Miracle-dz/UniFAR-FTUs huggingface dataset:
A conceptual framework for enabling a value-adding digital transformation

Organizations face various challenges that inhibit the successful enactment of a value-adding digital transformation. This study develops a conceptual framework aimed at assisting organizations to successfully undergo digital transformation. The framework is developed by synthesizing all the relevant concepts identified from the existing scholarship and validated through semi-structured interviews with subject matter experts and a case study. The framework consists of two phases. Phase one consists of five sub-phases, namely, industry disruption, customer needs identification, customer value design, digital capability assessment, and challenges assessment. The outcomes from these sub-phases are integrated in Phase 2, which comprises of the sub-phases: assessment report, value equation, and challenges index. The framework provides the user organizations with suggested tools and processes to achieve the desired outcomes, while allowing the organization to make tool selections based on their needs, permitting that the outcomes remain the same. The research and its resulting conceptual framework contribute by providing organizations with a resource that will guide them in their decision-making process regarding digital transformation, and it also contributes as an educational tool in Industry 4.0 principles and its integrated applications. The conceptual framework further serves as basis that can be evolved through future research to include new concepts and account for new challenges as technology, industries and people evolve.
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By representing the ﬁeld content as well as the particle creation operators in terms of fermionic Fock operators, we compute the corresponding matrix elements of the Federbush model. Only when these matrix elements satisfy the form factor consistency equations involving anyonic factors of local commutativity, the corresponding operators are local. We carry out the ultraviolet limit, analyse the momentum space cluster properties and demonstrate how the Federbush model can be obtained from the SU (3) 3 -homogeneous sine-Gordon model. We propose a new Lagrangian which on one hand constitutes a generalization of the Federbush model in a Lie algebraic fashion and on the other a certain limit of the homogeneous sine-Gordon models.
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We investigate proposals of how the form factor approach to compute correlation functions at zero temperature can be extended to ﬁnite temperature. For the two-point correlation function we conclude that the suggestion to use the usual form factor expansion with the modiﬁcation of introducing dressing functions of various kinds is only suitable for free theories. Dynamically interacting theories require a more severe change of the form factor program.
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In this paper we present a general method for information extraction that exploits the features of data compression techniques. We ﬁrst deﬁne and focus our attention on the so-called dictionary of a sequence. Dictionaries are intrinsically interesting and a study of their features can be of great usefulness to investigate the properties of the sequences they have been extracted from (e
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What processes can explain how very large populations are able to converge on the use of a particular word or grammatical construction without global coordination? Answering this question helps to understand why new language constructs usually propagate along an S-shaped curve with a rather sudden transition towards global agreement. It also helps to analyze and design new technologies that support or orchestrate self-organizing communication systems, such as recent social tagging systems for the web. The article introduces and studies a microscopic model of communicating autonomous agents performing language games without any central control. We show that the system undergoes a disorder/order transition, going trough a sharp symmetry breaking process to reach a shared set of conventions. Before the transition, the system builds up non-trivial scale-invariant correlations, for instance in the distribution of competing synonyms, which display a Zipf-like law. These correlations make the system ready for the transition towards shared conventions, which, observed on the time-scale of collective behaviors, becomes sharper and sharper with system size. This surprising result not only explains why human language can scale up to very large populations but also suggests ways to optimize artiﬁcial semiotic dynamics.
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The creation and validation of product line software architectures are inherently more complex than those of software architectures for single systems. This paper compares a process for creating and evaluating a traditional, one-of-a-kind software architecture with one for a reference software architecture. The comparison is done in the context of PuLSE-DSSA, a customizable process that integrates both product line architecture creation and evaluation
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Existing approaches to modeling software systems all too often neglect the issue of component mismatch identification and resolution. The traditional view of software development over-emphasizes synthesis at the expense of analysis - the latter frequently being seen as a problem one only needs to deal with during the integration stage towards the end of a development project. This paper discusses three software modeling and analysis techniques, all tool supported, and emphasizes the vital role analysis can play in identifying and resolving risks early on. This work also combines model based development with component based development (e.g., COTS and legacy systems) and shows how their mismatch detection capabilities complement each other in providing a more comprehensive coverage of development risks.
Charging Time Prediction of Electric Vehicle Charging Pile via Momentum-incorporated Non-negative Latent-factorization-of-tensors with Swish-regularization

As electric vehicles (EVs) are wildly applied in our daily life, how to rationally deploy the public charging piles has become a thorny issue. Hence, it is necessary to comprehensively consider the utilized periods and installation location of charging piles to analyze the charging time of each charging pile. However, large-scale data collection and processing is inefficient and time-consuming. To address this issue, this paper constructs a Momentum-incorporated Non-negative Latent-factorization-of-tensors with Swish-regularization (MNLS) model. Its main ideas include: 1) incorporating a general momentum method into the model to achieve rapid convergence; and 2) utilizing a swish regularization scheme to address the over-fitting issue Experimental results on four real-world datasets demonstrate that the MNLS model achieves higher prediction accuracy compared to the state-of-the-art forecasting methods.
Study on in-situ measurement of water-resisting ability of coal seam floor rock mass

The principle, method and process of test on water resisting ability of coal seam floor rock mass are introduced in this paper.The reliability of the test result is analyzed, and the water resisting ability of the floor rock mass of coal seam No.6 in Yang Zhuang Coal Mine is evaluated.A new way for forecasting floor water gush in coal mine is provided.
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There is a wealth of user-centred evaluation methods (UEMs) to support the analyst in assessing interactive systems. Many of these support detailed aspects of use – for example: Is the feedback helpful? Are labels appropriate? Is the task structure optimal? Few UEMs encourage the analyst to step back and consider how well a system supports users’ conceptual understandings and system utility. In this paper, we present CASSM, a method which focuses on the quality of ‘fit’ between users and an interactive system. We describe the methodology of conducting a CASSM analysis and illustrate the approach with three contrasting worked examples (a robotic arm, a digital library system and a drawing tool) that demonstrate different depths of analysis. We show how CASSM can help identify re-design possibilities to improve system utility. CASSM complements established evaluation methods by focusing on conceptual structures rather than procedures. Prototype tool support for completing a CASSM analysis is provided by Cassata, an open source development.
Can Semantic-based Filtering of Dynamic Objects improve Visual SLAM and Visual Odometry?

This work introduces a novel approach to improve robot perception in dynamic environments using Semantic Filtering. The goal is to enhance Visual Simultaneous Localization and Mapping (V-SLAM) and Visual Odometry (VO) tasks by excluding feature points associated with moving objects. Four different approaches for semantic extraction, namely YOLOv3, DeepLabv3 with two different backbones, and Mask R-CNN, were evaluated. The framework was tested on various datasets, including KITTI, TUM and a simulated sequence generated on AirSim. The results demonstrated that the proposed semantic filtering significantly reduced estimation errors in VO tasks, with average error reduction ranging from 2.81% to 15.98%, while the results for V-SLAM were similar to the base work, especially for sequences with detected loops. Although fewer keypoints are used, the estimations benefit from the points excluded in VO. More experiments are needed to address the effects in VSLAM due to the presence of loop closure and the nature of the datasets.
Co-seismic displacements of the 1994 Northridge, California, earthquake

The 17 January 1994 Northridge, California, earthquake significantly deformed the Earth's crust in the epicentral region. Displacements of 66 survey stations determined from Global Positioning System (GPS) observations collected before and after the earthquake show that individual stations were uplifted by up to 417 ± 5 mm and displaced horizontally by up to 216 ± 3 mm. Using these displacements, we estimate parameters of a uniform-slip model. Fault geometry and slip are estimated independent of seismological information, using Monte Carlo optimization techniques that minimize the model residuals. The plane that best fits the geodetic data lies 1 to 2 km above the plane indicated by aftershock seismicity. Modeling for distributed slip on a coplanar, yet larger model fault indicates that a high-slip patch occurred up-dip and northwest of the mainshock hypocenter and that less than 1 m of slip occurred in the uppermost 5 km of the crust. This finding is consistent with the lack of clear surface rupture and with the notion that the intersection with the fault that ruptured in 1971 formed the up-dip terminus of slip in the Northridge earthquake. Displacements predicted by either of these simple models explain most of the variance in the data within 50 km of the epicenter. On average, however, the scatter of the residuals is twice the data uncertainties, and in some areas, there is significant systematic misfit to either model. The co-seismic contributions of aftershocks are insufficient to explain this mismatch, indicating that the source geometry is more complicated than a single rectangular plane.
Clumpy star formation and an obscured nuclear starburst in the luminous dusty z=4 galaxy GN20 seen by MIRI/JWST

Dusty star-forming galaxies emit most of their light at far-infrared to millimeter wavelengths as their star formation is highly obscured. Far-infrared and millimeter observations have revealed their dust, neutral and molecular gas properties. The sensitivity of JWST at rest-frame optical and near-infrared wavelengths now allows the study of the stellar and ionized gas content. We investigate the spatially resolved distribution and kinematics of the ionized gas in GN20, a dusty star-forming galaxy at $z$=4.0548. We present deep MIRI/MRS integral field spectroscopy of the near-infrared rest-frame emission of GN20. We detect spatially resolved out to a radius of 6 kpc, distributed in a clumpy morphology. The star formation rate derived from (144 pm 9 is only 7.7 $ 0.5 $ of the infrared star formation rate (1860 pm 90 We attribute this to very high extinction (A$_V$ = 17.2 pm 0.4 mag, or V,mixed $ = 44 pm 3 mag), especially in the nucleus of GN20, where only faint is detected, suggesting a deeply buried starburst. We identify four, spatially unresolved, clumps in the emission. Based on the double peaked profile, we find that each clump consists of at least two sub-clumps. We find mass upper limits consistent with them being formed in a gravitationally unstable gaseous disk. The ultraviolet bright region of GN20 does not have any detected emission, suggesting an age of more than 10 Myrs for this region of the galaxy. From the rotation profile of we conclude that the gas kinematics are rotationally dominated and the rot m 1.4$ is similar to low-redshift luminous infrared galaxies. From the kinematics, we cannot distinguish between a rotational profile of a large disk and a late stage merger mimicking a disk. We speculate that GN20 is in the late stage of a major merger, where the clumps in a large gas-rich disk are created by the major merger, while the central starburst is driven by the merger event.
Beyond Low Rank: A Graph-Based Propagation Approach to Tensor Completion for Multi-Acquisition Scenarios

Tensor completion refers to the problem of recovering the missing, corrupted or unobserved entries in data represented by tensors. In this paper, we tackle the tensor completion problem in the scenario in which multiple tensor acquisitions are available and do so without placing constraints on the underlying tensor's rank. Whereas previous tensor completion work primarily focuses on low-rank completion methods, we propose a novel graph-based diffusion approach to the problem. Referred to as GraphProp, the method propagates observed entries around a graph-based representation of the tensor in order to recover the missing entries. A series of experiments have been performed to validate the presented approach, including a synthetically-generated tensor recovery experiment which shows that the method can be used to recover both low and high rank tensor entries. The successful tensor completion capabilities of the approach are also demonstrated on a real-world completion problem from the field of multispectral remote sensing completion. Using data acquired from the Landsat 7 platform, we synthetically obscure image sections in order to simulate the scenario in which image acquisitions overlap only partially. In these tests, we benchmark against alternative tensor completion approaches as well as existing graph signal recovery methods, demonstrating the superior reconstruction performance of our method versus the state of the art.
Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations

Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations independently. We aim to imbue Intelligent Tutoring Systems with this ability using few-shot learning capability of Large Language Models. Our work proposes a novel prompting technique, Assertion Enhanced Few-Shot Learning, to facilitate the generation of accurate, detailed oriented educational explanations. Our central hypothesis is that, in educational domain, few-shot demonstrations are necessary but not a sufficient condition for quality explanation generation. We conducted a study involving 12 in-service teachers, comparing our approach to Traditional Few-Shot Learning. The results show that Assertion Enhanced Few-Shot Learning improves explanation accuracy by 15% and yields higher-quality explanations, as evaluated by teachers. We also conduct a qualitative ablation study to factor the impact of assertions to provide educator-friendly prompting guidelines for generating explanations in their domain of interest.
A Comprehensive Web Application for Chronic Kidney Disease Prediction with Cuisine-Centric Diet Recommendation

Despite advancements in detection and treatment, there exists a critical gap in patient care-post-diagnosis guidance and support. In response to this challenge, our proposed work presents a novel integrated system designed to guide and empower CKD patients towards a healthier and more fulfilling life. The proposed work aims at creating an integrated system for the early detection of chronic kidney disease (CKD) using Machine Learning technology, a diet recommender for the prevention of disease from further development. The model output is integrated with the Flask framework and the front end, which is developed in HTML (includes Java Script) is used to receive user input on various parameters needed to decide on the early detection of kidney disease. This model is deployed into the IBM cloud using API keys and scoring endpoints. The chronic kidney disease prediction and diet recommendations are displayed in a Kidney Guard web application that has multiple user-engagement features such as a motivator chatbot, homemade paradise hub (for homemade recipes), prediction history, calorie recorded and burnt, soul stretch (for yoga) and sweat spot (for exercise). This work enables doctors in the early detection of CKD and cuisine-centric diet recommendation helps the users to adhere to the dietary and lifestyle changes which are holistic components in the prevention and management of CKD.
Nanotechnology in Retinal Disease: Current Concepts and Future Directions

The retina is one of the most complex and extraordinary human organs affected by genetic, metabolic, and degenerative diseases, resulting in blindness for ∼1.3 million people in the United States and over 40 million people worldwide. This translates into a huge loss of productivity, especially among younger patients with inherited retinal diseases (IRDs) and diabetic retinopathy. Age-related macular degeneration accounts for 90% of all blindness cases worldwide. The prevalence of this condition is projected to reach over 5 million individuals over the next 3 decades. There are also >20 IRD phenotypes, affecting >2 million people worldwide. Nanobiotechnology uses nanotechnology for biological applications, making use of biological materials either conceptually or directly in the fabrication of new materials. Bionanotechnology, on the other hand, uses molecular biology for the purpose of creating nanostructures (ie, structures with at least 1 dimension <100 nm). Retinal applications of these technologies are developing at a rapid pace. This review includes the most current nanotechnological applications in retinal diagnostics, theranostics, drug delivery, and targeting, including the potential for nonviral vehicles such as liposomes, micelles, and dendrimers, which pose advantages over viral vectors in retinal drug delivery. Furthermore, we discuss current and future applications as surgical adjuncts and in regenerative medicine as they pertain to retinal disease. Structure and function of nanoparticles such as carbon nanotubules, quantum dots, and magnetic nanoparticles, as well as diagnostic technologies such as next-generation DNA sequencing and single-molecule bionanosensing, will also be discussed.
Supplementary: A Spatially Constrained Probabilistic Model for Robust Image Segmentation

A. Banerjee is with the Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom and the Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom. E-mail: abhirup.banerjee@cardiov.ox. ac.uk. P. Maji is with the Biomedical Imaging and Bioinformatics Lab, Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. E-mail: pmaji@isical.ac. in. This work was done when A. Banerjee was a senior research fellow of Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. This work is partially an outcome of the R&D work undertaken by P. Maji in the project under the Visvesvaraya PhD Scheme of Ministry of Electronics and Information Technology, Government of India. B. tHMRF: Student’s t-Distribution for HMRF Model Based Segmentation
Supplementary: Self-Supervised 3D Human Pose Estimation Via Part Guided Novel Image Synthesis

The part transformation module takes a set of 2D pose joint locations q and outputs a spatial part-wise pose map using a fixed dictionary of canonical part maps as shown in Fig 1, right panel. Each canonical part, φ c is represented by 2 anchored joints, r1 and r2. Here, l(j1) denotes parent joint index of the limb l, whereas l(j2) denotes index of the child joint. Similarly, for a camera projected 2D pose q ∈ R , the spatial joint locations for the limb l are denoted by q1 for the parent and q2 for the child joint. Note that, in the canonical part map, φ c all the limbs or body parts are aligned along the positive X-axis, i.e. a vector directed from r1 to r2 makes an angle of 0◦ with respect to positive X-axis. Also, mid-point of the line-segment joining r1 to r2 aligns with the origin (0, 0) while the corresponding spatial indices, u ∈ [−H/2, H/2]× [−W/2,W/2], whereH andW are respectively height and width of the spatial map. The rotation angle for the spatial transformation, S required to align the canonical part along the vector directed from q1 to q2 is computed as
Supplementary: Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules

The query generator is a recurrent module proposed by MAC [2], also used in SNMN [1], to obtain question-related reasoning operation qt at each step t. The recurrent module essentially takes its output from previous timestep t−1 i.e., qt−1 along with question features fs and fw as sentence and word embeddings respectively, and generate attention over question words at current timestep t. More specifically, the query generator applies a time-step dependent linear transformation on fs and combines it with the previous reasoning operation qt−1 as follows: u =W2([W t 1(fs) + b1; qt−1]) + b2 (1)
Supplementary: Multi-Domain Multi-Definition Landmark Localization for Small Datasets

Non-Standard Datasets. While the AnimWeb [4] and CariFace [14] datasets contain larger numbers of images, in this study, for the purpose of evaluating our method’s performance for novel domains with small datasets, we only consider a single animal from AnimWeb, the Japanese macaque, for its greater visual similarity with human faces, as well as the first 148 images of CariFace. Additionally, we utilize a small unlabeled dataset of 150 in-the-wild illusory faces [10], called pareidolias. We label the bounding boxes in addition to a 9 landmark definition, following AnimWeb [4], and refer to this dataset of PAREeidolias as the PARE dataset. We will release the include the GT landmarks and images indices from the dataset used for PARE.
Supplementary:Human Body Model Fitting by Learned Gradient Descent

Data sampling for training. At each iteration of training, we uniformly randomly sample a pose and a shape parameter from the motion database, processed via MOSH. The pose parameters already contain global orientation of the body. In order to simulate different camera view points, we rotate the body with yaw angles that we uniformly sample from [−180◦, 180◦] and roll and pitch angles uniformly sampled from [−20◦, 20◦]. To simulate missing or erroneous detections from the 2D pose detector, we randomly drop simulated joints with a probability of 20%.