Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods

This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification

This article introduces the system description of the hub team, which explains the related work and experimental results of our team’s participation in SemEval 2021 Task 5: Toxic Spans Detection. The data for this shared task comes from some posts on the Internet. The task goal is to identify the toxic content contained in these text data. We need to find the span of the toxic text in the text data as accurately as possible. In the same post, the toxic text may be one paragraph or multiple paragraphs. Our team uses a classification scheme based on word-level to accomplish this task. The system we used to submit the results is ALBERT+BILSTM+CRF. The result evaluation index of the task submission is the F1 score, and the final score of the prediction result of the test set submitted by our team is 0.6640226029.
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection

Humour detection is an interesting but difficult task in NLP. Because humorous might not be obvious in text, it can be embedded into context, hide behind the literal meaning and require prior knowledge to understand. We explored different shallow and deep methods to create a humour detection classifier for task 7-1a. Models like Logistic Regression, LSTM, MLP, CNN were used, and pre-trained models like DistilBert were introduced to generate accurate vector representation for textual data. We focused on applying multi-scale strategy on modelling, and compared different models. Our best model is the DistilBERT+MultiScale CNN, it used different sizes of CNN kernel to get multiple scales of features, which achieved 93.7% F1-score and 92.1% accuracy on the test set.
YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles

This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research contributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicate-object triples to form a knowledge graph with the help of multiclass classification and multi-label classification. We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results. The average F1-score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F1-score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions

Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS and Laptop-ACOS, for this new task, both of which contain the annotations of not only aspect-category-opinion-sentiment quadruples but also implicit aspects and opinions. The former is an extension of the SemEval Restaurant dataset; the latter is a newly collected and annotated Laptop dataset, twice the size of the SemEval Laptop dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and source code of four systems are publicly released at https://github.com/NUSTM/ACOS.
SkoltechNLP at SemEval-2021 Task 5: Leveraging Sentence-level Pre-training for Toxic Span Detection

This work describes the participation of the Skoltech NLP group team (Sk) in the Toxic Spans Detection task at SemEval-2021. The goal of the task is to identify the most toxic fragments of a given sentence, which is a binary sequence tagging problem. We show that fine-tuning a RoBERTa model for this problem is a strong baseline. This baseline can be further improved by pre-training the RoBERTa model on a large dataset labeled for toxicity at the sentence level. While our solution scored among the top 20% participating models, it is only 2 points below the best result. This suggests the viability of our approach.
S-NLP at SemEval-2021 Task 5: An Analysis of Dual Networks for Sequence Tagging

The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents the second-place method for the task, an ensemble of two approaches. While one approach relies on combining different embedding methods to extract diverse semantic and syntactic representations of words in context; the other utilizes extra data with a slightly customized Self-training, a semi-supervised learning technique, for sequence tagging problems. Both of our architectures take advantage of a strong language model, which was fine-tuned on a toxic classification task. Although experimental evidence indicates higher effectiveness of the first approach than the second one, combining them leads to our best results of 70.77 F1-score on the test dataset.
Multi-scenario urban flood risk assessment by integrating future land use change models and hydrodynamic models

Abstract. Urbanization and climate change are the critical challenges in the 21st century. Flooding by extreme weather events and human activities can lead to catastrophic impacts in fast-urbanizing areas. However, high uncertainty in climate change and future urban growth limit the ability of cities to adapt to flood risk. This study presents a multi-scenario risk assessment method that couples the future land use simulation model (FLUS) and floodplain inundation model (LISFLOOD-FP) to simulate and evaluate the impacts of future urban growth scenarios with flooding under climate change (two representative concentration pathways (RCPs 2.6 and 8.5)). By taking Shanghai coastal city as an example, we then quantify the role of urban planning policies in future urban development to compare urban development under multiple policy scenarios (Business as usual, BU; Growth as planned, GP; Growth as eco-constraints, GE). Geospatial databases related to anthropogenic flood protection facilities, land subsidence, and storm surge are developed and used as inputs to the LISFLOOD-FP model to estimate flood risk under various urbanization and climate change scenarios. The results show that urban growth under the three scenario models manifests significant differences in expansion trajectories, influenced by key factors such as infrastructure development and policy constraints. Comparing the urban inundation results for the RCP2.6 and RCP8.5 scenarios, the urban inundation area under the GE scenario is less than that under the BU scenario, but more than that under the GP scenario. We also find that urban will tend to expand to areas vulnerable to floods under the restriction of ecological environment protection. The increasing flood risk information determined by the coupling model helps to understand the spatial distribution of future flood-prone urban areas and promote the re-formulation of urban planning in high-risk locations.
Comprehensive Review on Ficus Deltoidea Effervescent Mouthwash Formulation in Treating Oral Pathogens

Ficus deltoidea or its well-known local name Mas Cotek belongs to the family of Moracaeae. This native plant is commonly found in Malaysia, other tropical and subtropical countries. Oral periodontal disease is an alarming disease and the utilization of herbal plants in treating oral pathogens has raised attention and concern. Therefore the main objective of this study was to review the effectiveness of effervescent mouthwash formulation of the FDL on treating oral pathogens. Plaque accumulation and oral microorganisms are the main predisposing factors to oral periodontal diseases. Herbal mouthwash has been of particular interest these days to treat oral pathogens. Different effervescent agents such as citric acid, tartaric acid, and sodium bicarbonate were used in formulating effervescent mouthwash by different methods with varying concentrations. FDL leaves have claimed to possess different properties such as antioxidant, anti-diabetic, and anti-inflammatory properties which are helpful in treating many diseases. Post compression parameters such as effervescent time, moisture content, and in vitro antibacterial test were reviewed in this study. FDL has shown a strong correlation to the presence of high content of polyphenols, flavonoids, saponin, tannins, and triterpenoids. In conclusion, the type of preparation choosen is by using wet granulation method and the suitable ratio is 2:1.
WSN Localization Algorithm on Underground Mining Area

The main aim or goal of our research work is to localize the workers working in mining area exactly or with minimum localization error. Network formation in mining area is always very crucial. Laborers working in mining area need strong availability of network as when they go down or deep in a mining area they can be rescued easily. It can only be possible when we know the exact location of the worker working in the particular area. For this, we need better localization scheme. Many recent developments have been made in the field of mining area. Random forest scheme, SVM based regressive localization, Wi-Fi based localization, and these are some schemes developed so far. RSSI and Trilateration works for both indoor and outdoor localization. The difference is only in terms of temperature because indoor temperature is different from outdoor temperature. When we are working on the basis of distance and signal strength then the proposed localization algorithm is suitable for hill area too. From the results of simulation, the new localization algorithm proposed in paper with error checking and correction increases the accuracy of the localization in X direction is 99.98 and in Y direction is 99.97 algorithms based on RSSI and dual prediction.
TOPOGRAPHIC AMPLIFICATION EFFECTS ON SEISMIC MOTIONS: THE CASE OF THE LARGE (M=7.4) 1956 AMORGOS EARTHQUAKE AND ITS IMPACT IN THE AREA OF SANTORINI

We apply a stochastic finite-fault simulation method to model the macroseismic damage of the destructive 1956 M=7.4 Amorgos earthquake, for which the macroseismic intensities for Santorini suggest that local topography has amplified ground motions up to a factor of ~1.5-2.0 along the caldera settlements. For the modelling, strong ground motions measures (PGA and PGV) are converted into macroseismic intensities and compared with the actual macroseismic intensities observed for this event. The main earthquake information (moment magnitude, focal mechanism, etc.) are used to constrain the simulations, which successfully reproduce the damage distribution of the Amorgos event throughout the broader southern Aegean area. Additional detailed simulations are performed for the island of Santorini, to study the topographic amplification phenomena by incorporating frequency-dependent topography amplification factors from the approach of Bouckovalas and Papadimitriou (2005). Vs velocity profiles for the upper 200-400m were estimated from three noise arrays performed in Santorini, revealing average velocities of the order of ~800m/s for this depth range. The simulations employ a detailed land and sea topographic map, allowing to estimate the basic parameters that affect the topographic amplification such as topographic slope, height and wavelength, while generic transfer functions from Klimis et al. (1999) are used to account for additional site-amplification effects. The simulations show an improved agreement between predictions and observations when topographic amplification effects are considered, efficiently explaining the heavy damages of the 1956 Amorgos mainshock for the Santorini caldera area settlements (Oia, Imerovigli, Fira). The generic character of the employed approach suggests that it can be efficiently employed for topographic amplification studies, allowing its easy incorporation in finite-fault stochastic modelling simulations.
Washing Effect of a LiNi0.83Co0.15Al0.02O2 Cathode in Water

The formation of LiOH and Li 2 CO 3 impurities on high Ni content LiNi 0 . 8 3 Co 0 . 1 5 Al 0 . 0 2 O 2 powders due to H 2 O and CO 2 absorption from the air can be reduced without structural degradation by washing in water. Although the as-synthesized sample had a moisture content of 570 ppm immediately after firing, this level increased rapidly to 1270 ppm in air with a relative humidity of 50%. However, its content was decreased to 210 ppm after washing twice in water, followed by heat-treatment at 700°C. It is believed that this improvement was due to the decreased level of Li 2 CO 3 and LiOH impurities on the particles. This was highlighted by the decreasing swelling of the Li-ion cell at 90°C, and the thickness of the cell containing the washed samples was decreased by 50% compared with the bare sample.
Studies on the Constituents of Aconitum Species. III. On the Components of Aconitum subcuneatum NAKAI

A new alkaloid, 14-benzoylneoline, and four known alkaloids, neoline, karakoline, 14-acetyldelcosine, and penduline, were isolated from the roots of Aconitum subcuneatum NAKAI. The structures of 14-benzoylneoline and penduline were confirmed by derivation of these compounds from neoline and aconitine, respectively. A radical-type deoxygenation of a bridgehead hydroxyl group at C-13 was used in the transformation of aconitine into penduline through three steps.
The Fate and Factors Determining Arsenic Mobility of Arsenic in Soil-A Review

Arsenic which is found in several different chemical forms and oxidation states and causes acute and chronic adverse health effects is a toxic trace element widely distributed in soils and aquifers from both geologic and anthropogenic sources. Arsenic which has a mysterious ability to change color, behavior, reactivity, and toxicity has diverse chemical behavior in the natural environment. Arsenic which has stronger ability to readily change oxidation state than nitrogen and phosphorus due to a consequence of the electronic configuration of its valence orbitals with partially filled states capable of both electron donation and acceptance although the electronegativity of arsenic is greater than that of nitrogen and similar to that of phosphorus. Arsenate (V) is the thermodynamically stable form of As under aerobic condition and interacts strongly with solid matrix. However, it has been known that adsorption and oxidation reactions of arsenite (III) which is more soluble and mobile than As(V) in soils are two important factors affecting the fate and transport of arsenic in the environment. That is, the movement of As in soils and aquifers is highly dependent on the adsorption– desorption reactions in the solid phase. This article, however, focuses primarily on understanding the fate and speciation of As in soils and what fate arsenic will have after it is incorporated into soils.
An elliptic Harnack inequality for difference equations with random balanced coefficients

We prove an elliptic Harnack inequality at large scale on the lattice Z, for non-negative solutions of a difference equation with balanced i.i. d. coefficients which are not necessarily elliptic. We also identify the optimal constant in the Harnack inequality. Our proof relies on a quantitative homogenization result of the corresponding invariance principle to Brownian motion, and on percolation estimates. As a corollary of our main theorem we derive an almost optimal Hölder estimate.
Updates in artificial intelligence in gastroenterology endoscopy in 2020

Purpose of review Artificial intelligence is becoming rapidly integrated into modern technology including medicine. Artificial intelligence has a wide range of potential in gastroenterology, particularly with endoscopy, given the required analysis of large datasets of images. The aim of this review is to summarize the advances of artificial intelligence in gastroenterology (GI) endoscopy over the past year. Recent findings Computer-aided detection (CADe) systems during real-time colonoscopy have resulted in increased adenoma detection rate with no significant increase in procedure times. Deep learning techniques have been utilized to accurately assess bowel preparation quality, which would impact surveillance colonoscopy recommendations. For the upper GI tract, CADe systems have been developed to aid in improving the diagnosis of Barrett's neoplasia during real-time endoscopy. Artificial intelligence-assisted real-time endoscopy has been shown to reduce blind spots during EGD. Summary The application of artificial intelligence in gastroenterology endoscopy remains promising. Advances over the past year include improved detection of GI neoplasia during endoscopy and characterization of lesions. Further research including randomized, multicenter trials are needed to further evaluate the use of artificial intelligence for real-time endoscopy.
Effects of six weeks of stable versus unstable multi-dimensional surfaces balance training on passing skills and balance performance in young male basketball players

Background and objective: Adolescence is a period when balance and coordination decrease as a result of rapid musculoskeletal growth. These changes may affect various sports-specific skills, including balance. This study aimed to examine the changes in balance performance and basketball passing speed in male youth basketball players following six weeks of balance training performed on unstable versus stable surfaces. Methods: Twenty-five male basketball players, mean age 15.53 ± 0.57 years, participated in balance training on different surfaces (unstable surface (US, n = 13); stable surface (SS, n = 12)) for six weeks (2 days/week; 35 to 50 min/day, incrementally). Their physical characteristics, passing skills (passing accuracy and speed test) and balance performance (Y-Balance Test and The Closed Kinetic Chain Upper Extremity Stability Test) were measured. Results: The improvement rates for passing accuracy (effect size (ES) = 0.86, moderate, P < 0.001), lower extremity (ES right = 0.94, moderate, P < 0.001; ES left = 0.88, moderate, P < 0.001) and upper extremity (ES right = 1.01, moderate, P < 0.001; ES left = 0.94, moderate, P < 0.001) balance scores, upper extremity stability test power (ES = 0.89, P ≤ 0.001) and the normalized scores (ES = 1.20, moderate, P < 0.001) of the US group were higher than those of the SS group. Passing speed significantly increased only in the US group (P < 0.028). Conclusion: A multi-dimensional surface training model improved balance performance and passing accuracy skills compared to a stable surface training model for male basketball players. US balance training may be used as a complementary training model to increase dynamic balance and passing accuracy skills for male basketball players.
Solar PV Array Fed BLDC Motor Employed With Zeta Converter and Fuzzy Logic

This paper proposes a method that describes the idea of utilizing a solar photovoltaic (SPV) array that is fed with a BLDC motor which essentially makes use of a zeta converter as an intermediate DCDC converter which is exclusively meant for extracting the enormous amount of power preferably from the SPV array. An algorithm preferably termed as the incremental conductance maximum power point tracking (INC-MPPT) algorithm is incorporated for the tracking purposes, this is also meant for controlling the zeta converter in an efficient manner. This controlling strategy is conducted in a soft manner and thus this initialization procedure is called as the soft start for the brushless DC motor (BLDC). The amount of current must be reduced in a preferable way in order to reduce the harmful effects caused by an increased volume of current flow. This reduced amount of current flow is called as the soft start and it is responsible for minimizing the dangerous effects of the current in huge volumes on the windings of the BLDC motor. The efficiency of the entire system can be considerably enhanced by adopting the maximum power point tracking method in suitable ways. A fuzzy logic controller (FLC) which is based on a single-ended primary-inductor converter for the purpose of maximum power point tracking (MPPT) operation of a photovoltaic (PV) system is incorporated in this method. The efficiency of the system is assessed by measuring its ability in terms of its speed and controlling power, the fuzzy logic controller is seen to tremendously improve the efficiency by controlling the above mentioned factors. VLSI comprises of the DC link capacitors that are seen as low valued ones, normal design strategies may not be suitable hence new design techniques with improved features have been proposed in this method. The fuzzy logic controller helps in smooth operation of inverter. This helps in enhancing the manipulation aspect of the BLDC motor. The software incorporated in this method is the MATLAB/Simulink software, the outputs have been obtained by the simulation mechanism that helps is establishing the entire system.