Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Shot VR

The physical world has contents at varying depths, allowing our eye to squish or relax to focus at different distances; this is commonly referred to as the accommodation cue for human eyes. To allow a realistic 3D viewing experience, it is crucial to support the accommodation cue—the 3D display needs to show contents at different depths. However, supporting the native focusing of the eye has been an immense challenge to 3D displays. Commercial near-eye VR displays, which use binocular disparity as the primary cue for inducing depth perception, fail this challenge since all contents they show arise from a fixed depth—ignoring the focusing of the eye. Many research prototypes of VR displays do account for the accommodation cue; however, supporting accommodation cues invariably comes with performance loss among other typically assessed criteria for 3D displays. To tackle these challenges, we present a novel kind of near-eye 3D display that can create 3D scenes supporting realistic accommodation cues in a single shot, i.e., without using time multiplexing or eye tracking. This display, which we present in our demo, can stream 3D content over a large depth range, at 4K spatial resolution, and in real-time. Our display offers an exciting step forward towards a truly immersive real-time 3D experience. Participants will get to enjoy 3D movies and play interactive games in their demo experience.
DETERMINANTS OF VOLUNTARY DISCLOSURE QUALITY IN EMERGING ECONOMIES: EVIDENCE FROM FIRMS LISTED IN NIGERIA STOCK EXCHANGE

The main objective of this study is to analyze the determinants of voluntary disclosure quality among listed firms in emerging economy. Unweighted voluntary disclosure quality index was used as the dependent variable, while firm disclosure quality determinants such as size, profitability, Board Composition and Gearing constitute the independent variables. Ex-post facto research method was employed as the research design. Data was sourced from 793 corporate annual reports of firms listed in the Nigeria stock exchange between 2000 to 2014.Two models; one based on the combined sample and the other on the non-financial companies only were developed. Generalized Method of Moment (GMM) regression techniques is used to test the statistical significance of the hypotheses of the study. Using the reduced model and full model, the results indicate that firm size and Board Composition has significant and positive relationship with voluntary disclosure quality. On the other hand, profitability and gearing were found to be significant and negatively related to the voluntary disclosure quality of listed firms in Nigeria. The implication of these findings is that large firm discloses more extensive information than do small firms. Moreover, highly profitable firms in Nigeria tend to disclose less information to avoid political attention in the form of pressure for the exercise of social responsibility and greater regulations such as price control and higher corporate taxes. Moreover, high number of non-executive independent directors on the board promotes extensive disclosure than do firms with less number of non-executive independent directors and finally, firms with more debt in their capital structure tend to provide less information to gain access to the capital markets and to reassure investors of the possibility of continuous going concern by the firms.
PULSED NOISE IN SELF-SUSTAINED OSCILLATIONS OF MUSICAL INSTRUMENTS

Musical tones from bowed strings and winds, though nearly periodic, have a noise component that is a subtle but crucial part of the sound. Attempts to simulate these instruments in digital electronic synthesis are often deficient with regard to the exact quality of the noise component. A new description of the noise generation mechanism accounts for some of the noise present in self-sustained mechanical oscillators. Analyses have verified the existence of the predicted noise and digital simulations have synthesized tones with improved bow and breath noise.
SyzDescribe: Principled, Automated, Static Generation of Syscall Descriptions for Kernel Drivers

Fuzz testing operating system kernels has been effective overall in recent years. For example, syzkaller manages to find thousands of bugs in the Linux kernel since 2017. One necessary component of syzkaller is a collection of syscall descriptions that are often provided by human experts. However, to our knowledge, current syscall descriptions are largely written manually, which is both time-consuming and error-prone. It is especially challenging considering that there are many kernel drivers (for new hardware devices and beyond) that are continuously being developed and evolving over time. In this paper, we present a principled solution for generating syscall descriptions for Linux kernel drivers. At its core, we summarize and model the key invariants or programming conventions, extracted from the "contract" between the core kernel and drivers. This allows us to understand programmatically how a kernel driver is initialized and how its associated interfaces are constructed. With this insight, we have developed a solution in a tool called SyzDescribe that has been tested for over hundreds of kernel drivers. We show that the syscall descriptions produced by SyzDescribe are competitive to manually-curated ones, and much better than prior work (i.e., DIFUZE and KSG). Finally, we analyze the gap between our descriptions and the ground truth and point to future improvement opportunities.
Investigation of Decision-Making Computational cortex-like mechanism models in Neuroeducation

Background and Objectives: Methods of pedagogy, for a long time, have been a social practice based on direct experiences from the past, and many of teaching methods have been traditionally formed. These methods were not supported by a scientific theory-based system and failed to keep up with the rapidly changing social needs. Neuroeducation is an experimental science and an interdisciplinary field that applies the latest theoretical advances in the human brain and psychology to education. By studying the theories in neuroeducation, it enables us to improve optimal presentation of contents for a course, teaching strategy and teaching methods of new subjects, and simultaneously improve students' computational thinking ability. One way that these theories can be tested is to study how decision-making is formed in the human brain. Goal-based decisions and behaviors depend on both sensory evidence mechanisms that collect perceptual information from the outside and mechanisms that select appropriate behaviors based on that sensory information which is decision-making mechanisms. Behaviorism is one of the basic foundations of theories of learning and behavior. One way to study behavior in detail is to use computational models based on brain biology that have been developed by neuroscientists in recent years. In this paper we try to explore the relationship between neuroeducation and pedagogy by studying theoretical achievements in computational neuroscience, cognitive neuroscience and psychology. Methods: To investigate this issue, a neural-computational model of brain-based for decision making was used. This model consists of two recurrent dynamic neurons that can explore how perceptual decisions are formed in complex behavioral spaces and show the key
Prevalence and correlates of Metabolic Syndrome among adults in Freetown, Sierra Leone: comparative analysis of NCEP ATP III, IDF and Harmonized ATP III

Background: Metabolic Syndrome (MS) is a global health concern, especially for low and middle-income countries with limited resources and information. The study's objective was to assess the prevalence of MS in Freetown, Sierra Leone, using the Adult Treatment Panel III (ATP III), International Diabetes Federation (IDF) and Harmonised ATP III. Additionally, we aimed to establish the concordance between these three different criteria used. Methods: This community-based health screening survey was conducted from October 2019 to October 2022. A multistage stratified random design was used to select adults aged 20 years and above. Mean, interquartile range (IQR) and logistic regression were used for statistical analysis. The kappa coefficient statistics resolved the agreement between these defined criteria. Results: The prevalence for NCEP ATP III, Harmonised ATP III and IDF criteria was 11.8% (95% CI:9.0-15.15), 14.3% (95 CI: 11.-18.0) and 8.5% (95% CI: 6.2 -11.2)respectively for the 2394 selected adults. The kappa coefficient (k) agreement between the MS is: Harmonized ATP III and IDF criteria = [208(60.8%);(k=0.62)]; Harmonize ATP III and NCEP ATP III = [(201) (58.7%),(k=071), while IDF and NCEP ATP III was [132 (38.6%); (k=0.52). In the multivariable regression analysis, waist circumference correlated with all three MS criteria: ATP III [AOR=0.85; C.I 95%: (0.40-1.78), p=0.032], Harmonized ATP III [AOR = 1.14; CI 95%: (0.62-2.11), p=0.0024], IDF [AOR =1.06; C.I 95% (0.52-2.16), p=0.018] Conclusion: We reported a high prevalence of MS in Freetown, Sierra Leone and identified waist circumference as a major risk factor for MS.This underscore the crucial role of health education and effective management of MS in Sierra Leone.
Short-term Metallurgical Load Forecasting Based on Adaptive Ensemble Learning

Accuracy and rapidity are the primary objectives of load forecasting, and also the necessary conditions for ensuring power supply and production schedule. However, in actual production, due to the variability of operating modes and the interference of production environment, the difficulties such as non-stable and high fluctuation are in the power load prediction. In light of this, we propose an adaptive hybrid prediction model based on Discrete Wavelet decomposition(DWT). It is well known that DWT can show local features such as mutation and fluctuation in the sequence, and has excellent multi-scale analysis ability. Adopt energy entropy evaluate the aggregation of wavelet coefficients in order to obtain the subsequence with the highest degree of preserving the main frequency of the original signal. Random Forest (RF) and Long Short-Term Memory (LSTM) were used to track low-frequency profile and high-frequency detail fluctuations respectively. Further, parameters of the heterogeneou models are optimized using the Particle Swarm Optimization(PSO) to improve the adaptability of hybrid models to different frequency components. Compared with other excellent metallurgical load forecasting techniques, the effectiveness and superiority of the proposed model are verified by experiments on the actual industrial data set of an electrical smelting furnaces for magnesia.
High-resolution and localized parametric embodied impact calculator of PV systems

Buildings are responsible for a large amount of greenhouse gas emissions in the world. In order to decarbonize the electricity grid and reduce the environmental impact of the building stock, photovoltaic panels can be installed. However, in order to assess the environmental impact of PVs, the whole life cycle has to be considered including embodied emissions. Several options for photovoltaics exist on the market or are under development including silicon-based panels, thin films, and third generation panels. Currently, many configurations of the panels exist making it difficult to estimate the embodied impact. The goal of this paper is to close this gap by providing a parametric PV carbon calculator for designers and decision-makers. In this study, the embodied impact of different PV types and configurations is assessed. First, the life cycle inventories data and bill of quantities for different generations’ panel types are gathered. Second, life cycle impact assessment is performed. The results of the analysis are presented in a form of a software application allowing users to select the panel’s composition, e.g., frame and glass type, cell type, encapsulant, etc. The developed application will assist in understanding the impact of choices made in regards to PV systems and will support engineers and architects in the selection of the photovoltaic panels from embodied impact perspective.
The Generalist Approach to Frame Problems

The frame problem commonly appears in the field of artificial intelligence and in related philosophical literature. It pertains to the difficulty of making meaningful inferences in dynamic contexts. We discuss the frame problem and its manifestations in the practical design of artificial agents, and we set out some criteria against which the effectiveness of such an agent may be judged. Finally, we claim that technology currently exists in the form of a new clustering technique - dimensional clustering - which can enable an agent to satisfy these criteria. We justify this claim with three case studies where we apply dimensional clustering to data sets of varied nature.
SolVis: A pilot study to facilitate understanding of real-time solar energy production through energy visualizations in learning environments

The transition to a carbon neutral society requires actions on all levels of society. Hereby, renewable energy - especially considering the current European energy crisis – such as solar energy will play an increasing role. To promote a more extensive use of solar energy and thus contributing to a faster energy transition, it is valuable to engage all citizens, not least children. How can real-time visualization of solar energy production in learning environments contribute to increased understanding of energy production and consumption? Educational environments can be a powerful platform to cultivate an interest in more environmentally and sustainable behaviour at an early age. However, energy is intangible, abstract, and difficult to grasp for non-experts. Digital tools incorporating energy visualization may be able to facilitate an increased understanding of energy units and measurements. The City of Gothenburg, Sweden, has the ambition to place photovoltaics on all roofs of schools and kindergartens, a process that is already in progress. The solar energy production is measured in real-time and only used for building operation management. This paper presents a study that develops a concept for a digital and educational visualization tool for school children aged 9 to 12, with the goal to engage, facilitate a lasting learning outcome, and awaken curiosity in solar energy. A prototype of a real-time visualization tool was developed, which in a playful way visualises the solar electricity production of the school property and compares it to the energy consumption of everyday activities, displayed on a centrally placed screen in the school. It was installed in four pilot schools and evaluated with 233 pupils. Results indicate several potentials of the visualization: to communicate local solar energy production to pupils, triggering thoughts, ideas, and a will to continue to learn more about solar energy production.
Target-driven multi-input mapless robot navigation with deep reinforcement learning

Collision-free navigation without pre-built maps is a promising and challenging problem. To make full use of the information in the environment, we propose a multi-input mapless navigation framework that combines both vision-based and ranging-based approaches. It takes the position and orientation of the robot relative to the target as the reference, incorporating RGB-D images and distance measurements as state inputs to the deep reinforcement learning model. In the framework, Dual-VAE is designed to encode RGB images and depth images simultaneously, in order to effectively mine the intrinsic information of RGB-D images. Moreover, we engineer a special reward function that enables the robot to navigate with a smooth trajectory and leave redundant space with obstacles for other tasks. Target-driven multi-input mapless navigation framework has been experimentally proven to achieve satisfactory performance in simulation environments. Our source code and simulation environments are released at https://github.com/YosephYu/MultiInput-Mapless-Navigation.
Assessment of today’s condition of Modernist architecture in Skopje in terms of energy efficiency and authentic architectural appearance using actor network theory

The identity of the city of Skopje was completely lost after the earthquake in 1963. The buildings from the post – earthquake period were built in Modernist architectural style and they play a major role in creating the new identity of the city. On the other hand, in terms of energy efficiency, these buildings have very poor thermal properties. In order to improve the energy efficiency, retrofit interventions have been constantly implemented and their authentic appearance has been significantly compromised. Many buildings have already been changed to an unrecognizable state, which degrades the architectural identity of Skopje. In order to explore the connection and implication between the reconstruction of the city and the current condition of the buildings in terms of materials, thermal properties, authenticity, degradation and retrofit measures, the methodology of actor network theory (ANT) has been used. The results showed that certain type of buildings are the most vulnerable ones in terms of their energy efficiency issues, sustainability, authentic appearance and in process of significant degradation.
Developing approaches and strategies to promote increased active mobility in urban city neighbourhood

With the world’s population expanding quickly, cities must deal with problems such as traffic jams, air pollution, car accidents, and urban sprawl. Walking and cycling are non-motorized modes of transport that require little infrastructure and don’t utilize any fossil fuel energy. In comparison to motorized modes, they are easily adaptable and inexpensive to maintain for both users and governments. Therefore, the goal of this paper is to identify strategies for greater active mobility through non-motorized modes of transport. Through a document study, methods that can be utilized as incentives to improve walkability and bikeability in the Elgeseter neighbourhood of the City of Trondheim, Norway have been identified. The analysis addresses following research question: “What may persuade Elgeseter district commuters or travellers to modify their behaviour toward more walking and biking? ” This study reveals five topics of interest: Identifying existing difficulties in the district, Evaluation of current pedestrian, bicycle, and vehicle links, as well as meeting places and open spaces, and identification of opportunities to improve them, How to outline planning concepts, Methods and design recommendations for enhancing walking and micro transportation, as well as The importance of accessibility, safety, and comfort in attracting people to active mobility. The main argument is that a multi-dimensional approach is required to persuade more people to choose environmentally friendly, sustainable modes of transportation as a lifestyle choice, and function better than compulsion. The research presented in this paper contributes to our understanding of the factors that influence commuters’ decision to engage in active mobility in their urban city neighbourhood.
QUADRATURE BASED MOMENT METHODS FOR MODELING SUPERSONIC COMBUSTION

The transported probability density function (PDF) approach is a powerful technique for large eddy simulation (LES) based modeling of scramjet combustors [1]. In this approach, a highdimensional transport equation for the joint composition-enthalpy PDF needs to be solved. Quadrature based approaches provide deterministic Eulerian methods for solving the joint-PDF transport equation. The direct quadrature method of moments (DQMOM) is one quadraturebased approach developed for modeling combustion [2]. The DQMOM has been successfully implemented for general combustion configurations include supersonic combusor [3] and spray reaction problems [4]. Recently, an improved version of quadrature-based method, called semidiscrete quadrature method of moments (SeQMOM), has been formulated [5]. In this presentation, these quadrature based method of moments are discussed and applied to canonical flow configurations.
Knowledge graph based AR content adaptive generation for 5G communication network maintenance services

5G technology puts forward new requirements for communication network human maintenance services. However, with the complexity of 5G maintenance knowledge and the diversification of maintenance scenarios, the ability of maintenance engineer cannot be greatly improved in a short time. This paper proposed a knowledge graph (KG) based augmented reality (AR) content adaptive generation method for 5G communication network maintenance services. The KG and AR technology are used to reduce the cognitive burden of manual maintenance and improve the efficiency of communication network maintenance.
Indigenous Oral and Gut Phages Defeat the Deadly NDM-1 Superbug

Aim: Antibiotics treat various diseases by targeting microorganisms by killing them or reducing their multiplication rate. New Delhi Metallo-beta-lactamase-1 (NDM-1) is produced by bacteria possessing the resistance gene blaNDM-1, the enzyme that makes bacteria resistant to beta-lactams. Bacteriophages, especially Lactococcus, have shown their ability to break down lactams. Hence, the current study computationally evaluated the binding potential of Lactococcus bacteriophages with NDM using Molecular docking and dynamics. Methods: Modelling of NDM I-TASSER for Main tail protein gp19 OS=Lactococcus phage LL-H or Lactobacillus delbrueckii subsp. lactis after downloading from UNIPROT ID- Q38344. Cluspro tool helps in Understanding cellular function and organization with protein-protein interactions. MD simulations(19) typically compute atom movements over time. Simulations were used to predict the ligand binding status in the physiological environment. Results: The best binding affinity score was found -1040.6 Kcal/mol compared to other docking scores. MD simulations show in RMSD values for target remains within 1.0 Angstrom, which is acceptable. The ligand-protein fit to receptor protein RMSD values of 2.752 fluctuates within 1.5 Angstrom after equilibration. Conclusions: Lactococcus bacteriophages showed a strong affinity to the NDM. Hence, this hypothesis, supported by evidence from a computational approach, will solve this life-threatening superbug problem.
Where did the line go? A case of a duplicated left superior vena cava

A 24-year-old male was transferred to our intensive care unit with acute leukemia, tumor lysis syndrome and bilateral pulmonary embolism. A peripherally inserted central catheter (PICC) was placed on the left brachial vein. A post-insertion chest X-ray showed an abnormal left mediastinum catheter trajectory (Fig. 1A). To rule out arterial placement, the following steps were taken: First, the catheter was transduced, exhibiting a mean arterial pressure of 0 mmHg and no arterial waveforms. Second, blood gasses were performed on a PICC line blood sample (pH = 7.4, PaCO 2 = 47