Reviewerly/exHarmony huggingface dataset:
Meeting the demands of industry: A study on identifying and teaching emerging technologies in Engineering Education

As technology advances at an unprecedented pace, it is crucial for engineering faculties at contemporary universities and higher education institutions to continuously update their curricula. This is to ensure that graduates are equipped with the skills and knowledge required to design, develop, and implement emerging technologies in various industries (Wahyuningsih et al., 2020). However, there is a lack of consensus on which emerging technologies should be included in the engineering curriculum and how they should be taught. This study aims to identify the emerging technologies that should be included in the engineering curriculum and the best practices for teaching these technologies to engineering students. By addressing this gap in knowledge, this study contributes to the development of a comprehensive and up-to-date engineering curriculum that prepares graduates for the challenges and opportunities of the 21st century. The objective of this research was to identify the most relevant emerging technologies, assess the current teaching practices and identify areas for improvement, and identify the key skills and knowledge that engineering students need to develop to prepare them for the challenges and opportunities of the 21st century. There were two phases to this study. An online survey was done in phase one to gather quantitative data from engineering educators on attitudes and perceptions on the emerging technologies that are most relevant to the engineering field, the current practices for teaching these technologies, and the key skills and knowledge that engineering students need to develop. In the second phase, a focus group interview was conducted to gather more in-depth qualitative data on how emerging technologies can be integrated into the curriculum. This research study found a comprehensive understanding of the emerging technologies that should be included in the engineering curriculum and the best practices for teaching these technologies to engineering students.
What are acceptable reductions?

It has been argued that reduction procedures are closely connected to the question about identity of proofs and that accepting certain reductions would lead to a trivialization of identity of proofs in the sense that every derivation of the same conclusion would have to be identifed. In this paper it will be shown that the question, which reductions we accept in our system, is not only important if we see them as generating a theory of proof identity but is also decisive for the more general question whether a proof has meaningful content. There are certain reductions which not only would force us to identify proofs of different arbitrary formulas but which would render derivations in a system allowing them meaningless.
Harnessing the Power of Auxiliary Generators in Contrastive Learning for Advancing Few-Shot Learning in Remote Sensing Applications

Remote sensing technologies have revolutionized our ability to observe and analyze our planet from afar. From monitoring environmental changes to disaster management, agriculture, and urban planning, remote sensing applications are integral to modern data-driven decision-making. One of the fundamental challenges in remote sensing is the ability to learn from a limited set of labeled data, which is often referred to as few-shot learning. This challenge becomes even more critical when we consider the vast and dynamic landscapes that remote sensing covers, where traditional supervised learning approaches often fall short due to the scarcity of labeled training data.In recent years, the field of machine learning has made remarkable strides in addressing the challenges of few-shot learning, thanks to innovative techniques like contrastive learning. Contrastive learning leverages the power of similarity-based learning by comparing and contrasting data points, enabling the model to learn meaningful representations without relying heavily on large amounts of labeled data. It has proven to be a game-changer in various domains, including computer vision and natural language processing.
Interaktif 3d Level Design Game dengan Tema Nusantara pada Blueprint Unreal Engine 4

The development of the gaming world has undergone very rapid progress, and we can see that the platforms for playing games have become more flexible. Looking at current trends and smartphone technology, the mobile gaming industry will become very popular due to the ease of use of the platform. In this research, the method used is Blueprint Visual Scripting, using this method allows for the development of games without coding. The purpose of this research is to design an interactive level design in a game by bringing the latest technology from a game engine that produces a prototype design of the map and interaction components in the game.
ARCHIVES AND SOURCES Archival Trials: Unpublished Records from the Allahabad High Court

Abstract In recent years legal historians of South Asia have demonstrated the value of moving beyond traditional state archives and published law reports by retrieving records left in working courts. Building on this scholarship, this essay presents reflections from time spent with the unpublished historic records of the Allahabad High Court. The essay begins by examining two unpublished cases retrieved from these records. I then consider how more careful attention to the historical development of the legal record might encourage new ways of thinking through the politics of the colonial archive.
DEVELOPMENT OF E-WORKSHEETS (E-LKPD) FOR SCIENCE IN ELEMENTARY SCHOOLS BASED ON HIGHER-ORDER THINKING SKILLS (HOTS)

This research aims to improve HOTS-based E-Worksheets in Science, test the effectiveness of the E-Worksheets through expert validation, and identify student responses to the development of HOTS-based Science E-Worksheets. This research uses the research and development model, including potential problems, information gathering, product design, design validation, design improvement, and product testing. The research was conducted at SD Negeri Serang 13 with 20 students of 5 th grade as subjects for limited testing. The instruments used were questionnaire sheets for module, design, language expert validation, and student responses. The research results indicate that the feasibility of HOTS-based Science E-Worksheets, according to expert evaluations, is considered highly feasible, with a percentage of 92% from material experts, 83.65% from design experts, and 80% from linguists. Students responded positively, with a percentage of 94.6%, categorized as “very good.” The development of HOTS-based Science E-Worksheets is highly suitable to use and has received positive responses to be used as teaching materials.
Enhancing Visual Understanding by Removing Dithering with Global and Self-Conditioned Transformation

PNG-8 images are commonly used on the web due to their small size, but their limited color palette often leads to dithering artifacts. Unfortunately, restoring these images using a conventional convolutional neural network (CNN) often results in suboptimal performance since the spatial distribution of dithering is not uniform across the image. This is because the convolutional operator is spatially consistent, meaning it applies the same kernel to all pixels, which we refer to as a global transformation. To address this issue, we propose PNG8IRNet, one approach that combines global and self-conditioned transformations to remove dithering artifacts. Our method incorporates a multilayer perceptron (MLP) to generate diverse kernels for each pixel, taking into account the spatial non-uniformity of dithering, which we define as a self-conditioned transformation. PNG8IRNet demonstrates its performance on multiple datasets, substantially enhancing visual comprehension through a comprehensive set of experiments.
An Interactive Visualization Tool for Exploring Implicit Relationships in Relational Datasets

Relational datasets capture insights into complex networks of entities and their interconnections. The analysis of such datasets is critical for various domains, from social and biological networks to scientific research. The complexity and interdependencies inherent to relational datasets present significant challenges for analysts aiming to explore and understand such data. These challenges are particularly notable for individuals lacking expertise in data visualization tools and techniques, as well as those without training in deriving complex relations between the entities contained within the dataset. In this paper, we present a visualization tool designed to facilitate this kind of exploration. We illustrate the use of our tool with a dataset on the scientific production of the VINCI symposium.
An Attention-Guided Matching Association Network for Hyperspectral and RGB Fusion Tracking

RGB-based trackers are prone to drift in some challenging scenarios. Hyperspectral data can provide more material information to address these challenges. Therefore, using hyperspectral information to supplement the RGB modality defects to improve tracking performance is worth exploring. However, there is almost no relevant work about this valuable issue. In addition, the two modality data in the existing hyperspectral-RGB dataset are not strictly matched and aligned, which brings significant challenges to the multi-modality tracking task. Therefore, we propose a simple but effective scheme to alleviate the problem of the difficulty of utilizing multi-modality information in tracking caused by the spatial difference between two modalities. In addition, to promote the development of the hyperspectral-RGB multi-modality tracking field, we propose a novel network that adaptively captures the relationship between the two modality information using the attention mechanism in Transformer to improve the tracking performance. Experimental results demonstrate the proposed method’s effectiveness.
Pengaruh Pemahaman Konsep Dasar Akuntansi dan Pengalaman Praktik Kerja Lapangan terhadap Kesiapan Siswa dalam Bekerja Pada Kelas XII Program Keahlian Akuntansi dan Keuangan Lembaga di SMK Negeri 1 Tabanan

The purpose of this study was to be able to determine the effect of understanding the basic concepts of accounting and practical experience of field work on student work readiness. This study used a quantitative research type with a research population of class XII AKL students at SMK Negeri 1 Tabanan, totaling 53 students using a proportional random sampling technique. Based on the results of research (1) there is a positive and significant influence between understanding the basic concepts of accounting on student work readiness as indicated by a sig value &lt;0.05 (0.002 &lt;0.05). (2) there is a positive and significant relationship between practical field work experience and student work readiness with a sig &lt;0.05 (0.000&lt;0.05). (3) there is an influence between understanding the basic concepts of accounting and practical experience of field work simultaneously on student work readiness with a sig value &lt;0.05 (0.000&lt;0.05). Based on the findings and results of the research, the implications that can be given are (1) students increase their knowledge and master the areas of expertise they are interested in, one of which is accounting. (2) make good use of field work practices as a vehicle for skills training. (3) in order to achieve high job readiness, it is necessary to master knowledge and skills through actual training related to the field of work of interest.
Biohybrid Devices: Prototyping Interactive Devices with Growable Materials

Living bio-materials are increasingly used in HCI for fabricating objects by growing. However, how to integrate electronics to make these objects interactive still needs to be clarified. This paper presents an exploration of the fabrication design space of Biohybrid Interactive Devices, a class of interactive devices fabricated by merging electronic components and living organisms. From the exploration of this space using bacterial cellulose, we outline a fabrication framework centered on the biomaterials‘ life cycle phases. We introduce a set of novel fabrication techniques for embedding conductive elements, sensors, and output components through biological (e.g. bio-fabrication and bio-assembling) and digital processes. We demonstrate the combinatory aspect of the framework by realizing three tangible, wearable, and shape-changing interfaces. Finally, we discuss the sustainability of our approach, its limitations, and the implications for bio-hybrid systems in HCI.
THE SPIRITUAL WORLD OF THE PERSONALITY IN THE WORKS OF MODERN DOMESTIC PHILOSOPHERS

The article is devoted to the problem of spirituality of the individual. Based on the identification of certain of its characteristics, which, according to modern researchers, determine its meaning, a certain general idea is formed about its multifacetedness, an intellectual inquiry regarding its structural analysis. An attempt at such an analysis is made on the basis of reference to the works of prominent domestic philosophers who created certain theoretical foundations for its further study. The author refers to the works of S.B. Krymskyi, M.V. Popovych, I.D. Bekha, V.O. Lozovoy in order to identify the general structure of the spirituality of the individual. The composition of such a structure as a spiritual basis includes the Valuable-Semantic Universe and various forms of spiritual irradiation and spiritual influence. Such an understanding of the spiritual world of the individual requires the implementation of a cultural and activity approach in spiritual education.
Session Types With Multiple Senders Single Receiver (report version)

Message passing is a fundamental element in software development, ranging from concurrent and mobile computing to distributed services, but it suffers from communication errors such as deadlocks. Session types are a typing discipline for enforcing safe structured interactions between multiple participants. However, each typed interaction is restricted to having one fixed sender and one fixed receiver. In this paper, we extend session types with existential branching types, to handle a common interaction pattern with multiple senders and a single receiver in a synchronized setting, i.e. a receiver is available to receive messages from multiple senders, and which sender actually participates in the interaction cannot be determined till execution. We build the type system with existential branching types, which retain the important properties induced by standard session types: type safety, progress (i.e. deadlock-freedom), and fidelity. We further provide a novel communication type system to guarantee progress of dynamically interleaved multiparty sessions, by abandoning the strong restrictions of existing type systems. Finally, we encode Rust multi-thread primitives in the extended session types to show its expressivity, which can be considered as an attempt to check the deadlock-freedom of Rust multi-thread programs.
Direct Neural Machine Translation with Task-level Mixture of Experts models

Direct neural machine translation (direct NMT) is a type of NMT system that translates text between two non-English languages. Direct NMT systems often face limitations due to the scarcity of parallel data between non-English language pairs. Several approaches have been proposed to address this limitation, such as multilingual NMT and pivot NMT (translation between two languages via English). Task-level Mixture of expert models (Task-level MoE), an inference-efficient variation of Transformer-based models, has shown promising NMT performance for a large number of language pairs. In Task-level MoE, different language groups can use different routing strategies to optimize cross-lingual learning and inference speed. In this work, we examine Task-level MoE's applicability in direct NMT and propose a series of high-performing training and evaluation configurations, through which Task-level MoE-based direct NMT systems outperform bilingual and pivot-based models for a large number of low and high-resource direct pairs, and translation directions. Our Task-level MoE with 16 experts outperforms bilingual NMT, Pivot NMT models for 7 language pairs, while pivot-based models still performed better in 9 pairs and directions.
On the nonclassicality distance indicator of qudits

We consider the nonclassicality distance indicator of a state in finite-dimensional quantum systems which is evaluating a state nonclassicality by its remoteness from the set of "classical states". The latter are identified with those states whose Wigner function is non-negative. The corresponding Wigner function's positivity polytope in the simplex of qudit eigenvalues is introduced and the representation for the nonclassicality distance indicator as a piecewise function is derived. The results are exemplified by the qutrit case.
A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm

Domain incremental learning aims to adapt to a sequence of domains with access to only a small subset of data (i.e., memory) from previous domains. Various methods have been proposed for this problem, but it is still unclear how they are related and when practitioners should choose one method over another. In response, we propose a unified framework, dubbed Unified Domain Incremental Learning (UDIL), for domain incremental learning with memory. Our UDIL **unifies** various existing methods, and our theoretical analysis shows that UDIL always achieves a tighter generalization error bound compared to these methods. The key insight is that different existing methods correspond to our bound with different **fixed** coefficients; based on insights from this unification, our UDIL allows **adaptive** coefficients during training, thereby always achieving the tightest bound. Empirical results show that our UDIL outperforms the state-of-the-art domain incremental learning methods on both synthetic and real-world datasets. Code will be available at https://github.com/Wang-ML-Lab/unified-continual-learning.
Improving SCGAN's Similarity Constraint and Learning a Better Disentangled Representation

SCGAN adds a similarity constraint between generated images and conditions as a regularization term on generative adversarial networks. Similarity constraint works as a tutor to instruct the generator network to comprehend the difference of representations based on conditions. We understand how SCGAN works on a deeper level. This understanding makes us realize that the similarity constraint functions like the contrastive loss function. We believe that a model with high understanding and intelligence measures the similarity between images based on their structure and high level features, just like humans do. Two major changes we applied to SCGAN in order to make a modified model are using SSIM to measure similarity between images and applying contrastive loss principles to the similarity constraint. The modified model performs better using FID and FactorVAE metrics. The modified model also has better generalisability compared to other models. Keywords Generative Adversarial Nets, Unsupervised Learning, Disentangled Representation Learning, Contrastive Disentanglement, SSIM
A field study on Polish customers' attitude towards a service robot in a cafe

More and more stores in Poland are adopting robots as customer assistants or promotional tools. However, customer attitudes to such novelty remain unexplored. This study focused on the role of social robots in self-service cafes. This domain has not been explored in Poland before, and there is not much research in other countries as well. We conducted a field study in two cafes with a teleoperated robot Nao, which sat next to the counter serving as an assistant to a human barista. We observed customer behavior, conducted semi-structured interviews and questionnaires with the customers. The results show that Polish customers are neutral and insecure about robots. However, they do not exhibit a total dislike of these technologies. We considered three stages of the interaction and identified features of each stage that need to be designed carefully to yield user satisfaction.
Multi-domain analysis and prediction of the light emitted by an inductively coupled plasma jet

Inductively coupled plasma wind tunnels are crucial for replicating hypersonic flight conditions in ground testing. Achieving the desired conditions (e.g., stagnation-point heat fluxes and enthalpies during atmospheric reentry) requires a careful selection of operating inputs, such as mass flow, gas composition, nozzle geometry, torch power, chamber pressure, and probing location along the plasma jet. The study presented herein focuses on the influence of the torch power and chamber pressure on the plasma jet dynamics within the 350 kW Plasmatron X ICP facility at the University of Illinois at Urbana-Champaign. A multi-domain analysis of the jet behavior under selected power-pressure conditions is presented in terms of emitted light measurements collected using high-speed imaging. We then use Gaussian Process Regression to develop a data-informed learning framework for predicting Plasmatron X jet profiles at unseen pressure and power test conditions. Understanding the physics behind the dynamics of high-enthalpy flows, particularly plasma jets, is the key to properly design material testing, perform diagnostics, and develop accurate simulation models
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.