Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials

There is a pressing need to exploit recent advances in natural language processing technologies, in particular language models and deep learning approaches, to enable improved retrieval, classification and ultimately access to information contained in multiple, heterogeneous types of documents. This is particularly true for the field of biomedicine and clinical research, where medical experts and scientists need to carry out complex search queries against a variety of document collections, including literature, patents, clinical trials or other kind of content like EHRs. Indexing documents with structured controlled vocabularies used for semantic search engines and query expansion purposes is a critical task for enabling sophisticated user queries and even cross-language retrieval. Due to the complexity of the medical domain and the use of very large hierarchical indexing terminologies, implementing efficient automatic systems to aid manual indexing is extremely difficult. This paper provides a summary of the MESINESP task results on medical semantic indexing in Spanish (BioASQ/ CLEF 2021 Challenge). MESINESP was carried out in direct collaboration with literature content databases and medical indexing experts using the DeCS vocabulary, a similar resource as MeSH terms. Seven participating teams used advanced technologies including extreme multilabel classification and deep language models to solve this challenge which can be viewed as a multi-label classification problem. MESINESP resources, we have released a Gold Standard collection of 243,000 documents with a total of 2179
Quality-aware Argument Retrieval with Topical Clustering

We present a specialized approach to argument retrieval which combines both the general argumen-tative quality of texts, as well as the latent semantic (topic-)space of the document collection as boost factors to a general-purpose retrieval model to address the specific domain requirements of argument search. This setup aims to satisfy our three hypothesized aspects of an argumentative information need: quality-aware result ranking, near-complete topical coverage, and text proximity to the query.
ImageSem Group at ImageCLEFmed Caption 2021 Task: Exploring the Clinical Significance of the Textual Descriptions Derived from Medical Images

This paper presents the work of ImageSem group in the ImageCLEFmed Caption 2021 task. In the concept detection subtask, we employed the transfer learning-based multi-label classification model as our baseline. We also trained multiple fine-grained MLC models based on manually annotated semantic categories, such as Imaging Type, Anatomic Structure, and Findings, which may reveal clinical insights of radiology images. We submitted 9 runs to the concept detection subtask, and achieved the F1 Score of 0.419, which ranked 3 rd in the leader board. In the caption prediction subtask, our first method simply combines detected concepts according to the sentence patterns. The second method used a dual path CNN model for matching images and captions. We submitted 4 runs to the caption prediction subtask, and achieved the BLEU score of 0.257, which ranked 6 th among the participating teams.
A new approach for authentication of four ginseng herbs and their related products based on the simultaneous quantification of 19 ginseng saponins by UHPLC-TOF/MS coupled with OPLS-DA

White ginseng, red ginseng, notoginseng and American ginseng are commonly called ginseng herbs and are frequently used as traditional Chinese medicine (TCM) and dietary supplements in China. Although their functions are evidently different, the discrimination of the four varieties still remains a challenge, especially for the deeply processed products which have lost their botanical morphological features and share similar chemical profiles. The chemometric approach is now the frequently used pattern recognition method for botanical origin discrimination. In the present study, OPLS-DA was employed to discriminate the four varieties using 19 major ginsenosides as variables, which were accurately measured by using UHPLC-TOF/MS. A clear separation of the four varieties was observed in the OPLS-DA score plot. The constructed model proved to be able to discriminate 10% of adulteration. Furthermore, this model showed high accuracy in authenticating the botanical origin of the deeply processed ginseng products. The results also showed that adulteration could be identified for some ginseng herb products in the market.
Qword at CheckThat! 2021: An Extreme Gradient Boosting Approach for Multiclass Fake News Detection

Fake news basically means fabricating a story without verifiable information, source or quote of the story. CheckThat! 2021 Task-3 has two subtasks, subtask 3a and 3b. We participated in Subtask 3a, which is a multi-class fake news classification problem. The goal was to determine whether the main claim of a news article is true, partially true, false or other. We were provided with a dataset of news articles by the organizers which consists of news articles, their titles and the rating of the article. We took advantage of TF-IDF vectorization and proposed an Extreme Gradient Boosting algorithm for our best classification model. The approaches were very interpretative with a highest classification accuracy of 0.57 and highest f1-macro score of 0.54 on the given dataset. We also tried other classification models and got varying results which are simple Logistic Regression Classifiers, Passive Aggressive Classifiers and Random Forest Classifiers.
Profiling Hate Speech Spreaders on Twitter

Hate speech on social media has led to feelings of concern and distrust among individuals and communities. This has aggravated over time because spreading hate speech is often inconsequential to its authors. Hate speech detection can help organizations identify, monitor and manage hate speech spreaders. This paper describes the systems submitted to hate speech profiling task at PAN-2021 using tweets provided by its organizers. This task aims to identify whether the author of the tweets spreads hate speech. We investigated two popular embeddings; namely TF-IDF and BERT (Bidirectional Encoder Representations from Transformers) for converting tweets into vectors. The systems using TF-IDF features with Support Vector Machine (SVM) obtained the best results of 67% and 81% for both English and Spanish tweets (test datasets), respectively. We also used Convolutional Neural Network (CNN) on features generated using BERTweet (a pre-trained language model for English tweet) and it obtained an accuracy of 66% on English tweets.
Chabbiimen at VQA-Med 2021: Visual Generation of Relevant Natural Language Questions from Radiology Images for Anomaly Detection

The new progress in the domain of artificial intelligence has created recent opportunity in support clinical decision. In exceptional, appropriate solutions for the automated analysis of medical pictures are increasing interest expected to their applications potential in picture research and in the diagnosis assisted. In addition, systems able to understand clinical pictures and answer the questions about its content can assist objective decision making, objective education. In my article, I will describe my technique for produce visual questions on radiology images. I have used augmentation techniques for increasing data and VGG19 for extraction of the feature from a picture and prediction. VQGR is implemented using Tensorflow. My model is quite similar to GRNN [1].
Lijie at ImageCLEFmed Tuberculosis 2021: EfficientNet Simplified Tuberculosis Case Classification

Tuberculosis has been a bacterial infectious disease known to humans for a long time, and different types of tuberculosis cases have different treatment methods. Therefore, rapid and accurate positioning of its classification is essential for the treatment of tuberculosis. This paper describes our experiment in ImageCLEF 2021 Tuberculosis-TBT classification. We first use two versions of automatically extracted masks of the lungs which are provided by the organizer to process all the CT images. Then the image is transformed into a 2-dimensional through data preprocessing that is projection, and data enhancement is used in the training process. Through the modified version of the EfficientNet network pre-trained on Image Net, and the introduction of the ghost module, and fine-tuning the model after migration learning, the one classification task with 5 labels required by this challenge was completed. The final kappa score of our team in ImageCLEF 2021 Tuberculosis-TBT classification is 0.015, and the acc score is 0.380.
Yunnan University at VQA-Med 2021: Pretrained BioBERT for Medical Domain Visual Question Answering

This paper describes the submission of the Yunnan University team in the Visual Question Answering task of the ImageCLEF 2021 VQA medical image challenge. According to the analysis of the dataset, we regard this task as a classification task. Firstly, we use the pre-trained VGG16 model, Global Average Pooling (GAP), and image enhancement technology to process and extract the image features. Secondly, we use BioBERT, which is pre-trained with biomedical text, to extract all the semantic features. BioBERT and BERT have the same model structure, but BioBERT have better performance in extracting medical text features. Thirdly, the semantic features and image features are fused by Multi-modal Factorized High-order (MFH) Pooling. Finally, the fused features are input into a fully connected layer for classification. Our method achieved an accuracy score of 0.362 and a BLEU score of 0.402 and ranked 2nd among all the participating teams in the VQA-Med task at ImageCLEF 2021. Our code is publicly available 1 .
TAM at VQA-Med 2021: A Hybrid Model with Feature Extraction and Fusion for Medical Visual Question Answering

This paper briefly describes our model for the ImageCLEF Medical Visual Question Answering Task 2021 (ImageCLEF VQA-Med task 2021). Our method is based on a universal VQA framework and consists of image feature extraction module, question feature extraction module and feature fusion module. We employ the modified ResNet-34 as the backbone to construct an image feature extractor, which effectively extracts pixel-level features and enhances the model performance in a deep network. For question feature extraction, we firstly use word embedding to map question tokens to high dimension vectors, and then input them to a long-short-term memory (LSTM) to extract high-level question features. In addition, we leverage Multi-modal Factorized Bilinear Pooling (MFB) with a co-Attention mechanism to fuse these features to predict final answers. Our model achieves the accuracy score of 0.222 and bleu score of 0.255, ranking at the eighth among all participating teams in the VQA-Med task.
Learning Deformable Network for 3D Object Detection on Point Clouds

3D object detection based on point cloud data in the unmanned driving scene has always been a research hotspot in unmanned driving sensing technology. With the development and maturity of deep neural networks technology, the method of using neural network to detect three-dimensional object target begins to show great advantages. The experimental results show that the mismatch between anchor and training samples would affect the detection accuracy, but it has not been well solved. The contributions of this paper are as follows. For the first time, deformable convolution is introduced into the point cloud object detection network, which enhances the adaptability of the network to vehicles with different directions and shapes. Secondly, a new generation method of anchor in RPN is proposed, which can effectively prevent the mismatching between the anchor and ground truth and remove the angle classification loss in the loss function. Compared with the state-of-the-art method, the AP and AOS of the detection results are improved.
Dual Neural Network Classification Based on BERT Feature Extraction for Authorship Verification

Authorship verification is the task of deciding whether two texts have been written by the same author. We regard authorship verification as a classification task. A dual neural network is proposed to classify the features of text extraction. Especially, BERT is exploited as the encoder to extract the text features. After training and forecasting on the given pan20-authorship-verification-training-small data set, the weighted average of the specified evaluation indexes (including: AUC, F1, c@1, f0.5u, Brier) can reach about 0.85.
EFFECT OF GAMMA IRRADIATION AND SOME HOLDING SOLUTIONS ON VASE LIFE OF GERBERA CUT FLOWERS

Prof. Dr. A.Z. Sarhan, Cairo Univ. ABSTRACT: Nowadays, the most of investigations are directed towards the using of natural materials for reducing pollution environment. So, this study was consummated at Post-Harvest Lab. of Floriculture Dept., Hort. Res. Inst. , Giza, Egypt during 2012 and 2013 seasons to study the effect of some holding solutions, viz. distilled water (control), calcium chloride (1 & 3 g/l), calcium claw (1 & 3 g/l), egg shell (0.5 & 1.5 g/l), oysters (0.5 & 1.5 g/l), humic acid (50 & 150 g/l) and three doses of gamma irradiation (100, 200 & 400 Gy) on longevity of flower heads (vase life), water uptake, water loss, relative fresh weight, dry weight and flower diameter of Gerbera jamesonii, cv. Rosalin & Express cut flowers. The obtained results cleared that application of CaCl2 (1 or 3 g/l) with HQ 0.2 g/l and 20 g/l sucrose resulted in a great extension in vase life and water uptake more than other treatments. Rosalin cultivar recorded significant increase in longevity, water uptake comparing to gerbera cut flowers of cv. Express during the two seasons. Gamma ray (200 Gy) recorded the least rate of water loss in cv. Rosalin in the two seasons. Superiority was for the treatment with oysters (1.5 g/l) in improvement of relative fresh weight in both seasons followed by gamma ray (200 Gy), also gamma irradiation at the doses 200 Gy plus CaCl2 at 1 g/l + sucrose at 20 g/l significantly increased dry weight of the two cultivars Rosalin and Express followed by claw (1 g/l). All treatments gave higher record in comparison with control for diameter. The present research indicated that the application of CaCl2 (1 g/l) with HQ 0.2 g/l and 20 g/l sucrose after exposure of cut flowers to irradiation with gamma at (200 Gy) maximizes flower diameter. Results showed that, treating with CaCl2 at 3 g/l + HQ at 0.2 g/l and suc. at 20 g/l, or gamma rays at 200 Gy + CaCl2 at 1 g/l + HQ at 2 g/l + Sucrose at 20 g/l registered the highest increments in chlorophyll a in the stems and soluble sugars in the stems and petals. It is concluded that the use of the holding solution containing calcium chloride at 3 g/l plus hydroxyqenolin plus sucrose alone or after irradiation with gamma at 200 Gy gave the maximum vase life.
Mapping the Static Parts of Dynamic Scenes from 3D LiDAR Point Clouds Exploiting Ground Segmentation

Dynamic objects are an inherent part of our world, but their presence deteriorates the performance of various localization, navigation, and SLAM algorithms. This not only makes it important but necessary to remove these dynamic points from the map before they can be used for other tasks. In this paper, we address the problem of building maps of the static aspects of the world by detecting and removing dynamic points from the source point clouds. We target a map cleaning approach that removes the dynamic points and maintains a high quality of the generated static map. To this end, we propose a novel ground segmentation method and integrate it into the OctoMap to better distinguish between the moving objects and static road backgrounds. We evaluate our approach using SemanticKITTI for both dynamic object removal and ground segmentation algorithms. The evaluation results show that our method outperforms the baseline methods in both tasks and achieves good performance in generating clean maps.
PSFC/JA-20-86 Dynamic Phase Alignment in Inertial Alfven Turbulence

Work supported by DOE Award No. DE-FG0291ER54109 (L. M. M. and N. F. L.), NSF CAREER Grant No. 1654168 and NSF CAREER Grant No. 2010136 (N. F. L.), the Prof. Amar G. Bose Research Fellows Program at MIT (L. M. M. and N. F. L.), and NSF Grant No. PHY1707272, NASA Grant No. 80NSSC18K0646, and DOE Award No. DESC0018266 (S. B.). Reproduction, translation, publication, use and disposal, in whole or in part, by or for the United States government is permitted.
Optical frequency combs generated by four-wave mixing in a dual wavelength Brillouin laser cavity

We propose and demonstrate the generation of optical frequency combs via four-wave mixing in a dual wavelength Brillouin laser cavity. When pumped by two continuous-wave lasers with a varied frequency separation, dual wavelength Brillouin lasers with reduced linewidth and improved optical signal to noise ratios are generated in a direction opposite to the pump laser. Simultaneously, cavity-enhanced cascaded four-wave mixing between dual wavelength Brillouin lasers occurs in the laser cavity, causing the generation of broadband optical frequency combs with step tunable mode spacing from 40 to 1300 GHz. Compared to the cavity-less case, the number of the comb lines generated in the dual wavelength Brillouin laser cavity is increased by ∼38 times.
Fluorescent Molybdenum Oxide Quantum Dots and HgII Synergistically Accelerate Cobalt Porphyrin Formation: A New Strategy for Trace HgII Analysis

The construction of an efficient and sensitive platform for HgII analysis is of great importance because of the potential hazards of HgII to human safety and environmental stability. Herein, we propose a new strategy for HgII sensing based on the synergistic effect of fluorescent MoO3–x quantum dots (QDs) and HgII on cobalt porphyrin formation, in which the accelerated reaction rate far exceeds the simple sum of the contributions from the individuals. A first-principles theoretical study reveals the synergistic mechanism of cobalt(II) porphyrin generation, with mercury(II) porphyrin and CoII being docked together on the MoO3–x QD platform. The synergistic reaction results in a distinct and rapid evolution of the optical properties of porphyrins and MoO3–x QDs, facilitating kinetic HgII sensing and intracellular HgII imaging. Compared with the traditional “substitution reaction” method in the presence of an individual accelerator, the present strategy can markedly shorten the analysis time, largely improve.
IMPLEMENTATION OF DISTRIBUTED AVERAGE CONSENSUS METHOD ON WIRELESS SENSOR NETWORK FOR MONITORING TEMPERATURE AND HUMIDITY IN SWITCHGEAR ROOM

The development of wireless sensor networks is currently very rapid in various sectors. One of the implementations of the wireless sensor network is for monitoring temperature and humidity. In monitoring a room or a large area, many nodes are installed at a certain point. With the number of senses by each node so that one data that can represent the area is needed. To get one data that can represent the data in that area, we need a consensus method. In this study, the average distributed consensus method was used to obtain data representation from an area's wireless sensor network. After testing and simulating the ideal range between WSN nodes is 30 meters where the average percentage of detection errors reaches 0%. And the maximum capability is 33 meters with an average percentage of error detection of 46%. If given a barrier between WSN nodes, the distance that can be reached becomes shorter, with an ideal distance of 16 meters with an average percentage of error detection of 0% and the farthest distance reaching 21 meters with a detection error of 50%. The results of the consensus method simulation show that the more slots are filled with consensus data about the results, the faster and easier convergence will be achieved, in this case the monitoring results represent the real situation.
Discrete Continuum Robotic Structures

When overcoming environmental constraints, nature shows the capacity to generate hybrid hard-soft morphing continuum structures at very low cost at almost any scale. Human attempts to replicate nature-like systems to overcome modern engineered solutions, based on classical rigid mechanics, commonly lead to hyper-redundant and complicated designs. Novel trends like soft robotics or continuum robotics are showing new successful directions but mostly at small sizes. It is still a challenge to achieve accessible and cost-efficient scalable nature-like solutions. The earliest research towards digital materials focused on proving reversibility of their assembly, their low relative densities vs. ultra-high stiffness ratios and scalability properties. Now we can find architected metamaterials with many kinds of exotic physical properties. This thesis will focus on digital materials with custom mechanical properties. Recent work showed the capacity to generate controlled mechanical anisotropies as embedded compliancy, chirality, and auxeticity. That enables generating continuum macroscopic foams with controlled deformation that could preserve some properties and help bring simplicity to overcome tasks that, with classic rigid-joint mechanical systems, would require a very complex system. Equally important, many of the modern engineering solutions that would require digital materials are very dependent on their outer shape. Literature shows less acclaim for providing an accurate shape to these digital materials. Some of the strategies proposed have been based on hierarchical strategies or reducing the overall size of the building blocks but these findings conflict with the many of the claimed premises. This thesis is proposing a folded solution that will integrate onto the continuum structure and provide a desired shape that is structurally efficient while respecting its intrinsic degrees of freedom. As a whole, this thesis explores if heterogeneous digital materials can provide all the mechanical needs of a movable structure integrated. This thesis tries to mimic nature’s engineering strategies by joining the kinematical and shape-form needs into a single material system composed of a discrete building block core and a folded outermold-line layer. As examples, this thesis recreates a water snake and a morphing wing inspired by birds camber morphing. 3 Thesis Supervisor: Neil Gershenfeld Title: MIT Professor and Director of the Center for Bits and Atoms
Nordic Journal of Comparative and International Education (NJCIE)

Few studies highlight the content of early childhood teacher education (ECTE) and examine the quality of knowledge acquired by future early childhood teachers. The current study concerns two questions. The first explores the goals of the national curricula in Danish and Norwegian ECTE concerning children’s language learning and early literacy. The second explores how satisfied Danish and Norwegian student teachers are with their own subjective learning outcomes related to the same themes. To answer the first question, data were collected through document analysis of the two countries’ national curricula. To answer the second question, data were collected through a survey handed out to student teachers in both countries at the end of their education. The survey contained questions about factual, procedural, and meta-knowledge areas. The survey participants comprised 199 Norwegian student teachers from three University Colleges and 90 Danish student teachers from three separate campuses at one University College. This study reveals that the Norwegian student teachers evaluated their subjective learning outcomes in the fields of language learning and early literacy more highly than Danish student teachers. Our research points to the fact that, compared to the Norwegian ECTE, the breadth of subjects in the Danish ECTE bachelor program tends to give lower subjective learning outcomes in these knowledge areas.