bluuebunny/crossref_metadata_2025 huggingface dataset:
A Pathway-Specific Polygenic Risk Score Is Associated with Tau Pathology and Cognitive Decline
Authors: Sun Yuqing , Wang Meng , Zhao Yuxin , Hu Ke , Liu Yong , Liu Bing ,   Alzheimer’s Disease Neuroimaging Initiative
Published: December 2021

Background:

Tauopathy is a primary neuropathological hallmark of Alzheimer’s disease with a strong relationship to cognitive impairment. In the brain, tau aggregation is associated with the regulation of tau kinases and the binding ability of tau to microtubules.

Objective:

To explore the potential for using specific polygenic risk scores (PRSs), combining the genetic influences involved in tau-protein kinases and the tau-protein binding pathway, as predictors of tau pathology and cognitive decline in non-demented individuals.

Methods:

We computed a pathway-specific PRS using summary statistics from previous large-scale genome-wide association studies of dementia. We examined whether PRS is related to tau uptake in positron emission tomography (PET), tau levels, and the rate of tau level changes in cerebrospinal fluid (CSF). We further assessed whether PRS is associated with memory impairment mediated by CSF tau levels.

Results:

A higher PRS was related to elevated CSF tau levels and tau-PET uptake at baseline, as well as greater rates of change in CSF tau levels. Moreover, PRS was associated with memory impairment, mediated by increased CSF tau levels. The association between PRS and tau pathology was significant when APOE was excluded, even among females. However, the effect of PRS on cognitive decline appeared to be driven by the inclusion of APOE.

Conclusion:

The influence of genetic risk in a specific tau-related biological pathway may make an individual more susceptible to tau pathology, resulting in cognitive dysfunction in an early preclinical phase of the disease.
Effects of Memantine and High Dose Vitamin D on Gait in Male APP/PS1 Alzheimer’s Disease Mice Following Vitamin D Deprivation
Authors: Broberg Dana N. , Wong Dickson , Bellyou Miranda , Montero-Odasso Manuel , Beauchet Olivier , Annweiler Cedric , Bartha Robert
Published: December 2021

Background:

Altered gait is a frequent feature of Alzheimer’s disease (AD), as is vitamin D deficiency. Treatment with memantine and vitamin D can protect cortical axons from exposure to amyloid-β and glutamate toxicity, suggesting this combination may mitigate altered gait in AD.

Objective:

Investigate the effects of vitamin D deprivation and subsequent treatment with memantine and vitamin D enrichment on gait performance in APPswe/PS1dE9 mice.

Methods:

Male APPswe/PS1dE9 mice were split into four groups ( n = 14 each) at 2.5 months of age. A control group was fed a standard diet throughout while the other three groups started a vitamin D-deficient diet at month 6. One group remained on this deficient diet for the rest of the study. At month 9, the other two groups began treatment with either memantine alone or memantine combined with 10 IU/g of vitamin D. Gait was assessed using CatWalk at months 6, 9, 12, and 15.

Results:

Vitamin D deprivation led to a 13% increase in hind stride width by month 15 ( p < 0.001). Examination of the treatment groups at month 15 revealed that mice treated with memantine alone still showed an increase in hind stride width compared to controls ( p < 0.01), while mice treated with memantine and vitamin D did not ( p = 0.21).

Conclusion:

Vitamin D deprivation led to impaired postural control in the APPswe/PS1dE9 model. Treatment with memantine and vitamin D, but not memantine alone, prevented this impairment. Future work should explore the potential for treatments incorporating vitamin D supplementation to improve gait in people with AD.
lncRNA MIR600HG Knockdown Alleviates Cognitive Impairment in Alzheimer’s Disease Through NEDD4L Mediated PINK1 Degradation
Authors: Liu Qingqing , Ling Zaisheng , Zhang Jinpeng , Yu Hongli , Wang Ye , Xue Yang , Wang Chunyan , Zhao Jiwei , Cao Jingwei , Duan Shurong , Zhao Jingkun
Published: December 2021

Background:

Growing evidence has demonstrated that long non-coding RNAs (lncRNAs) play a critical role in Alzheimer’s disease (AD), which is characterized by sustained mitochondrial dysfunction, inevitable memory loss, and cognitive decline. However, the potential function of lncRNAs MIR600 Host Gene (MIR600HG) in AD remains unanswered.

Objective:

Our study aimed to investigate the role of MIR600HG and its related molecular mechanism in AD.

Methods:

The expression of MIR600HG was examined by qRT-PCR. The MIR600HG interacting proteins were identified by RNA pull-down assay and mass spectrometry and verified by RNA immunoprecipitation. Immunofluorescence staining was applied to examine the colocalization of PINK1 and NEDD4L. The PINK1 level and the activation of autophagy were detected by immunoblotting. Morris water maze test was performed to evaluate cognitive decline in AD mice model.

Results:

MIR600HG expression was elevated during aging in two different types of AD transgenic mouse models. Next, we found that increased MIR600HG directly interact with NEDD4L, which promoted PINK1 ubiquitination and degradation, and as well as autophagy activation. Additionally, MIR600HG promoted Aβ production and suppressed Cytochrome C Oxidase activity. Administration of AAV-shMIR600HG restored the Cytochrome C Oxidase activity and inhibited Aβ production. Furthermore, PINK1 overexpression or MIR600HG knockdown significantly ameliorated the cognitive impairment in APP/PS1 mice. PINK1 depletion recovered the spatial memory defect in the AAV-shMIR600HG injected APP/PS1 mice.

Conclusion:

MIR600HG was increased in AD and promoted AD pathogenesis. Targeting MIR600HG significantly improved cognitive function in AD mice, which could pave the way for exciting new avenues in AD therapeutic strategy research.
Difference in Amyloid Load Between Single Memory Domain and Multidomain Subjective Cognitive Decline: A Study from the SILCODE
Authors: Wang Xiaoqi , Bi Qiuhui , Lu Jie , Chan Piu , Hu Xiaochen , Su Li , Jessen Frank , Lin Hua , Han Chunlei , Shu Ni , Liu Hesheng , Han Ying
Published: December 2021

Background:

Subjective cognitive decline (SCD), an at-risk condition of Alzheimer’s disease (AD), can involve various cognitive domains, such as memory, language, planning, and attention.

Objective:

We aim to explore the difference in amyloid load between the single memory domain SCD (sd-SCD) and the multidomain SCD (md-SCD) and assess the relationship of amyloid pathology with quantitative SCD scores and objective cognition.

Methods:

A total of 63 SCD participants from the SILCODE study underwent the clinical evaluation, neuropsychological assessment, and 18F-florbetapir PET scan. Global amyloid standard uptake value ratio (SUVr) was calculated. Additionally, regional amyloid SUVr was quantified in 12 brain regions of interests. A nonparametric rank ANCOVA was used to compare the global and regional amyloid SUVr between the md-SCD ( n = 34) and sd-SCD ( n = 29) groups. A multiple linear regression analysis was conducted to test the relationship of amyloid SUVr with quantitative SCD scores and objective cognition.

Results:

Compared with individuals with sd-SCD, individuals with md-SCD had increased global amyloid SUVr ( F = 5.033, p = 0.029) and regional amyloid SUVr in the left middle temporal gyrus ( F = 12.309, p = 0.001; Bonferroni corrected), after controlling for the effects of age, sex, and education. When pooling all SCD participants together, the increased global amyloid SUVr was related with higher SCD-plus sum scores and lower Auditory Verbal Learning Test-delayed recall scores.

Conclusion:

According to our findings, individuals with md-SCD showed higher amyloid accumulation than individuals with sd-SCD, suggesting that md-SCD may experience a more advanced stage of SCD. Additionally, increased global amyloid load was predictive of a poorer episodic memory function in SCD individuals.
The method of repairing OHE damaged ancient painted murals based on machine vision
Authors: Li Hao
Published: December 2021

Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show: Compared with the OPTICS-based unsupervised method and the machine vision for orchard navigation method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.
Evaluation of urban and rural population flow and spatial planning effect under the background of big data
Authors: Shen Tao
Published: December 2021

In order to improve the effect of urban and rural regional spatial planning and resource allocation, we need to focus on the factor of urban and rural population flow. Therefore, this paper evaluates the effect of urban and rural population mobility and spatial planning under the background of big data. Based on the evaluation of the effect of urban-rural population migration, according to the evaluation principle of the effect of urban-rural population migration after the reform and opening up, the evaluation index system is constructed, the dimensionless processing of the evaluation index system is carried out, the evaluation index weight is calculated, and the evaluation index is quantified, so as to complete the effect evaluation of spatial planning. Finally, taking Tianjin as an example, the application effect of this design method is verified.
Evaluation system of college physical education teaching reform based on wireless sensor network
Authors: Zhang Long
Published: December 2021

The traditional evaluation system of physical education reform in colleges and universities is usually designed with I/O module. This evaluation system is not real-time and it is difficult to meet the requirements of many evaluation indexes. In order to solve the problem of weak anti-interference and real-time performance of the evaluation system, this paper designs the evaluation system of College Physical Education Reform Based on wireless sensor network from two aspects of hardware design and software design. The hardware part mainly designs the evaluation interface, evaluation circuit, processor and display; The software part analyzes the principle of software evaluation, develops a new evaluation system and a new software evaluation process, and completes the work through three steps: communication information collection, communication information evaluation processing and information display. Compared with the traditional system, the experimental results show that the system can complete the accurate evaluation in a short time, and can quickly transmit the evaluation results to the LCD, which has strong real-time performance, effectively solves the problem of long delay in the traditional system, ensures the evaluation effect, and improves the quality of teaching reform.
Semi supervised inspection algorithm of automatic packaging curve based on deep learning
Authors: He Yong
Published: December 2021

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.
Virtual interaction algorithm of cultural heritage based on multi feature fusion
Authors: Li Hao
Published: December 2021

During the traditional cultural heritage virtual interaction algorithm in the interaction action recognition, the database is too single, resulting in low recognition accuracy, recognition time-consumer and other issues. Therefore, this paper introduces the multi feature fusion method to optimize the cultural heritage virtual interaction algorithm. Kinect bone tracking technology is applied to identify the movement of the tracking object, 20 joints of the human body are tracked, and interactive action recognition is realized according to the fingertip candidate points. In order to carry out the judgment virtual interactive operation of subsequent recognition actions, a multi feature fusion database is established. The mean shift is used to derive the moving mean of the target’s action position and to track the interactive object. The Euclidean distance formula is used to train samples of multi feature fusion database data to realize the judgment of recognition action and virtual interaction. In order to verify the feasibility of the research algorithm, the virtual interactive script of ink painting in a cultural heritage museum is used to simulate the research algorithm, and a comparative experiment is designed. The experimental results show that the proposed algorithm is superior to the traditional virtual interactive algorithm in recognition accuracy and efficiency, which proves the feasibility of this method.
Lighting design method of museum exhibition hall based on Internet of Things and deep learning
Authors: Chen Han
Published: December 2021

In order to improve the lighting effect of the museum exhibition hall, clearly express the exhibition content of the museum exhibition hall, a lighting design method of museum exhibition hall based on Internet of Things and deep learning is proposed. According to the characteristics and functions of light sources and lamps, select appropriate light sources and lamps, and establish a convolutional neural network to evaluate the performance of lighting characteristic network model through computing accuracy, precision, recall and F1 score. Because the illumination of museum exhibition hall cannot be too high, the light projection method is designed to realize the lighting design of museum exhibition hall from two aspects: lighting mode and lighting characteristics, environmental lighting and light source form. The experimental results show that the lighting design method of the museum exhibition hall based on the Internet of Things and deep learning can achieve more than 70%, which has a good lighting effect and can clearly express the display content of the museum exhibition hall.
Automatic update method of GIS platform drawing model based on machine learning
Authors: Yuan Ji
Published: December 2021

Aiming at the problem that the number of data bytes in the traditional automatic update technology of GIS platform is small, a method of automatic update of GIS platform graph model based on machine learning is studied. Firstly, the data of the GIS platform model is convolved by the iso-linear feature detection operator in the automatic updating technology of the GIS platform model, and the calculated data of the GIS platform model is expressed as spatial data. A reasonable updating criterion is established, the spatial relationship of GSI data is reconstructed by the measure of updating criterion, the data vector of GIS platform model updated within the updating time range is calculated, and the regional data elements in the space are constantly changed to complete the data updating of GIS platform model. The experimental results show that compared with the automatic updating method of GIS platform model, the proposed method can update more data bytes with the same number of data bytes.
Automatic verification method of progressive image model matching information based on machine learning
Authors: Yuan Jie , Ji Yuan , Zhu Zhou , Huang Liya , Qian Junfeng , Xiong Zhiwen
Published: December 2021

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.
Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester
Authors: Ji Hong , He Xun , Ding Li , Qu Zhe , Huang Wenkang , Wang Wanzhang
Published: December 2021

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.
Design of feasibility analysis platform for college physical education based on mobile social network
Authors: Han Yingchao , Liao Jianlu
Published: December 2021

In order to promote the development of sports distance network education, a college sports teaching feasibility analysis platform based on mobile social network is designed. The key technologies such as Ajax, database and XML, Java Web containers applied to mobile social networks provide a technical basis for the platform design of this paper. Design the feasibility analysis platform framework using MVC mode, three platform functional modules were designed. Among it, the member function module is responsible for the registration and modification of the platform personal information of members; The feasibility analysis interactive discussion module is based on the cloud database storage and analysis platform user discussion records; The feasibility evaluation module of mobile social network is based on mobile social network, builds the hierarchical model of feasibility analysis and completes the design of feasibility analysis of distance teaching in universities. Experimental performance tested the platform, with an average response time of 2.3 ms and an analysis time of 18 ms. The feasibility analysis results of the platform have high reliability and effectiveness to meet the practical application requirements.
Two-tier end-edge collaborative computation offloading for edge computing
Authors: Xiao Yong , Wei Ling , Feng Junhao , En Wang
Published: December 2021

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.
Identification of crowd behaviour patterns using stability analysis
Authors: Muhammed Anees V. , Santhosh Kumar G.
Published: December 2021

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.
Intelligent fault diagnosis using image representation of multi-domain features
Authors: Zhang Yulong , Zhang Chaofei , Tan Jian , Lim Frank , Duan Menglan
Published: December 2021

Deep learning (DL) algorithms, especially the convolutional neural network (CNN), have been proven as a newly developed tool in machinery intelligent diagnosis. However, the current CNN-based fault diagnosis studies usually consider features or images extracted from a single domain as model input. This single domain information may not reflect fault patterns comprehensively, leading to low modeling accuracy and inaccurate diagnostic results. To overcome this limitation, this paper proposes a new CNN-based fault diagnosis approach using image representation considering multi-domain features of vibration signals. First, multi-domain features of vibration signals are extracted. These extracted features are then used to construct a n × n matrix, and subsequently to form images by RGB color transformations. This image transformation technique allows for capturing complementary and rich diagnostic information from multiple domains. At last, these images associated with different mechanical defects are fed into a CNN model that is improved based on the classic LeNet-5 CNN architecture for fault diagnosis and identification. Comparative experiments with the traditional feature extraction methods as well as state-of-the-art CNN-based methods are also investigated. Experimental studies on rolling bearings validate the effectiveness and superiorities of the proposed approach.
A fully supervised universal adversarial perturbations and the progressive optimization
Authors: Sun Guangling , Hu Haoqi , Zhang Xinpeng , Lu Xiaofeng
Published: December 2021

Universal Adversarial Perturbations(UAPs), which are image-agnostic adversarial perturbations, have been demonstrated to successfully deceive computer vision models. Proposed UAPs in the case of data-dependent, use the internal layers’ activation or the output layer’s decision values as supervision. In this paper, we use both of them to drive the supervised learning of UAP, termed as fully supervised UAP(FS-UAP), and design a progressive optimization strategy to solve the FS-UAP. Specifically, we define an internal layers supervised objective relying on multiple major internal layers’ activation to estimate the deviations of adversarial examples from legitimate examples. We also define an output layer supervised objective relying on the logits of output layer to evaluate attacking degrees. In addition, we use the UAP found by previous stage as the initial solution of the next stage so as to progressively optimize the UAP stage-wise. We use seven networks and ImageNet dataset to evaluate the proposed FS-UAP, and provide an in-depth analysis for the latent factors affecting the performance of universal attacks. The experimental results show that our FS-UAP (i) has powerful capability of cheating CNNs (ii) has superior transfer-ability across models and weak data-dependent (iii) is appropriate for both untarget and target attacks.
Transfer learning based handwritten character recognition of tamil script using inception-V3 Model
Authors: Gayathri R. , Babitha Lincy R.
Published: December 2021

The paper describes the excellent method to get first-rate accuracy and performance in the discipline of Tamil character recognition in a handwritten mode. However, the subject is still at a nascent stage and grossly lacks adequate accuracy in the Tamil language, even though several studies have been conducted within the discipline of handwritten character recognition. This paper draws the attention to the offline handwritten recognition for the Tamil language using the Inception-v3 based transfer learning method. The proposed work is conducted on the readily available HP Tamil handwritten character offline dataset (Hewlett-Packard Lab: hpl-tamil-iso-char-offline-1.0.). It reveals that with the suitable arrangement of transfer learning approach with Inception-v3, the pre-trained model can achieve the recognition accuracy of 93.1%, overtaking the former deep learning designs. The achieved accuracy is due to the use of a pre-trained version with transfer learning that regularly hastens the method of the training process on a new task. Overall, this results in higher accuracy and a more capable version.
A deep learning model based anomalous behavior detection for supporting verifiable access control scheme in cloud servers
Authors: Vijayanand S. , Saravanan S.
Published: December 2021

Due to the growth of Big Data (BD) storage and access in cloud computing infrastructure, the detection of anomalies for Cloud Servers (CSs) is essential to ensure data confidentiality. Over the past decades, different security systems have been designed based on various methods like encryption, Access Policy (AP) control schemes, signcryption and so on. Among many security systems, a new Improved NTRU (INTRU) decryption based on the AP strategy has been suggested to secure the BD processed by the CSs. Also, the shared secret data was authenticated to defend the clients from anomalies in the cloud. But, the AP upgrade must not degrade the confidentiality of storing information, reveal trust in the CS or cause any different security challenges. It is not considered that such security challenges occur when the data owner shares its data with many CSs. Hence in this article, an INTRU with Detecting Anomalous in CS (INTRU-DACS) system is proposed that employs a deep learning-based Anomaly Detection System (ADS) to handle and secure the BD stored in the CSs. The main goal of this method is to effectively identify the abnormalities in the real world by the conduct utilization, i.e., the System Call Identifier Sequences (SCISs) created from CSs in which these conducts are associated with BD. Initially, effective data summarization is constructed via different feature states to analyze the SCISs of specific durations. After that, an anomaly identification algorithm is proposed to train and test the streaming of raw SC sequences. This observable SCs execution task of CSs is gathered from log files. The variations of such SCISs having a specified duration are random for usual and unusual sequences. So, the fact of current normal and abnormal services is recognized regarding their SCISs. Such normal and abnormal behavioral states are learned from Convolutional Neural Network-Hidden Markov Model (CNNHMM) classifier to identify the anomalies in CSs. But, it is still a challenging process because of the patterns of usual and unusual events. The performance is not effective since it models only the conduct of a huge number of SCISs created from a single CS. As a result, a Secure Access Control Scheme with DACS (SACS-DACS) system is proposed in which a Multidimensional Feature Misbehavior Server Detection method (MFMSD) is introduced for detecting anomalies in multiple CSs. In this method, large-scale SCISs of multiple CSs are extracted, including different features such as network traffic sequence features, CPU energy usage and memory usage from host logs. These extracted multidimensional features are fed to the CNNHMM that identifies the anomalies and maximizes the detection accuracy. At last, the simulation results demonstrate the effectiveness of the SACS-DACS and INTRU-DACS as compared to the INTRU.
Ensemble-based deep meta learning for medical image segmentation
Authors: Ahmed Usman , Lin Jerry Chun-Wei , Srivastava Gautam
Published: December 2021

Deep learning methods have led to the state-of-the-art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders for further collaboration. However, limited labeled data set limits the deep learning algorithms to be generalized for one domain into another. To handle the problem, meta-learning helps to solve this issue especially it can learn from a small set of data. We proposed a meta-learning-based image segmentation model that combines the learning of the state-of-the-art models and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segment part and remove noise from the new test images. The proposed model can achieve 0.94 precision and 0.92 recall. The ability is to increase 3.3% among the state-of-the-art algorithms.
Multi-label classification of feedbacks
Authors: Ruiz Alonso Dorian , Zepeda Cortés Claudia , Castillo Zacatelco Hilda , Carballido Carranza José Luis , García Cué José Luis
Published: December 2021

This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation, praise and other. Four multi-label classification methods of the data transformation approach - binary relevance, classification chains, power labelset and rakel-d - are compared with the base algorithms SVM, Random Forest, Logistic Regression and Naive Bayes. The methodology was applied to a case study in which 11013 feedbacks written in Spanish language from 121 online courses of the Law degree from a public university in Mexico were collected from the Blackboard learning manager system. The results show that the random forests algorithms and vector support machines will have the best performance when using the binary relevance transformation and classifier chains methods.
Wavelets as activation functions in Neural Networks
Authors: Herrera Oscar , Priego Belém
Published: December 2021

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.
An autoencoder-based representation for noise reduction in distant supervision of relation extraction
Authors: García-Mendoza Juan-Luis , Villaseñor-Pineda Luis , Orihuela-Espina Felipe , Bustio-Martínez Lázaro
Published: December 2021

Distant Supervision is an approach that allows automatic labeling of instances. This approach has been used in Relation Extraction. Still, the main challenge of this task is handling instances with noisy labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). The approaches reported in the literature addressed this problem by employing noise-tolerant classifiers. However, if a noise reduction stage is introduced before the classification step, this increases the macro precision values. This paper proposes an Adversarial Autoencoders-based approach for obtaining a new representation that allows noise reduction in Distant Supervision. The representation obtained using Adversarial Autoencoders minimize the intra-cluster distance concerning pre-trained embeddings and classic Autoencoders. Experiments demonstrated that in the noise-reduced datasets, the macro precision values obtained over the original dataset are similar using fewer instances considering the same classifier. For example, in one of the noise-reduced datasets, the macro precision was improved approximately 2.32% using 77% of the original instances. This suggests the validity of using Adversarial Autoencoders to obtain well-suited representations for noise reduction. Also, the proposed approach maintains the macro precision values concerning the original dataset and reduces the total instances needed for classification.
Motion estimation in vehicular environments based on Bayesian dynamic networks
Authors: Reyes-Cocoletzi Lauro , Olmos-Pineda Ivan , Olvera-López J. Arturo
Published: December 2021

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.
Identifying the polarity of a text given the emotion of its author
Authors: Sánchez Belém Priego , Cabrera Rafael Guzman , Carrillo Michel Velazquez , Castro Wendy Morales
Published: December 2021

The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author’s Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics.
Improved neural machine translation for low-resource English–Assamese pair
Authors: Laskar Sahinur Rahman , Khilji Abdullah Faiz Ur Rahman , Pakray Partha , Bandyopadhyay Sivaji
Published: December 2021

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.
YouTube based religious hate speech and extremism detection dataset with machine learning baselines
Authors: Ashraf Noman , Rafiq Abid , Butt Sabur , Shehzad Hafiz Muhammad Faisal , Sidorov Grigori , Gelbukh Alexander
Published: December 2021

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.
Creating a corpus of historical documents for emotions identification
Authors: Vázquez-González Stephanie , Somodevilla-García María , López Rosalva Loreto , Gómez-Adorno Helena
Published: December 2021

The aim of this article is to contextualize and describe the gathering and annotation of a conventual Hispanic and Novo Hispanic texts corpus for emotions identification. Such corpus will be the dataset for an emotions identification model based on machine learning ∖ deep learning techniques. Furthermore, this document describes several exploratory experiments carried out on the corpus. Within these experiments, it is described how the corpus is also used to obtain a lexicon mapped to polarities and emotions, and how some of the documents are hand-labeled by experts for the evaluation of the Machine Learning ∖ Deep learning -based emotion classification model. Finally, the future uses and experiments with said corpus are described.
Anbar: Collection and analysis of a large scale Urdu language Twitter corpus
Authors: Tahir Bilal , Mehmood Muhammad Amir
Published: December 2021

The confluence of high performance computing algorithms and large scale high-quality data has led to the availability of cutting edge tools in computational linguistics. However, these state-of-the-art tools are available only for the major languages of the world. The preparation of large scale high-quality corpora for low-resource language such as Urdu is a challenging task as it requires huge computational and human resources. In this paper, we build and analyze a large scale Urdu language Twitter corpus Anbar. For this purpose, we collect 106.9 million Urdu tweets posted by 1.69 million users during one year (September 2018-August 2019). Our corpus consists of tweets with a rich vocabulary of 3.8 million unique tokens along with 58K hashtags and 62K URLs. Moreover, it contains 75.9 million (71.0%) retweets and 847K geotagged tweets. Furthermore, we examine Anbar using a variety of metrics like temporal frequency of tweets, vocabulary size, geo-location, user characteristics, and entities distribution. To the best of our knowledge, this is the largest repository of Urdu language tweets for the NLP research community which can be used for Natural Language Understanding (NLU), social analytics, and fake news detection.
Extended Disease Control with Unconventional Cabozantinib Dose Increase in Metastatic Renal Cell Carcinoma
Authors: Sharma Akanksha , Elias Roy , Christie Alana , Williams Noelle S. , Pedrosa Ivan , Bjarnason Georg A. , Brugarolas James
Published: December 2021

BACKGROUND:

Cabozantinib is among the most potent tyrosine kinase inhibitors (TKIs) FDA-approved for metastatic renal cell carcinoma (mRCC). Effective treatments after progression on cabozantinib salvage therapy are limited. Dose escalation for other TKIs has been shown to afford added disease control.

OBJECTIVE:

We sought to evaluate whether dose escalation of cabozantinib (Cabometyx®) from conventional doses in select patients with limited treatment options offered additional disease control. We asked how cabozantinib dose increases may affect circulating drug levels.

METHODS:

We identified patients with mRCC at the University of Texas Southwestern Medical Center who were treated with cabozantinib dose escalation to 80 mg after progressing on conventional cabozantinib 60 mg. We then queried leading kidney cancer investigators across the world to identify additional patients. Finally, we reviewed pharmacokinetic (PK) data to assess how higher doses impacted circulating levels by comparison to other formulations (Cometriq® capsules).

RESULTS:

We report six patients treated at two different institutions with cabozantinib-responsive disease and good tolerability, where cabozantinib was dose escalated (typically to 80 mg, but as high as 120 mg) after progression on 60 mg, a strategy that resulted in added disease control (median duration, 14 months; 95% Confidence Interval [CI]: 8 –Not Estimable[NE]). Four patients (66.7%) had disease control lasting at least 1 year. No grade III/IV adverse events were identified in this small, select, cohort. A comparison of PK data to FDA-approved cabozantinib 140 mg capsules suggests that cabozantinib 80 mg tablets results in comparable exposures.

CONCLUSIONS:

mRCC patients with cabozantinib responsive disease and reasonable tolerability may benefit from dose escalation at progression.
Assessment of health and social security agency participants proportion using hierarchical bayesian small area estimation
Authors: Yanuar Ferra , Sari Atika Defita , Devianto Dodi , Zetra Aidinil
Published: December 2021

Data on the number of health insurance participants at the subdistrict level is crucial since it is strongly correlated with the availability of health service centers in the areas. This study’s primary purpose is to predict the proportion of health and social security participants of a state-owned company named Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS) in eleven subdistricts in Padang, Indonesia. The direct, ordinary least square, and hierarchical Bayesian for small area estimation (HB-SAE) methods were employed in obtaining the best estimator for the BPJS participants in these small areas. This study found that the HB-SAE method resulted in better estimation than two other methods since it has the smallest standard deviation value. The auxiliary variable age (percentage of individuals more than 50 years old) and the percentage of health complaints have a significant effect on the proportion of the number of BPJS participants based on the HB-SAE method.
Experimental and theoretical observations of alkylated EOSIN based “turn-on” superoxide sensor as well as its anti-microbial study
Authors: Sandhu Navjot , Madaan Simran , Pandey Satish K. , Singh Amritpal , Kumar Kamlesh , Yadav Rajesh K. , Shrivastava Rahul , Singh Atul P.
Published: December 2021

The alkylating as well as alkoxylating behavior of dialkyl sulfites are well presented in the literature. In the present work dialkyl sulphites behavior has been investigated towards EOSIN Y (tetrabrominated fluorescein). The products of the reactions (dialkylated eosin Y) reveal that alkylation takes place at the both active sites (at hydroxyl and carboxyl functionalities) in a single step through sulphur-centred Arbuzov type rearrangement. Density functional theory (DFT) has been used as a tool to analyze the role of the halogen groups present at the periphery of the xanthene moiety in alkylation at hydroxyl and carboxy positions of EOSIN Y. Furthermore, in photo-physical study, it was observed that compound C acts as a superoxide sensor specifically with the detection limit 63μM. Its anti-microbial activity was checked against the E. coli and S-aureus using the agar well diffusion assay and, it was observed that it could be used as better antimicrobial agent.