Miracle-dz/UniFAR-FTUs huggingface dataset:
RevASIDE: Evaluation of Assignments of Suitable Reviewer Sets

Scientiﬁc publishing heavily relies on the assessment of quality of submitted manuscripts by peer reviewers. Assigning a set of matching reviewers to a submission is a highly complex task which can be performed only by domain experts. We introduce and deeply evaluate RevASIDE, a reviewer recommendation system that assigns suitable sets of complementing reviewers from a predeﬁned candidate pool without requiring manually deﬁned reviewer proﬁles. Here, suitability includes not only re-viewers’ expertise, but also their authority in the target domain, their diversity in their areas of expertise and experience, and their interest in the topics of the manuscript. We present three new data sets for the expert search and reviewer set assignment tasks and compare the usefulness of simple text similarity methods to document embeddings for expert search. We analyse the suitability of the approach for diﬀerent sizes of reviewer sets. Furthermore, a quantitative evaluation demonstrates signiﬁcantly better results in reviewer set assignment compared to baselines. A qualitative evaluation also shows their superior perceived quality.
THE TEACHER’S ROLE AS A “MENTOR OF EMOTIONAL RE- SILIENCE”

Current contexts in the educational process have become more and more unstable and numerous and this fact is demonstrated by the recent events occurring in the educational system, in which the traditional face-to-face teaching and learning have moved in the on-line environment. If it comes to foresee the future of education, these changes may lead to positive outcomes in teaching, if the teacher has knowledge and is guided to interact with the pupils, develop new strategies and techniques meant to eradicate pupils’ inappropriate emotional and social behaviours. In order to help diminishing the effects spread by the pandemic upon pupils, the teacher has to adapt her/his teaching style and develop a series of emotional competences that are needed in the teaching activities. The teacher should also demonstrate a high level of openness towards new experiences, a strong social intuition, an extremely well-oriented sensitivity to the context and a great resilience. However, all the above could only be achieved by employing an Emotional Style of its own in the educational process. We believe that the role of mentoring in the current educational context should be reflected upon all the pupils, not only upon the ones capable of high performance. Therefore, the idea of implementing an educative scheme for primary classes as concerns the development and improvement of the emotional resilience of the primary learners is perfectly justified. 2672-815X © 2022 Published by European Publisher.
. Bots y polarización política en el debate sobre el COVID-19

The contexts of social and political polarization are generating new forms of communication that affect the digital public sphere. In these environments, different social and political actors contribute to extreme their positions, using bots to create spaces for social distancing where hate speech and incivility have a place, a phenomenon that worries scientists and experts. The main objective of this research is to analyze the role that these automated agents played in the debate on social networks about the Spanish Government’s management of the global COVID-19 pandemic. For this, “Social Big Data Analysis” techniques were applied: machine learning algorithms to know the positioning of users; bot detection algorithms; “topic modeling” techniques to learn about the topics of the debate on the web, and sentiment analysis. We used a database comprised of Twitter messages published during the confinement, as a result of the Spanish state of alarm. The main conclusion is that the bots could have served to design a political propaganda campaign initiated by traditional actors with the aim of increasing tension in an environment of social emergency. It is argued that, although these agents are not the only actors that increase polarization, they do contribute to deepening the debate on certain key issues, increasing negativity.
L∞ ‐Bootstrap Approach to Non‐Commutative Gauge Theories

A consistent description of gauge theories on coordinate dependent non‐commutative (NC) space‐time is a long‐standing problem with a number of solutions, none of which is free from criticism. In this work, we discuss the approach proposed in Blumenhagen et al. [1] based on the conjecture that any consistent gauge theory can be described in terms of the L∞ ‐structure. Starting with a well‐defined commutative gauge theory, we represent it, together with the non‐commutative deformation, as a part of a bigger L∞ ‐algebra by setting some initial brackets ℓ1, ℓ2, etc. Then, solving the L∞ ‐relations we determine the missing brackets ℓn and close the L∞ ‐algebra defining the NC gauge theory which reproduces in the commutative limit the original one. We provide the recurrence relations for the construction of the pure gauge algebra L∞ gauge , using which we find an explicit form of the NC su(2) ‐like and non‐associative octonionic‐like deformations of the Abelian gauge transformations. The construction of the L∞ full ‐algebra describing the dynamics is discussed using the example of the NC Chern–Simons theory. The obtained equations of motion are non‐Lagrangian, which indicates the difference between our approach and the previous ones.
New Extended Short-trem Load Forecasting Method Consideration of Human Body Amenity

Extended short-term load forecasting was derived from the establishment of rolling generating schedule.When there is a relatively large deviation between the original plan and actual load(larger than 3%),accurate extended short-term forecasting is helpful for planing rolling generating schedule scientifically and properly.A variety of human body amentiy indicators are used to select similar days with the idea of load derivation,and analyzes the effect of weather conditions on electric load.One method of extended short-term forecasting based on manifold human body amenity is proposed.Case study results show that the proposed method can improve the global forecasting accuracy while keeping the calculation speed.
A Text Location Method for Sign Based on Pixel-level Gradient Ray Images and Edge Contour Mapping

Compared with deep learning methods, traditional image processing methods have lower computational costs and have advantages for typical problems such as sign text, this paper proposes a sign text location method based on pixel-level gradient ray map and edge contour mapping. First, based on image pre-processing, the pixel-level gradient ray map is obtained by using the Stroke Width Transform. Then, using the Maximally Stable Extremal Region method to locate the irregular text candidate area in the ray map. At the same time, add a mask layer to filter the irregular candidate area, and then regularize the candidate area and perform Non-Maximum Suppression processing. Finally, the background non-text candidate area is processed by extracting the mapping of the closed edge contour of the image, and the remaining text area positioning candidate frame coordinates are mapped back to the original picture. The experimental results show that the method is simple and clear, the effect is remarkable, and it has good robustness to the sign of different sizes.
An Intelligent Multi-turn Dialogue Classification Method Based on Resampling

Intelligent multi-turn dialogue classification uses the whole text as input to predict tags. As one of the most popular research topics in dialogue system, intelligent multi-turn dialogue classification has important research significance in academia and industry. In this paper, we focus on removing redundant information from dialogue text. Multi-turn dialogue classification is essentially a text classification problem. We selects Bert as the core model of multi-turn dialogue classification in view of its strong learning and redundancy removing ability. However, Bert limits the length of its input to 512 and the length of dialogues often exceeds 512. A long text segmentation method based on resampling is presented to solve the problem of input length limitation. The resampling mechanism can also further remove the redundant information in multi-turn dialogues. Experimental results show that the proposed method outperforms all the baselines and achieves an F1 of 53.91%.
Design and Analysis of a New Cooling Structure for Interior Permanent Magnet Synchronous Motor with Built-in Fan

Aiming at the overheating problem of interior permanent magnet synchronous motor (IPMSM) for compressors due to the limitation of working environment, a new type of IPMSM with built-in fan based on the axial ventilation cooling structure is proposed. Firstly, an axial fan is installed inside the rotor without affecting the electromagnetic performance of motor, and the air velocity distribution and temperature distribution characteristics inside motor are analysed by computational fluid dynamics (CFD). Secondly, the influence of the position of the inlet and outlet on the heat flow distribution is analyzed. Finally, for the problem that the heat dissipation effect of the ventilation slots on casing is not obvious, the ventilation slots are removed and the air outlets are installed on front cover. And the results of heat flow characteristics show that this cooling structure is optimal.
Electrolytic production and dispensing of hydrogen

The fuel cell electric vehicle (FCEV) is undoubtedly the only option that can meet both the California zero emission vehicle (ZEV) standard and the President`s goal of tripling automobile efficiency without sacrificing performance in a standard 5-passenger vehicle. The three major automobile companies are designing and developing FCEVs powered directly by hydrogen under cost-shared contracts with the Department of Energy. Once developed, these vehicles will need a reliable and inexpensive source of hydrogen. Steam reforming of natural gas would produce the least expensive hydrogen, but funding may not be sufficient initially to build both large steam reforming plants and the transportation infrastructure necessary to deliver that hydrogen to geographically scattered FCEV fleets or individual drivers. This analysis evaluates the economic feasibility of using small scale water electrolysis to provide widely dispersed but cost-effective hydrogen for early FCEV demonstrations. We estimate the cost of manufacturing a complete electrolysis system in large quantities, including compression and storage, and show that electrolytic hydrogen could be cost competitive with fully taxed gasoline, using existing residential off-peak electricity rates.
Abnormal Breakdown Process and Mechanism of High-performance Epoxy Insulation

In recent years, various faults caused by insufficient mechanical properties of high-performance epoxy insulating parts occur from time to time. The breakdown position is epoxy metal interface, in which an obvious mechanical failure form is found. It is speculated that the failure mechanism may be the failure process caused by combined electrical mechanical breakdown. An insulation failure of epoxy insulating parts was analysed and he similarities and differences of bending and tensile properties of single column leg of different batches of products was studied, which is used to put forward the bulk insulation failure process of epoxy insulating parts under mechanical electrical coupling. In this paper, the bending test and tensile test of single column leg of high-performance insulation are designed, and the bonding strength of column leg insert is studied and analyzed. It is found that the bonding strength of column leg inserts of high-performance insulating parts in the fault batch is insufficient and has great dispersion. The bonding strength of column leg inserts is mainly affected by the knurling shape, knurling width and the coating shape of semi-conductive adhesive on the surface of the insert.
Research on Multi Charging Path Planning for Network Layout

With the development of Internet of things, Rechargeable Wireless Sensor Network (WRSN) has been widely used. In order to keep the WRSN working, the mobile charger needs to be charged regularly. This paper studies the practical problems of WRSN charging. Aiming at the problem of path and battery capacity planning under complex constraints of multiple chargers, using the traditional MTSP model for reference, the minimum energy consumption path and battery capacity planning model under multiple chargers are established, and the equilibrium factor is creatively designed and applied. Considering the NP-hard characteristics of the problem, an improved genetic algorithm based on Balance degree is designed to solve it. Finally, four cases are tested and compared with the traditional genetic algorithm. The results show that the improved genetic algorithm saves 31.56% of the total distance on average compared with the traditional algorithm, and has better stability and better optimization ability. The results are reasonable and have wide application value.
Examination of the Physics solution of COVID-19

The Viruses are pathogens capable of infecting more than 10% of the world’s population annually in epidemics responsible for 3 to 5 million cases of severe illness and up to 500 000 deaths. In addition, new virus variants pose a continuous threat of sparking pandemic outbreaks. The success of the viral attack is attributed to their sheath in a capsid, abnormal mass production of infective RNA or DNA genetic material in living target. Other features include, great symmetry in structures based on assorted geometries with amazing mechanical properties. Consequently, prognostic models have been deployed to regulate and unravel understanding for successful treatment of viruses. In particular, this report seeks to investigate SARS-CoV-2 through its viral shell mechanical properties and a novel point of entropy and thermodynamic principles. It concludes with recommendation of effective physical remedies for SARS-CoV-2 from a local and global outlook.
A Image Segmentation Method Based on Dual Convolutions

Fundus retinal vascular image has high medical diagnostic value. Its segmentation image can help assess diseases that cause retinopathy, such as cerebral small vessel disease, coronary artery disease, glaucoma, and diabetes. Classical encoder-decoder segmentation methods like Unet cannot extract subtle details as well in the retinal vessel due to its complex structure. In this paper, we propose an improved Unet algorithm based on dual convolutions and integrate MultiRes feature extraction module. The MultiRes module improves the feature learning ability of micro vessels, which can collect further subtle details. The dual convolutions module utilizes dual feature maps with varying scales and channels, exchanges feature information with each other, thus can capture retinal vasculatures with better performance. Experiment based on the DRIVE dataset indicates that the accuracy, sensitivity, specificity, AUC(area under curve), and Dice coefficient of the proposed model are 96.13%, 78.57%, 98.17%, 97.29% and 80.87%. Compared with the existing advanced algorithms, our proposed algorithm shows its effectiveness.
An Automatic Marking Method Based on Object Detection Algorithm in Aerial Images

Aerial images of brassica napus in field were taken by UAV equipment but often contained unnecessary areas, which caused interferences and inconveniences to the subsequent study of identification of flowering stage by brassica napus images. To solve above problems, YOLO-V4 object detection algorithm based on Convolutional Neural Network (CNN) is adopted to build a model to detect brassica napus distribution area in aerial images, and model is trained iteratively by training data sets and setting initial parameters. As a result, the mean average precision (mAP) of the object detection model reached 96.24%, and the average loss is only 0.5843. In addition, the generalization and robustness ability of model have been evaluated by testing data sets. Conclusion shows the model proposed in this paper can indeed achieve an application of object detection and automatic labeling in aerial brassica napus images with a good performance.
Research on Mini-EfficientDet Identification Algorithm Based on Transfer Learning

In this research paper, the Chinese beehive culture in Fujian Province, China is used as the carrier, and the Mini-EfficientDet deep neural network after migration is used to identify common species at the hive door, that is, the identification of Chinese bees, wasps and cockroaches in the form of nymphs. In this paper, we define the modified model as Mini-EfficientDet by compressing the initial EfficientDet model and adding the category imbalance function, which makes it focus more on the recognition and classification of small targets while ensuring the recognition accuracy. Through the test on the MSCOCO2017 data set, it is concluded that when the backbone network adopts EfficientNet B7, it shows strong detection accuracy in detecting targets of various scales, which confirms the role of the category imbalance function proposed in this paper and the efficiency of the improved EfficientDet model. Detection accuracy. The pre-trained model is transferred to the field of beehive species detection through migration learning, that is, after the post-training of the self-collected data set, the detection accuracy of Chinese bee, cockroach, and wasp is 98.66%, 83.71%, and 82.06%. It has made sufficient algorithmic preparations for the later detection and early warning system of beehive species invasion.
Cellular Mechanisms of Action Associated with Transcranial Ultrasound for Modulation and its Acoustic Characterization through Skull

Author(s): Babakhanian, Meghedi | Advisor(s): Grundfest, Warren | Abstract: Recent in vivo modulation of region-specific brain activity suggests that Low Intensity Focused Ultrasound (LIFU) may be a non-invasive alternative therapy for drug-delivery applications and the treatment of neurological diseases, including epilepsy and Parkinson’s disease.Despite these recent successes, failure to reproduce published results continues to plague the field due to the limited understanding of cellular mechanisms that underlie neuromodulation and the impact of skull on targeting accuracy. The objective of this thesis is to help bridge these knowledge gaps and better understand non-invasive transcranial focused ultrasound modulation. While hypotheses exist explaining the mechanism underlying ultrasound modulation, they largely remain untested. It has been suggested that mechanical perturbation of cellular membranes with embedded protein channels using ultrasound has an impact on ion channel kinetics, resulting in depolarization, and ultimately, increased neural activity. In particular, this thesis investigates the hypothesis explaining the mechanical perturbation as a result of pure acoustic radiation forces using simplified in vitro models, including Large-Conductance Mechanosensitive Channels (MscL) and non-mechanically stimulated channels. The outcome revealed an increase in efflux through proteoliposomes regardless of the channel type except at the highest concentration of mechanosensitive channel (MS) model where a lowering efflux trend was noticed. These unexpected results suggest that focused ultrasound does not modulate the gating of ion channels, but instead effects the permeability of the membrane itself or protein-membrane interface. Also a dual effect of membrane stretch enhancement and pore formation is observed only at high MS channel concentration.In addition, to prepare for in vivo efficacy studies, the present dissertation characterizes the ultrasonic beam scatter and focal shifts that occur as ultrasound passes through a rat skull for a specific set of parameters. The results have shown significant beam shape deformation and target shift due to the skull. This suite of studies improved our understanding of the mechanism associated with LIFU-based stimulation at the molecular level, while also exploring how LIFU can be applied with greater accuracy and precision in vivo. In addition, insights gleaned from this approach are expected to promote new avenues of clinical applications for the treatment of drug delivery, gene therapy and neurological illnesses.
Audiovisual interaction with rate-varying signals

A task-irrelevant, amplitude-modulating sound influences perception of a size-modulating visual stimulus. To probe the limits of this audiovisual interaction we vary the second temporal derivative of object size and of sound amplitude. In the study’s first phase subjects see a visual stimulus size-modulating with f ″ ( x ) > 0, 0, or <0, and judge each one’s rate as increasing, constant, or decreasing. Visual stimuli are accompanied by a steady, non-modulated auditory stimulus. The novel combination of multiple stimuli and multi-alternative responses allows subjects’ similarity space to be estimated from the stimulus-response confusion matrix. In the study’s second phase, rate-varying visual stimuli are presented in concert with auditory stimuli whose second derivative also varied. Subjects identified each visual stimuli as one of the three types, while trying to ignore the accompanying sound. Unlike some previous results with f ″ ( x ) fixed at 0, performance benefits relatively little when visual and auditory stimuli share the same directional change in modulation. However, performance does drop when visual and auditory stimului differ in their directions of rate change. Our task’s computational demands may make it particularly vulnerable to the effects of a dynamic task-irrelevant stimulus.