used in numerical simulators for the implementation of mathematical models of groundwater flows and physicochemical processes of dissolution in deep aquifers.
Leveling the Research Playing Field: Decolonizing Global Health Research Through Web-Based Platforms

Global health research has traditionally been rooted in colonialism, with some investigators in high-income countries leading and managing research and investigators in low- and middle-income countries serving as implementing partners. The Community Health Worker-Led Intervention for Vaccine Information and Confidence (CIVIC) Project, conducted in India and led jointly by India- and US-based investigators, leveraged web-based platforms to facilitate a more horizontal, inclusive, and balanced approach to partnerships between researchers and the community. Using web-based platforms to conduct research was found to be an effective strategy to engage researchers at all levels and combat systemic barriers associated with in-person activities such as power, economic, social, and gender dynamics. Connecting online for research meetings created a more equitable environment for community members to engage meaningfully with research. Further, by conducting research through web-based platforms, we found that we were able to strengthen the diversity of participants, provide a space for more marginalized groups to speak up, and minimize logistical barriers to attendance. Harnessing web-based approaches in research provides a pathway toward opportunities to promote equity and contribute to the decolonization of global health spaces.
Large language models as models of human cognition

Can a large language model be used as a ‘cognitive model’, a scientific artifact that helps us understand the human mind? If LLMs can be made openly accessible to scientific investigation then they may provide a valuable model system for studying the emergence of language, reasoning, and other uniquely human behaviors.
A pragmatic investigation of linguistic politeness and power relations in request emails

Communication has evolved over the years.The advent of technology has mediated communication thus bringing about new media such as emails.This computer-mediated communication has continuously gained the attention of language scholars, and, more recently, most of the studies on emails analyzed politeness and impoliteness as marked by the appropriateness of the sender's language in various contexts.However, there seems to be a dearth of literature which analyzes emails in the workplace, especially in the Philippine setting.Hence, this study examined the presence (or the absence thereof) of Brown and Levinson's (1978, 1987) positive politeness strategies and/or negative strategies in 86 emails sent by the employees to the Chief Executive Officer (CEO) of a higher education institution.This study employed a quantitative-qualitative research design, and the findings indicate that there is preponderance of opening and closing moves, which reflects positive politeness strategies, and a salience of conventional indirectness with query preparatory, which demonstrates negative politeness strategies.In conclusion, this eclectic use of politeness strategies in workplace emails is not just a linguistic act, but, more significantly, it is a social act which depicts power relations among interlocutors.Accordingly, the discursive social practice such as writing emails is not just about exchanging information.More importantly, it reflects (re)negotiating communicative intentions within a continuum emphasizing solidarity and desirability on one end and mitigating impediment and imposition on one's act on the other.
Concept maps for formative assessment: Creation and implementation of an automatic and intelligent evaluation method

Formative assessment is about providing and using feedback and diagnostic information. On this basis, further learning or further teaching should be adaptive and, in the best case, optimized. However, this aspect is difficult to implement in reality, as teachers work with a large number of students and the whole process of formative assessment, especially the evaluation of student performance takes a lot of time. To address this problem, this paper presents an approach in which student performance is collected through a concept map and quickly evaluated using Machine Learning techniques. For this purpose, a concept map on the topic of mechanics was developed and used in 14 physics classes in Germany. After the student maps were analysed by two human raters on the basis of a four-level feedback scheme, a supervised Machine Learning algorithm was trained on the data. The results show a very good agreement between the human and Machine Learning evaluation. Based on these results, an embedding in everyday school life is conceivable, especially as support for teachers. In this way, the teacher can use and interpret the automatic evaluation and use it in the classroom.
Machine Learning with a Reject Option: A survey

Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.
Conformal Prediction Masks: Visualizing Uncertainty in Medical Imaging

Estimating uncertainty in image-to-image recovery networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms. A recent conformal prediction technique derives per-pixel uncertainty intervals, guaranteed to contain the true value with a user-specified probability. Yet, these intervals are hard to comprehend and fail to express uncertainty at a conceptual level. In this paper, we introduce a new approach for uncertainty quantification and visualization, based on masking. The proposed technique produces interpretable image masks with rigorous statistical guarantees for image regression problems. Given an image recovery model, our approach computes a mask such that a desired divergence between the masked reconstructed image and the masked true image is guaranteed to be less than a specified risk level, with high probability. The mask thus identifies reliable regions of the predicted image while highlighting areas of high uncertainty. Our approach is agnostic to the underlying recovery model and the true unknown data distribution. We evaluate the proposed approach on image colorization, image completion, and super-resolution tasks, attaining high quality performance on each.
Lecturers’ Perception in Using YouTube for Teaching ESP

Information and communication technology (ICT) developments have completely changed how education is delivered. The lecturers especially English for Specific Purposes lecturers have been encouraged to use websites such as YouTube to promote better and best learning among the ‘Net Generation’ students. YouTube is an option media for teaching listening of ESP. The purposes of this study are to explain the benefits of YouTube in ESP classes especially in teaching listening based on the lecturers’ perception. This study used descriptive qualitative research design. YouTube is also one of websites that can support ESP teaching and Learning Process especially in listening. YouTube video is shared to the students by the lecturers when they teach listening of ESP in the class. Using YouTube in teaching listening of ESP classes was very benficial for the lecturers and the students. Besides, the lecturers’ perceptions on using YouTube were very interesting and useful teaching and learning in ESP classes.&#x0D; &#x0D; Keywords: YouTube, Teaching ESP, ESP classes.