Miracle-dz/UniFAR-FTUs huggingface dataset:
International Journal of Public Health Science (IJPHS)

Received Dec 22, 2021 Revised Jan 23, 2022 Accepted Feb 24, 2022 One of the key strategies to stop the increase of coronavirus disease 2019 (COVID-19) cases is vaccines. The uncertainty and refusal of Filipinos to get the COVID-19 vaccine will be a crucial barrier to achieve the immunization coverage required for population protection. Thus, this study was conducted to determine the knowledge and attitude of the Nueva Ecija University of Science and Technology students and faculty and staff towards the COVID-19 vaccine. A descriptive study approach and total sampling were adopted. A questionnaire was constructed to accumulate data on the respondents' profile, knowledge, attitude and source of information about the vaccine for COVID-19. Informed consent and permission to conduct were acquired. Various statistical tools were used to analyze the data. The respondents were knowledgeable and had positive attitude towards the COVID-19 vaccine. Their primary source of information was from social media on the internet and television. There is a positive association between their attitude and knowledge of the COVID-19 vaccine, which means that they are closer to having a positive attitude when they are knowledgeable about the COVID-19 vaccine. Therefore, health education campaigns to increase their knowledge or awareness about the COVID-19 vaccine should encourage them to receive a COVID-19 vaccine shot.
Eclectic Rule-Extraction from Support Vector Machines

superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule-extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.
Real-Time Shadows

Shadows are important elements in creating a realistic image and in providing the user with visual cues about object placement. A review of many different shadow algorithms is found in the survey published by Woo et al. [23] and in Watt and Watt’s book [21]. Here we will present the most important real-time algorithms for dynamic shadows. The first section handles the special case of shadows cast on planar surfaces, and the second section covers more general shadow algorithms, i.e., casting shadows onto arbitrary surfaces.
Daily Access to Local Foods for School Meals: Key Drivers

Farm-to-school programs began in the 1990s and have been encouraged by the U.S. Department of Agriculture (USDA) through grant funding, technical assistance, and changes to school meal procurement regulations. In 2012, USDA’s Farm to School Program was formally established to improve access to local foods in eligible schools. Today, more than 4 in 10 school districts report serving local foods or implementing other farm-to-school activities. To examine progress toward USDA’s goal of daily availability of locally produced foods for all students and to identify potential targets for technical assistance, this report uses data from the 2013 Farm to School Census to measure the prevalence of school districts that serve local food daily and the characteristics of those districts.
Enhancement of Attention and Cognitive Skills using EEG based Neurofeedback Game

Neurofeedback, the self-regulation of brain signals recorded using Electroencephalogram (EEG), allows Brain-Computer Interface (BCI) users to enhance cognitive as well as motor functions using specific training strategies. Therapeutic effects of neurofeedback (by the induction of neuroplasticity) on treatment of people with neurological disorders such as Attention-Deficit Hyperactive Disorder (ADHD), dementia and stroke have been reported in literature. In this paper, we investigate the impact of a neurofeedback based BCI game on the enhancement of attention and cognitive skills of healthy subjects. The BCI game is controlled by player’s attention-related EEG signal. In the proposed training paradigm, subjects play the neurofeedback game regularly for a period of 5 days. The experimental analysis of player’s attention level (measured by entropy values of EEG) and the comparison of cognitive test results demonstrate the benefits of practicing BCI based neurofeedback game in the enhancement of attention/cognitive skills.
Designing Spellcasters from Clinician Perspectives

JARED DUVAL, University of California Santa Cruz, USA RUTUL THAKKAR, University of California Santa Cruz, USA DELONG DU, University of California Santa Cruz, USA KASSANDRA CHIN, University of California Santa Cruz, USA SHERRY LUO, University of California Santa Cruz, USA AVIV ELOR, University of California Santa Cruz, USA MAGY SEIF EL-NASR, University of California Santa Cruz, USA MICHAEL JOHN, University of California Santa Cruz, USA
Modelling population dynamics and persistence in fragmented landscapes

Patch geometry and habitat quality among patches are widely recognized as important factors affecting population dynamics in fragmented landscapes. Little is known, however, about the influence of within-patch habitat quality on population dynamics. In this paper, we investigate the relative importance of patch geometry and within-patch habitat quality in determining population dynamics using a spatially explicit, agent-based model. We simulate two mobile species that differ in their species traits: one resembles a habitat specialist and the other a habitat generalist. Habitat quality varies continuously within habitat patches in space (and time). Spatial variation in within-patch quality, together with patch area, controls population abundance of the habitat specialist. In contrast, the population size of the generalist species depends on patch area and isolation. Temporal variation in withinpatch quality is, however, less influential in driving the population resilience of both species. We conclude that specialist species are more sensitive than generalist species to within-patch variation in habitat quality. The patch area–isolation paradigm, developed in metapopulation theory, should incorporate variation in within-patch habitat quality, particularly for habitat specialists.
Smartapps, an application centric approach to high performance computing: compiler-assisted software and hardware support for reduction operations

State-of-the-art run-time systems are a poor match to diverse, dynamic distributed applications because they are designed to provide support to a wide variety of applications, without much customization to individual specific requirements. Little or no guiding information flows directly from the application to the run-time system to allow the latter to fully tailor its services to the application. As a result, the performance is disappointing. To address this problem, we propose application-centric computing, or SMART APPLICATIONS. In the executable of smart applications, the compiler embeds most run-time system services, and a performance-optimizing feedback loop that monitors the application's performance and adaptively reconfigures the application and the OS/hardware platform. At run-time, after incorporating the code's input and the system's resources and state, the SMARTAPP performs a global optimization. This optimization is instance specific and thus much more tractable than a global generic optimization between application, OS and hardware. The resulting code and resource customization should lead to major speedups. In this paper, we first describe the overall architecture of SMARTAPPS and then present some achievements to date, focusing on compiler-assisted software and hardware techniques for parallelizing reduction operations. These illustrate SMARTAPPS use of adaptive algorithm selection and moderately reconfigurable hardware.
Explicit Two-Source Extractors and Resilient Functions

A key ingredient in our construction is an explicit construction of a monotone, almostbalanced boolean function on n bits that is resilient to coalitions of size n^{1-delta}, for any delta>0. In fact, our construction is stronger in that it gives an explicit extractor for a generalization of non-oblivious bit-fixing sources on n bits, where some unknown n-q bits are chosen almost polylog(n)-wise independently, and the remaining q=n^{1-\delta} bits are chosen by an adversary as an arbitrary function of the n-q bits. The best previous construction, by Viola, achieved q=n^{1/2 \delta}.
REVEL: tactile feedback technology for augmented reality

Augmented Reality has recently emerged as one of the key application areas of interactive computer graphics and is rapidly expanding from research laboratories into everyday use. The fundamental premise of AR is to enable us to interact with virtual objects immediately and directly, seeing, feeling and manipulating them just as we do physical objects. Most AR applications, however, provide only visual augmentation of the real world and do not provide the means to let the user feel tactile, physical properties of virtual objects or to enhance the physical world with computer-generated tactile textures. The absence of tactile feedback does not allow us to take advantage of the powerful mechanisms of the human sense of touch and diminishes the quality of the experience.
Boundary Organizations: Enabling Collaboration among Unexpected Allies

Our research examines how parties challenging established social systems collaborate with defenders of those systems to achieve mutual goals. With field interviews and observations from four community projects in the open-source movement, we examine how these projects collaborated with firms defending proprietary approaches to software development. Drawing on social movement and organizational theory, we explain how challenging parties not only mobilize to achieve their goals but how they are able to transform contestation into collaboration. Open-source projects and firms held divergent interests but discovered areas of convergent interest and were able to adapt their organizing practices to collaborate through the creation of a boundary organization. By showing how boundary organizations help challengers and defenders manage four critical domains of organizing practices—governance, membership, ownership, and control over production—we provide analytic levers for determining when boundary organizations work. At the same time, we reveal the subsequent triadic role structure that unfolded among communities, the boundary organizations they designed, and firms.
“I always assumed that I wasn’t really that close to [her]”: Reasoning about invisible algorithms in the news feed

Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users’ experiences yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms’ existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed algorithm at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis , to reveal to users the difference between the algorithmically curated and an unadulterated News Feed, and used it to study how users perceive this difference. Participants were most up-set when close friends and family were not shown—they had often inferred social meaning from the ﬁltering of the feed. By the end of the study, however, participants were mostly satisﬁed with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm, however, algorithmic awareness led users to more actively engage with Facebook and bolstered their overall feelings of control on the site.
Antibacterial Properties of Essential Oil in Some Indonesian Herbs

The antibacterial activity of essential oil of five Indonesian herbs has been studied. The essential oil produced from different parts of plants (lime, lemon, Surinam cherry, fennel, and toothache plants) were extracted by water steam or hydro distillation and then examined by GCMS. The antibacterial activities of the essential oils were deter mined by measuring MIC (minimum inhibitory concentration), whereas some bacte - rial strains were used in this study such as follows: Staphylococcus aureus FNCC 0047, Bacillus subtilis ATCC 6051, B. cereus FNCC 0057, Escherichia coli IFO 0091, Pseudomonas cepacia FNCC 0063, and P. aeruginosa FNCC 0063, respectively. All the five samples used in this experiment have antibacterial activity against Gram-positive and Gram- negative bacteria. Gram-negative bacteria appear more resistant than Gram-positive bacteria. By using MIC measurement the Gram-positive and Gram-negative bacteria showed different sensitivities toward essential oils. Among the material study, the essential oil of Eugenia uniflora L (Surinam cherry) showed the highest antibacterial activity. response either to Gram-positive or Gram-negative bacteria (1500 μg), except for P. aeruginosa FNCC 0063 (4000 μg). Antibacterial activity of C. aurantifolia Swingle (Lime) is weaker than the other especially against E. coli (5000 μg). Five essential oils of aromatic Indonesian herbs in this study are potential candidates as antibiotic/antibacterial agents, can be applied as flavoring and preservative agents in cosmetic and food industry.
Computer-Aided Design

It is well-known that 3D shape and texture reconstruction from a single-view image is a very challenging and an ill-posed problem, especially without 3D supervision/ground truth. Existing neural implicit surface reconstruction approaches do easily get trapped in the local minima and cannot produce high-fidelity geometry and high-quality textures (and rendered images) under single-view setting, even with provided highly sparse depth prior. In this paper, we propose a new self-supervised learning method DiffSVR that represents a complicated surface as a new depth-aware occupancy function (DOF) and utilizes an end-to-end differentiable surface rendering paradigm to optimize the neural DOF field relying only on single-view image with highly sparse depth information. The developed surface-aware sampling , occupancy self-labeling , and differentiable surface rendering with inverse computation techniques can enhance both the neural implicit surface reconstruction and the neural renderer. The extensive experiments and comparisons on two real-world benchmark datasets (e.g., DTU and KITTI) demonstrate that our approach not only numerically outperforms the current state-of-the-art methods by a large margin, but also produces surface mesh model with qualitatively better geometric details and more accurate textures, as well as exhibits good performance on generalizability and flexibility. The code and data are available at https://github.com/akomarichev/DiffSVR. © 2023ElsevierLtd.Allrightsreserved.
Exploration and geology of the Arafura sea

Large scale systematic exploration of the Arafura Sea started in 1965. Following an aeromagnetic survey some 4,900 miles of seismic lines have been shot by Shell in their permits, which at present occupy almost 60 percent of the total acreage. The following results have been obtained: Possible Palaeozoic sediments and certain Mesozoic and Cenozoic sediments are present in the Arafura Basin, deposited on a strongly folded and faulted pre-Cambrian basement. The Mesozoic and Cenozoic overlies the pre-Mesozoic with regional unconformity. Sedimentary thicknesses gradually increase offshore to more than 20,000 feet. During Mesozoic and Cenozoic times the Arafura Basin was affected by more or less continuous subsidence, which resulted in very little deformation. Only one small closed structure, in the Money Shoal area, has been defined. Stronger pre-Mesozoic movements, which resulted locally in the formation of a westnorthwest-eastsoutheast trending graben, are evident. Structural leads in this graben might result in possible Palaeozoic fault traps. However, the presence of Palaeozoic sediments is speculative and the structures in the graben appear to have been strongly eroded prior to the deposition of the Mesozoic. In addition to structural traps, stratigraphic trapping possibilities show some promise.
Genomic imprinting in the development and evolution of psychotic spectrum conditions

I review and evaluate genetic and genomic evidence salient to the hypothesis that the development and evolution of psychotic spectrum conditions have been mediated in part by alterations of imprinted genes expressed in the brain. Evidence from the genetics and genomics of schizophrenia, bipolar disorder, major depression, Prader‐Willi syndrome, Klinefelter syndrome, and other neurogenetic conditions support the hypothesis that the etiologies of psychotic spectrum conditions commonly involve genetic and epigenetic imbalances in the effects of imprinted genes, with a bias towards increased relative effects from imprinted genes with maternal expression or other genes favouring maternal interests. By contrast, autistic spectrum conditions, including Kanner autism, Asperger syndrome, Rett syndrome, Turner syndrome, Angelman syndrome, and Beckwith‐Wiedemann syndrome, commonly engender increased relative effects from paternally expressed imprinted genes, or reduced effects from genes favouring maternal interests. Imprinted‐gene effects on the etiologies of autistic and psychotic spectrum conditions parallel the diametric effects of imprinted genes in placental and foetal development, in that psychotic spectrum conditions tend to be associated with undergrowth and relatively‐slow brain development, whereas some autistic spectrum conditions involve brain and body overgrowth, especially in foetal development and early childhood. An important role for imprinted genes in the etiologies of psychotic and autistic spectrum conditions is consistent with neurodevelopmental models of these disorders, and with predictions from the conflict theory of genomic imprinting.
A New Approach for Computing Option Prices of the Hull-White Type with Stepwise Reversion and Volatility Functions

Interest rate models, starting with Vasicek, generally include mean-reversion toward a long term level. As such models were applied in the real world to price interest-dependent instruments, including derivatives, they were extended in order to match observed market prices. One way is to introduce more stochastic factors, but the other is to introduce mean-reversion and other kinds of nonstochastic time variation for important parameters, notably volatility. Incorporating such flexibility into a single stochastic factor interest rate lattice requires some method of discretizing these functions, and it is not always easy. In this article, Jin, Gotoh and Sumita develop a new technique involving the use of Ehrenfest functions to approximate mean-reverting O-U processes. Performance of their approach is comparable to a trinomial tree, but it has the advantage of being operational for parameter values for which a trinomial breaks down. It may also facilitate pricing of more complex instruments, with barriers and other such features.
Trajectories toward the 1.5°C Paris target: Modulation by the Interdecadal Pacific Oscillation

Global temperature is rapidly approaching the 1.5°C Paris target. In the absence of external cooling influences, such as volcanic eruptions, temperature projections are centered on a breaching of the 1.5°C target, relative to 1850–1900, before 2029. The phase of the Interdecadal Pacific Oscillation (IPO) will regulate the rate at which mean temperature approaches the 1.5°C level. A transition to the positive phase of the IPO would lead to a projected exceedance of the target centered around 2026. If the Pacific Ocean remains in its negative decadal phase, the target will be reached around 5 years later, in 2031. Given the temporary slowdown in global warming between 2000 and 2014, and recent initialized decadal predictions suggestive of a turnaround in the IPO, a sustained period of rapid temperature rise might be underway. In that case, the world will reach the 1.5°C level of warming several years sooner than if the negative IPO phase persists.
Emphasizing Complementary Samples for Non-literal Cross-modal Retrieval (Supplementary Material)

We provide supplemental results to those in our main text. We first provide results further comparing our method to [5] using a different retrieval loss than triplet (i.e. angular loss) for direct comparison with that method. We show that our method continues to outperform [5] under its strongest configuration, even without finetuining our hyperparameters in any way. We also compare our method against this method using top-3 (rather than just top-1) and demonstrate that our conclusions do not change (i.e. our method continues to outperform significantly). Next, we show an additional way of comparing our method to the strongest baseline using mean rank of the correct sample as judged by our method. We show that the trends among the methods remain and that our method continues to outperform all other methods. We then provide a visualization of the distribution of |αX −αY | (as computed by our method) across the different datasets tested, showing that our different weighting measures emphasize different aspects of each dataset. We finally provide a discussion of the motivation of our measures and how to properly evaluate cross-modal retrieval for abstract samples.
Performance Comparison of Speech Enhancement Algorithms Using Different Parameters

— In speech communication system, background noise degrades the information or speech signal. For minimizing the effect of background noise different speech enhancement techniques are used. Some speech enhancement techniques are Spectral Subtraction, Wiener Filtering, Two Step Decision Directed Approach, Perceptual Decision Directed Approach. This paper includes the study of these algorithms, and compare the performance using different parameters i.e., signal to noise ratio(SNR), Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), Normalized Root Mean Square Error(NRMSE). From the results we conclude that the performance of PDD approach is better than other described algorithms.
Supplementary Material for “Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles”

001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 086 087 088 089 090 091 092 093 094 095 096 097 098 099 100 101 102 103 104 105 106 107 CVPR #8531 CVPR #8531 CVPR 2023 Submission #8531. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.