Miracle-dz/UniFAR-FTUs huggingface dataset:
KLF14 Inhibits Renal Mesangial Cell Proliferation by Promoting Btg2 Gene Expression

Krüppel-like factor 14 is one of zinc finger protein family that are closely associated with regulation of transcription, and its function in the kidneys remains unclear. So KLF14-deficient mouse models were established using TALEN. The levels of blood cholesterol, HDL-C, LDL-C, and TGs were significantly decreased in KLF14-/- mice. KLF14-/- mice had more obvious mesangial cell proliferation and greater accumulation of extracellular matrix. RNA-Seq showed that these differentially expressed genes were mainly involved in metabolism, proliferation and inflammation pathways. ChIP-Seq was performed to identify KLF14 target genes. Five overlapping KLF14 target genes (Btg2, Socs2, Hdc, Ler2 and Akr1b3) were identified by combined analyses of RNA-Seq and ChIP-SEq. Dual luciferase reporter assay and EMSA confirmed that Btg2 was a target gene of KLF14. EdU assay revealed that KLF14 could inhibit the proliferation of primary renal mesangial cells by promoting Btg2 expression. KLF14 might exert its function in kidneys by regulating metabolism, proliferation, and inflammation signaling pathways. KLF14 inhibited the proliferation of primary RMCs by promoting expression of its target gene Btg2. This study provides a basis for further functional studies of KLF14 in the development of kidney diseases, especially in mesangial proliferative glomerulonephritis, metabolic nephropathy and kidney tumors.
Extracting Relations One Argument at a Time

Our goal is to develop natural language processing technology that can extract information about Mars from planetary science publications to populate a Mars knowledge base for planetary science researchers. Speciﬁcally, our task is to harvest compositional relations pertaining to sites (“targets”) on the surface of Mars and the minerals or elements (“components”) that have been found at these sites. Existing relation extraction methods often struggle to recognize many of the relations in our corpus due to the complex language in scientiﬁc documents. Our research investigates a new approach to relation extraction that trains separate classiﬁers to recognize each argument of the relation and then merges their predictions to reconstruct binary relation instances. Experimental results show that this approach yields substantially higher recall and precision than several other relation extraction systems for our task
A New Dataset for Extracting Root Cause Events from Aviation Accident Reports

An aviation accident is commonly described by a series of events that are causally related. For the accident, finding the root cause event is one of the primary objectives in the aviation accident analysis. The root cause event triggers some events in the middle and finally leads to the consequence in a chain-like way. In this paper, we propose a dataset on an aviation report corpus. For each document, the root cause event, the target event, and the event chains in between are annotated. We collected 626 aviation accident reports contains at least one event chain. On average, each document has 3.81 events and 1.12 chains, and 2.44 hops per event chain. Moreover, two-step baselines have been performed on event causality identification and root cause detection. We show that our approach achieved a 75% accuracy on overall tasks.
Tissue-naive multimodal profiling of circulating tumor DNA in head and neck squamous cell carcinoma

Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada Deparment of Radiation Oncology, University of Toronto, Toronto, ON, Canada Deparment of Otolaryngology – Head and Neck Surgery, University of Toronto, Toronto, ON, Canada Division of Medical Oncology and Hematology, Department of Medicine, University of Toronto, Toronto, ON, Canada Deparment of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada Department of Computer Science, University of Toronto, Toronto, ON, Canada Vector Institute, Toronto, ON, Canada
: Predicting Hit Songs Using Repeated Chorus

Songwriters and producers have long believed writing a successful hook is what makes a song popular. In this work, we examined this myth by creating a data set of choruses from popular artists and applied five supervised Machine Learning techniques to predict the popularity purely based on the audio features extracted from a 15s chorus. Using a Neural Network, we were able to demonstrate the performance of the model that is better than random. Hence, we conclude the myth is plausible. and then used a linear Support Vector Machine (SVM
Contribution of Histochemical Techniques in the Identification of Polysaccharides in Meat Products

Nowadays, the consumption of meat products is marked by significant increase in the world. Because of the economic value of the meat, the likelihood of using products that are not desirable is possible in the meat-based products. The objective of this study is to exploit the histochemical techniques for the detection of certain additives added fraudulently, particularly the sour and starch. For this reason, seven different meat products were randomly selected, during a period of one month, to be studied using a variety of histochemical techniques that have been carried out on sections of the paraffin-processed by coloring PAS-Calleja and LugolCalleja. The technique histochemical PAS-Calleja made his way, with success, to be an original technique, in order to test the phenomenon of strafing bound to a sum fraudulent of the sugars. While the staining Lugol-Calleja, specific to the testing of starches, allowed to differentiate between the two populations of starches present in meat products : added fraudulently, and the one that comes from the spices. Histochemical techniques have shown, for excellence, their capacity for quality assessment of meat products. Especially the determination of the addition fraudulent polysaccharides.
Proposing WPOD-NET combining SVM system for detecting car number plate

Nowadays, there are many smart parking lots using plate detection system to control in/out vehicles. However, the disadvantages of systems are a fixed environment and necessity of manual labor and requirement of checkpoints in entrances. To solve the problems, a novel algorithm for wide-angle detecting car number plate using warped planar object detection (WPOD-NET) and a modified support vector machine (SVM) system is proposed. Comparing to other models, the proposal improves not only the range of detection angle but also the accuracy of detecting in shady conditions. The results show that the accuracy of proposal model is up to 95.1% with 1000 testing images in various scenarios.
Near-Realtime Server Reboot Monitoring and Root Cause Analysis in a Large-Scale System

Large-scale Internet services run on a fleet of distributed servers, and the continuous availability of the hardware is key to the robustness of the services. Unplanned reboots disrupt the services running on the hardware and lower the fleet availability. Server reboots are also important signals that could indicate underlying issues such as memory leaks from the services, catastrophic hardware failures, and network or power disruptions at the datacenters.In this paper, we present an at-scale, near-realtime reboot monitoring framework built with multiple state-of-the-art data infrastructures, as well as machine learning-based anomaly detection and automated root cause analysis across hundreds of server attribute combinations. We observed that 1% of the reboots in our hardware fleet were associated with kernel panics and out-of-memory events, and these reboots exhibit strong locality temporally and across services
From Language Descriptions to Language Facts

The diversity of the world’s 7 000 languages embodies a wealth of information on the communication machinery inside our heads as well as the history of populations. Traditionally language comparison has been done manually by humans reading grammatical descriptions, but the number of languages and books is now far beyond human capacities. In the present paper we report on experiments to transform a multilingual corpus of over 10 000 raw-text grammatical description into a collection of directly comparable “facts” for each language. The outcome database of facts is meant to serve for human investigation and human-designed machine comparisons. Therefore it is important that the collection of facts is human-interpretable and hence pure black-box solutions are not of interest. We report on succesive experiments using Tf-Idf, Tf-Idf-inspired Term Extraction, Semantic Similarity and Multi-Lingual Semantic Similarity inspired techniques to build representations of the world’s languages. Since historical language relationships are broadly known, and, on the whole, language similarity co-varies with historical proximity, the extracted language facts can be evaluated in terms of how well they reflect known historical relations. As expected, accuracy improves with increased sophistication from simple word counts to tf-idf, semantically, and multilingually informed word counts, modestly but substantially advancing the frontier on this novel task.
Unmasking conflict in vertical coopetition

Purpose – The purpose of this study is to increase understanding about vertical coopetition from the perspective of interrelated conflict episodes on multiple levels. Design/methodology/approach – The empirical part is based on a qualitative single case study of a coopetitive buyer-supplier relationship in the manufacturing sector. Findings – Conflicts in vertical coopetition evolve from being merely functional and task-related to becoming dysfunctional and relationship-related, as the level of competition increases. The nature of conflict episodes influences the development of vertical coopetition, and therefore, the interrelatedness of conflict episodes is important to acknowledge. Practical implications – Although a conflict is considered functional within a company, it may still be dysfunctional as far as the coopetitive relationship with the buyer or seller is concerned. Competition may trigger conflicts related to protecting own technology and knowledge, which may lead to termination of the cooperation, therefore coopetition should be managed in a way that balance sharing and protecting important knowledge to get advantages of coopetition. Originality/value – The findings enhance prior research on vertical coopetition by offering new perspectives on causes of conflicts, their management, outcomes and types. The value of taking a multilevel approach lies in the ability to show how conflicts occur and influence other conflicts through the interrelatedness of conflict elements on different levels.
Real-Time Outdoor Illumination Estimation for Camera Tracking in Indoor Environments

Dynamic illumination is a challenging problem for visual robot localization and tracking. In indoor environments, the main source of light during the day is outdoor illumination. We propose a method that estimates the appearance of an indoor scene in real-time based on a reflectance map and the current outdoor lighting. Our outdoor illumination model consists of three components, namely sun, sky and ground, where the sun position is obtained from the scene geolocation and the current time of day. The scene illumination is pre-computed using radiosity transport for each of those components. To deal with dynamic illumination resulting from changing weather conditions, we estimate the outdoor light brightness in each input frame and scale the pre-computed illumination accordingly. We evaluate our approach on real-world data covering diverse outdoor illumination settings and show that our adaptable model is beneficial for direct camera tracking.
Exploring the Students’ Behavior Intentions to Adopt E-Learning Technology: A Survey Study Based on COVID-19 Crisis

E-learning technology adoption has become, during the COVID-19 pandemic, an essential requirement and a new trend to manage the academic activities in the universities which follow traditional education (Face-to-Face Classroom). Because of the significant effect of coronavirus pandemic on the educational Process, this study's primary purpose is to gain an insight into the factors affecting the students' acceptance and their intentions to adopt an e-learning system in the faculty of business at Tafila university in Jordan. A total number of 257 students with different college disciplinary took part in the study by engaging via a survey distributed as a link by WhatsApp, E-mail, and Thuraya Platform. The study formulated a model based on a UTAUT of (Venkatesh et al., 2003) and suggested a new two moderating factors (study qualification level and scientific disciplinary) after drop two variables from the original model to make the model applicable in the context of a study. The data collection analyzed by smart PLS revealed that the research model constructs have positive explanatory power of the student's behavioral intention to adopt an e-learning system and improved after the moderating effect. Final result Sof this study states that the scientific discipline is a critical moderating factor that moderated the behavioral intentions to adopt e-learning technology from Tafila university students' viewpoint and accepted the new situation of academic activities with behavioral intentions to adopt e-learning.
Hierarchical Neural Dynamic Policies

We tackle the problem of generalization to unseen configurations for dynamic tasks in the real world while learning from high-dimensional image input. The family of nonlinear dynamical system-based methods have successfully demonstrated dynamic robot behaviors but have difficulty in generalizing to unseen configurations as well as learning from image inputs. Recent works approach this issue by using deep network policies and reparameterize actions to embed the structure of dynamical systems but still struggle in domains with diverse configurations of image goals, and hence, find it difficult to generalize. In this paper, we address this dichotomy by leveraging embedding the structure of dynamical systems in a hierarchical deep policy learning framework, called Hierarchical Neural Dynamical Policies (H-NDPs). Instead of fitting deep dynamical systems to diverse data directly, H-NDPs form a curriculum by learning local dynamical system-based policies on small regions in state-space and then distill them into a global dynamical system-based policy that operates only from high-dimensional images. H-NDPs additionally provide smooth trajectories, a strong safety benefit in the real world. We perform extensive experiments on dynamic tasks both in the real world (digit writing, scooping, and pouring) and simulation (catching, throwing, picking). We show that H-NDPs are easily integrated with both imitation as well as reinforcement learning setups and achieve state-of-the-art results. Video results are at https://shikharbahl.github. io/hierarchical-ndps/
Extended Graph Convolutional Networks for 3D Object Classification in Point Clouds

Point clouds are a popular way to represent 3D data. Due to the sparsity and irregularity of the point cloud data, learning features directly from point clouds become complex and thus huge importance to methods that directly consume points. This paper focuses on interpreting the point cloud inputs using the graph convolutional networks (GCN). Further, we extend this model to detect the objects found in the autonomous driving datasets and the miscellaneous objects found in the non-autonomous driving datasets. We proposed to reduce the runtime of a GCN by allowing the GCN to stochastically sample fewer input points from point clouds to infer their larger structure while preserving its accuracy. Our proposed model offer improved accuracy while drastically decreasing graph building and prediction runtime. Keywords—Object classification; graph convolution networks; non-autonomous driving
Identification of the human emotional states based on a sequence of images of his face

Emotion expression is an important aspect of human to human communication. Recognizing the emotional state of a person can help us better understand complex rethorical devices such as irony, understand the gravity of described situation and infer other information that is often not expressed as part of the verbal communication channel. With the growing popularity of integrated human-machine interfaces automatic emotion detection has a great potential to improve the way we interact with machines. Since camera sensors are being integrated into almost all devices, emotion recognition based on facial expression is one of the viable methods for widespread use. Several models performing emotion recognition based on sequence of frontal facial images were proposed and implemented in this thesis. Because emotion is a dynamic psychical state, three different types of temporal context information for recognition were examined and compared. To ensure usability with real-time streams a wrapper framework consuming one frame at the time is proposed. Both deep-learning based and conventional types of classifiers were implemented. The best performing model achieved accuracy of 95.1% on the CK+ dataset.
Pars-ABSA: An Aspect-based Sentiment Analysis Benchmark on Farsi

Due to the increased availability of online reviews, sentiment analysis had been witnessed a thriving interest from the researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e., aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, which is veriﬁed by 3 native Farsi speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some aspect-based sentiment analysis methods with a focus on deep learning, on Pars-ABSA.
Prototypical Networks for Domain Adaptation in Acoustic Scene Classification

Acoustic Scene Classification (ASC) refers to the task of assigning a semantic label to an audio stream that characterizes the environment in which it was recorded. In recent times, Deep Neural Networks (DNNs) have emerged as the model of choice for ASC. However, in real world scenarios, domain adaptation remains a persistent problem for ASC models. In the search for an optimal solution to the said problem, we explore a metric learning approach called prototypical networks using the TUT Urban Acoustic Scenes dataset, which consists of 10 different acoustic scenes recorded across 10 cities. In order to replicate the domain adaptation scenario, we divide the dataset into source domain data consisting of data samples from eight randomly selected cities and target domain data consisting of data from the remaining two cities. We evaluate the performance of the network against a selected baseline network under various experimental scenarios and based on the results we conclude that metric learning is a promising approach towards addressing the domain adaptation problem in ASC.
Match of the day: optimized experimental design in alternate-haul gear trials

European Union (EU) fishers need a range of gear options to comply with requirements under the landing obligation. Alternative fishing gears may be implemented provided equivalent selectivity can be demonstrated. Catch comparison is a valid method of testing the size selectivity of two or more fishing gears and simultaneous gear deployment helps minimize between-haul spatiotemporal variability in abundance. Non-simultaneous or alternate-haul deployments are generally required for single-rig trawls or seines nets. In those gears, matching consecutive test and control hauls helps minimize such variability. Random-haul matching strategies have also been employed where consecutive deployments are not logistically possible. Here, we investigated the effects of different matching methodologies by simulating a range of stylized scenarios of between-haul variation in abundance. We resampled data from a multi-rig catch comparison trial and emulated consecutive or randomly matched hauls. We examined how haul matching methodology influences catch curve estimates and uncertainty. Aiming for a balance in abundance across consecutively matched hauls is optimal, while random-haul matching may be the best strategy if neither balanced abundance nor consecutive hauls can be achieved. Based on these outputs, we provide practical guidance for experimental design during planning and at-sea operations to optimize trial outputs.
Cultural Events Classification using Hyper-parameter Optimization of Deep Learning Technique

Through digitization, maintaining and promoting cultural heritage is being strengthened. Concerning this background, this study presents a new Indonesia cultural events dataset and automatic image classification for cultural events. The dataset was developed using the Flickr image platform, and the five cultural events image was collected including the Baliem Festival, Jember Fashion Festival, Nyepi Festival, Pacu Jawi, and Pasola Festival. Further, Convolutional Neural Networks (CNN) was developed for the classification method. A comparison of CNN models (VGG16 and VGG19) using several optimization configurations was performed to get the best model. The results showed that the VGG16 with image augmentation and dropout regularization technique performed best with 94.66% accuracy. This study hoped to support the heritage's digital documentation process and preserve Indonesia's cultural heritage.
Impact of Adopting Agricultural Technologies on Profitability and Production Practices of Tribal Farmers in Bangladesh

Corresponding Author: Jasim Uddin Ahmed Department of Agricultural Economics and Policy, Sylhet Agricultural University, Sylhet, Bangladesh Email: jahmed.sau@gmail.com Abstract: The study identified the factors affecting tribal farmers’ decision adopting agricultural technologies in Bangladesh. A total of 600 respondents were surveyed using multi-stage sampling technique from Sylhet (Garo, Khasia and Manipuri gropus) and Chittagong (Chakma, Marma and Tanchang groups) divisions. The study revealed that the mostly practiced farming systems were C-L-P, C-P-H and C-L-H. In the study areas, over 80 percent of total cropped area were under crop production. On an average, poultry rearing was the highest profitable business in Sylhet division (with BCR 2.08) and livestock rearing was the highest profitable business in Chittagong division (with BCR 2.06). Overall, 39.2, 26.6 and 34.2 and 38.8, 26.5 and 34.7% farmers in Sylhet and Chittagong divisions assured about increased, decreased and constant state of production practices using agricultural technologies, respectively like betel leaf and betel nut production, Cuchia production, Jhum cultivation, medicinal plants cultivation, etc. Educational level of household head, farm size, farm income, extension contact and farming experience had significant influence on farmers’ adoption of agricultural technologies. Government and non-government organizations should arrange and implement training and motivational programs and properly provide extension services for raising the awareness about modern agricultural technologies among the tribal groups.