Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Activity of Brown Seaweed ( Padina pavonica (L.) Thivy, 1960) Extracts from the Algerian Mediterranean Coast

The crude methanolic extract, ethyl acetate and butanolic fractions, tannins, saponins and polysaccharides of the marine brown alga, Padina pavonica, growing on the Algerian Mediterranean Coast were measured for antioxidant activity, using total antioxidant capacity (TAC), reducing power and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays. Total phenolic, flavonoid and proanthocyanidin contents were determined. The total phenolic, proanthocyanidin and flavonoid contents of methanolic extract were about of 2.007 ± 0.104 mg GAE/g DM, 4.611 ± 0.346 mg CE/g DM and 1.132 ± 0.091 mg CE/g DM, respectively. The total antioxidant capacity was higher (4.365 ± 0.452 mg AAE/g DM) in crude methanolic extract. For the other two tests, the tannins and ethyl acetate fraction presented high activities compared to the other extracts with EC 50 about 1.430 ± 0.000 and 1.615 ± 0.009 mg/mL), respectively for the reducing power and 5.718 ± 1.111 and 5.848 ± 0.186 mg/mL for the DPPH radical scavenging. The seaweed extracts displayed moderate antioxidant activity compared to ascorbic acid.
Improving Security and Reliability in Merkle Tree-Based Online Data Authentication with Leakage Resilience

With the successful proliferation of data outsourcing services, security and privacy issues have drawn significant attention. Data authentication in particular plays an essential role in the storage of outsourced digital content and keeping it safe from modifications by inside or outside adversaries. In this paper, we focus on online data authentication using a Merkle (hash) tree to guarantee data integrity. By conducting in-depth diagnostics of the side channels of the Merkle tree-based approach, we explore novel solutions to improve the security and reliability of the maintenance of outsourced data. Based on a thorough review of previous solutions, we present a new method of inserting auxiliary random sources into the integrity verification proof on the prover side. This prevents the exposure of partial information within the tree structure and consequently releases restrictions on the number of verification execution, while maintaining desirable security and reliability of authentication for the long run. Based on a rigorous proof, we show that the proposed scheme maintains consistent reliability without being affected by continuous information leakage caused by repetitions of the authentication process. In addition, experimental results comparing with the proposed scheme with other state-of-the-art studies demonstrate its efficiency and practicality.
On the Commons and Climate Change: Collective Action and GHG Mitigation 1

Reducing greenhouse-gas (GHG) emissions from anthropogenic activity may be one of the greatest collective-action problems faced by humanity. This poses challenges not only in terms of the institutional configurations to support coordinated governance processes, but equally the information tools and expertise necessary to link GHG mitigation with other policy priorities. This paper theoretically explores how the adoption of a modified theory of collective action based upon a behavioral theory of the individual allows for a reframing of the climate-change policy challenge. As such, it appears important to develop a context within which collective action becomes possible where success is no longer solely tied to incentives, but equally to the provision of information, learning, and interaction between stakeholders while simultaneously fostering trust and reciprocity among actors. At all levels of government, information plays a key role to both inform and to facilitate communication, as well as to identify and develop the necessary actions and investments and to track changes in conditions. In the case of climate change, greenhouse-gas inventories and other informational tools are necessary components to track an a priori intangible emission. As such, it is key to analyze the legitimacy, credibility and saliency of information and expertise integrated into the decision-making process. Further, it is important to recognize that the construction of indicators and other information tools is not apolitical , but rather the product of a number of assumptions, interests and decisions concerning what is included and what is excluded shaped by the involved actors. no rest entirely on the model of the individual facing a particular type of payoff function. Instead…an explanation of cooperation must be based on individual learning and norm-adoption, as well as the influence of micro-situational and broader contextual variables, in generating variable levels of cooperation. (Poteete
Analysis of Representations of the Aid That Public Psychological Support Points Provide to Adolescent Female Victims of Gender-Based Violence: Reformulation of Policies and Practices

Gender-based violence in adolescence has become a social health problem that is creating great concern and interest worldwide. In this regard, knowledge of the role taken by the professionals responsible for screening, detecting, referring, and caring for adolescent victims is essential to be able to understand the phenomenon and its characteristics in a practical way. In this sense, psychologists specialising in the care of victims of adolescent gender-based violence have complete and in-depth knowledge, not only of the phenomenon itself and the way in which it is presented in society, but also of the features presented by these victims and the aspects that need to be worked on during the intervention process. Given this context, a series of interviews with psychologists specialised in the care of gender-based violence victims were subjected to a qualitative deductive/inductive analysis. These interviews addressed the psychologists’ theoretical-practical knowledge about the adolescent gender-based violence phenomenon. The analysis of the results points to the victims’ irrational ideas regarding abusive relationships, to the form in which Psychological Support Points are organised to help the victims, and to the strengths, weaknesses, and needs of these centres for them to be able to improve their operation and effectiveness in providing comprehensive care for adolescents beyond the psychological consultations themselves.
Classification of Butterfly Species

This project shows the results of identifying butter-fly species with computer vision methods applied on a dataset provided by the association ”Arbeitsgemeinschaft bayerischer Entomologen e.V” about butterfly species existing in the Alpine region. A convolutional neural network with residual layer serves as a baseline method and is compared with a slightly deeper version, a ResNet50 transfer learning model and the Inception-ResNet-v2 approach. Cause of present species imbalances, class weights are applied on the training process. Overall Inception-ResNet-v2 delivered the best result (Accuracy over 80% and Macro F1-score over 76% for unweighted an weighted loss), while ResNet50 performance was close behind this results.
An Entropic Safety Catch Controls 1 Hepatitis C Virus Entry and Antibody 2 Resistance . 3 4

28 E1 and E2 (E1E2), the fusion proteins of Hepatitis C Virus (HCV), are unlike that of any 29 other virus yet described, and the detailed molecular mechanisms of HCV entry/fusion 30 remain unknown. Hypervariable region-1 (HVR-1) of E2 is a putative intrinsically disordered 31 protein tail. Here, we demonstrate that HVR-1 has an autoinhibitory function that suppresses 32 the activity of E1E2 on free virions; this is dependent on its conformational entropy. Thus, 33 HVR-1 is akin to a safety catch that prevents premature triggering of E1E2 activity. Crucially, 34 this mechanism is turned off by host receptor interactions at the cell surface to allow entry. 35 Mutations that reduce conformational entropy in HVR-1, or genetic deletion of HVR-1, turn 36 off the safety catch to generate hyper-reactive HCV that exhibits enhanced virus entry but is 37 thermally unstable and acutely sensitive to neutralising antibodies. Therefore, the HVR-1 38 safety catch controls the efficiency of virus entry and maintains resistance to neutralising 39 antibodies. This discovery provides an explanation for the ability of HCV to persist in the face 40 of continual immune assault and represents a novel regulatory mechanism that is likely to be 41 found in other viral fusion machinery. 42 43 44
DETR with Modulated Object Queries For Object Detection

Object detection has been one of the fundamental problems in computer vision. It has numerous applications including automatic analysis of CCTV footage, automated retail checkout and vehicle detection just to name a few. Detection with Transformers (DETR) has been a groundbreaking contribution in this space. Though DETR achieves similar results to R-CNN, it suffers from the problem of often strug-gling to detect smaller objects (and compensates by better detection of larger objects). In this paper, we discuss approaches to help DETR learn from itself and use its strengths in detecting larger objects to help it in detecting other objects by using 2 passes through the decoder. In order to aid in this self-learning process, we explore GNNs and other transformation techniques.
Explaining the Effect of Data Augmentation on Image Classification Tasks

Explainability remains a substantial challenge in deep learning research. Data augmentation is a proven tool to improve image classification results, yet very little research has been conducted on its explainability. We applied five data augmentation techniques on 100 MNIST examples and used them to train CNNs along with the baseline model. Saliency mapping is retrieved from each model using 100 MNIST testing images as query. Classification evaluation indicates that several augmentation methods improved accuracy compared to the baseline. Pearson’s Correlation Coefficient (CC) and Kullback-Leibler Divergence (KL) were computed to compare information gain/loss of saliency mappings with respect to the baseline model, and they agree on distortion diverging the most from the baseline saliency mappings, but CC and KL no correlation with validation and testing accuracy. Lastly, Shannon entropy is computed for each saliency mapping but no significant variation is found across different models. The conclusion is that data augmentation can result in better models and significantly alter saliency mappings, and that observing saliency mappings qualitative and quantitatively provides intuition on how data augmentation alters models. How-ever, there remains concrete evidence to explore on how saliency mapping can be used to explain and optimize data augmentation. Future research should consider using a similar methodology but with more complex images and radical augmentation methods, as well as including measurements such as RUC and IG.
Ocean Initial State Contributions to Ensemble Spread: 1 Insights from the Energy Exascale Earth System Model version 1 Large Ensemble 2

21 Assessing uncertainty in projections of future climate change requires an understanding of the 22 relative importance of internal climate variability and external forcing. Since internal variability 23 can be quite large on multidecadal timescales, and is likely to itself be impacted by climate 24 change, single-model initial condition large ensembles (SMILEs) run with Earth System Models 25 (ESMs) have recently become popular. However, questions still remain regarding the effect of 26 ensemble initialization methods on the subsequent evolution of the climate system: generally the 27 goal is to maximize the initial spread across SMILE members, in order to sample as much of the 28 potential range of internal variability as possible. Here we present a new 20-member large 29 ensemble with the Energy Exascale Earth System Model version 1 (the E3SMv1-LE), which 30 uses a novel ‘macro’ initialization strategy based on inter-basin contrasts in ocean heat content to 31 maximize the initial spread in coupled atmosphere/ocean states across ensemble members. The 32 E3SMv1-LE simulates tropical climate variability relatively accurately, albeit with a muted 33 warming trend over the earlier part of the 20th century likely resulting from the model’s strong 34 sensitivity to aerosol forcing. We find that the E3SMv1-LE’s initialization method creates a 35 well-sampled selection of initial climate states, comparable to results from other (larger) 36 SMILEs, suggesting that maximizing inter-basin ocean heat contrasts may be an efficient method 37 of generating large ensemble spread with relatively few members. We also investigate the 38 potential for different initialization methods to create systematically different ensemble spread 39 behaviors, by analyzing spread in both surface temperature and upper-ocean heat content across 40 the E3SMv1-LE and other SMILEs. ‘Micro’ initialization, where only atmospheric initial 41 conditions are perturbed (as in, e.g., the Community Earth System Model version 1), yields 42 lower spread in the first ~30 years, but thereafter spread is comparable to macro-initialized 43 ensembles. The E3SMv1-LE exhibits a spread in both quantities on the larger end of the SMILE 44 range, but this result is highly sensitive to the method of normalization: generally, considering 45 ensemble spread relative to that observed in the relevant pre-industrial control is desirable, yet
Deep Learning for Cell Segmentation

Recent advancements in imaging techniques and protocols have led to the production of high-throughput quantitative imaging of two-dimensional cell culture. Despite the vast expanse of opportunities in biological and medical studies enabled by the potential of deep learning-based for cellular image segmentation, various challenges have dampened growth in this area. One critical challenge lies in the limited amount of annotated data available. We further expand upon the LIVECell dataset, a newly released, annotated dataset of various cell lines, and investigate performance of U-Net and Mask R-CNN-based models to render semantic and instance segmentation.
Blockchain for Project and Construction Management; A Systematic and Scoping Literature Review

At this time in history that the world is becoming smaller every day due to globalization; it is important to achieve an acceptable degree of trust and confidence in any field; and blockchain can put the world on this path. Since there is no central authority governing blockchain, its decentralized nature can turn it into an appropriate platform for project management. Implementing blockchain eliminates project management bureaucracies and allows a single, unchangeable, controllable, and encrypted source to be accessible to everybody and extensible throughout the project life cycle. This approach is the key to achieve an integrated information system and is one of the important reasons for blockchain applicability as an ideal platform for project management and construction. This issue that using blockchain technology in project management will bring about capacities and capabilities has not yet been seriously addressed. Changing the management of construction projects by blockchain is just an idea for now. This study aims to identify the capacity and platforms for developing blockchain technology in project management and construction using systematic and scoping literature reviews. Moreover, it provides some suggestions about the development and improvement areas of this technology. The review results showed that in all areas of project management and construction, there are applications relevant to blockchain technology that confirm the gradual understanding of the significance and positive impact of this technology on enhancing project management and construction performance. According to the results, a major part of blockchain studies was focused on “program and project management”, and two fields of human resources and behavioural areas and research in project management and schools of thought with a share of less than 2% of existing studies were neglected.
Critical thinking skills of chemistry education students in team project-based STEM-metacognitive skills learning during the Covid19 pandemic

Critical thinking skills has to be sharpened particularly during the COVID19 pandemic. This circumstance leads to the lack of students passion to apply their thinking skills in doing something necessary. Therefore, it requires a learning improving critical thinking skills. This study aims to describe the critical thinking skills of chemistry education students taking part in the lecture of Team Project Based STEM-metacognitive skills. The method utilized is a Pre-Experimental Design One-Shot Case Study consists of 130 chemistry education students taking part in 3rd and 5th semester. The 3rd semester students attend lectures on the development of chemistry learning programs (P3K) and the 5th semester students attend lectures on the basics of analytical chemistry (DDKA). The instruments utilized in this research are learning scenarios, worksheets, critical thinking test instruments, critical thinking skill assessment rubrics, and project implementation feasibility observation sheets. There are three critical thinking skills very well demonstrated by students in P3K and DDKA subjects, consist of (1) strategies and tactics, (2) providing simple explanations, and (3) providing further explanations. Two other skills, namely concluding and building basic skills shown by students in the good category. The results of this study contribute to chemistry learning in the future, thus efforts are inevitably required to train critical thinking skills for prospective chemistry teachers in order to produce teachers having good thinking skills.
SolarX: Solar Panel Segmentation and Classification

Increased emissions from fossil fuels has expedited climate change creating a pressing need to shift to renewable sources of energy. Solar photovoltaics (PV) is a promising form of renewable energy, but government and corporate stakeholders lack a comprehensive mapping of the current distribution of PV’s. Knowledge of where PV cells are and how many there are is critical information for the purpose of energy generation capacity estimation. We sought to create a model that could segment and detect PV cells from aerial satellite imagery. For detection, we trained a ResNet34 to achieve an AUC-ROC score of . 99. For segmentation we trained a U-Net to achieve an IoU of score of . 789. Both models were trained on a data set of 20,900 224x224 satellite images from the cities of Fresno, Stockton, Oxnard, and Modesto. Both models outpreformed prior benchmarks set by DeepSolar. Through various hyper-parameter tuning and experimentation, we seek to optimize a model for the task of PV segmentation and classification.
Deep Learning of Image Inpainting for Semiconductor Wafer Image Recognition

As AR / VR field advances and moving towards achieving ‘Metaverse’ in mixed reality world, creating the next generation devices – AR glasses, is identified as key for people to access the new world. From semiconductor manufacturing to nano photonics manufacturing which enables the ‘display’ - the optical waveguide being designed and developed on transparent substrates. We are now facing new challenges in transparent wafer auto handling, which requires high throughput, high precision with high stability. Inspired by the recent advancement and blooming in machine learning and AI field, in which lots of new algorithms have been created and put in application and gaining wide popularities quickly, such as context encoder [1], Google magic eraser [2] etc. In this project, I am looking at applying deep learning algorithms of image inpainting to automated wafer handling and image recognition, especially for fiducial marks on patterned wafer and to find a way for solving the new challenges in developing new products. Since all confidential information is under NDA with partners, so no real data can be shared for training. I am exploring a Sim2Real solution as a first step and to better fit into future development plan. The encouraging results in the project will serve as proof of concept of a potential AI feature in our product development roadmap and lead us towards a new ‘Metaverse’ future.
Relationship between Adherence to Treatment and Self-Efficacy in the Elderly with Arthritis

Background: Arthritis is one of the most common diseases among the elderly, which is affected by adherence to treatment and self-efficacy. The purpose of this study was to determine relationship of adherence to treatment and self-efficacy among the elderly with arthritis. Methods: This descriptive–correlational study was conducted in 2019. Based on the convenience sampling method, 200 eligible elderly patients were recruited from three clinics affiliated with Tehran University of Medical Sciences in Tehran, Iran. Data were collected using the Modanloo adherence to treatment questionnaire and the perceived self-efficacy scale. Data were summarized using descriptive statistics (mean, frequency percentage, and standard deviation) and analyzed using inferential statistics (the Pearson correlation coefficient) in SPSS software (version 16). The statistical significance level was set at 0.05. Results: The mean age of patients was 67.97±5.32 years. The mean scores of self-efficacy and adherence to treatment among the elderly with arthritis were 119.8±35.3 and 123.88±23.04, respectively. There was a statistically significant correlation between adherence to treatment and self-efficacy (r=0.648). The strongest positive correlation (r=0.693) was observed in the subscale of adherence to the treatment, and the weakest positive correlation (r=0.228) was observed in the subscale of indecisiveness for applying treatment with pain. Conclusion: Because of the direct relationship between treatment adherence and self-efficacy in the elderly with arthritis, healthcare policymakers and planners should develop and implement programs to increase self-efficacy and treatment adherence among the elderly with arthritis and other chronic diseases.
Palo Alto Streets Vehicle Detection and Tracking

This is the final report for CS231N Deep Learning for Computer Vision, Spring 2022. In this project, we proposed the study of object detection and tracking problem for autonomous vehicles. Specifically, we investigated and compared several YOLO model variants [11] on Udacity Self Driving Car Dataset [13]. Then, we collected and annotated our own driving scene dataset in Palo Alto, and ran YOLOv5 [8] on our own custom dataset. Lastly, we performed transfer learning on our Palo Alto dataset starting with the model weights pre-trained on the COCO dataset [9]. For the Udacity Self Driving Car Dataset, we did not see a significant difference of performance among YOLO model variants. However, for the Palo Alto dataset, we have witnessed a significant improvement on the performance after transfer learning.