Miracle-dz/UniFAR-FTUs huggingface dataset:
One foot out the door: Stay/leave ambivalence predicts day‐to‐day fluctuations in commitment and intentions to end the relationship

Why do some people maintain stable feelings of commitment toward their partners, whereas others’ feelings wax and wane from day to day? The current paper draws insight from decision conflict research suggesting that individuals torn between decision options are particularly susceptible to attitude change. In three samples, we validated a stay/leave ambivalence scale to capture internal conflict about whether to remain in versus exit a relationship. In two dyadic daily experience studies, individuals who felt more ambivalent about their relationships experienced greater daily fluctuation in commitment and breakup contemplation compared to less ambivalent individuals. Ambivalent individuals’ relationship intentions were also more strongly tied to their daily experiences, such that they felt more motivated to stay on days with greater relationship positivity, and more motivated to leave on days with greater relationship negativity. We discuss implications of these results for ambivalent individuals, their partners, and our understanding of stay/leave decision processes.
Efficient detection of online communities and social bot activity during electoral campaigns

ABSTRACT Threats of social media manipulation during elections have become a central concern for modern democracies. This study tackles the problem of identifying the purpose and origins of social bots during electoral campaigns. We propose a methodology – uniform manifold approximation and projection combined with user-level document embeddings – that efficiently reveals the community structure of social media users. We show that this method can be used to predict the partisan affiliation of social media users with high accuracy, detect anomalous concentrations of social bots, and infer their geographical origin. We illustrate the methodology using Twitter data from the 2019 Canadian electoral campaign. Our evidence supports the thesis that social bots have become an integral component of campaign strategy for national actors. We also demonstrate how the methodology can be deployed to identify clusters of foreign bots, and we show that such accounts were used to share far-right and environment-related content during the campaign.
Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

BackgroundGastric cancer (GC) is one of the most common and fatal cancers worldwide and effective biomarkers aids in GC management and prognosis. Hence, we explored the role and function of cadherin 6 (CDH6) in diagnosis and prognosis of gastric cancer. MethodsThe expression level of CDH6 in GC tissue and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas dataset (TCGA). The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and expression of CDH6 were evaluated statistically and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. Forty-four GC tissues, corresponding adjacent normal tissues (n=20), and detailed clinical information were collected from Tianjin Medical University General Hospital, CDH6 expression level was detected for further validation. ResultsCDH6 was upregulated in GC samples compared with normal gastric tissue, and GSEA identified the citrate cycle tricarboxylic (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism oxidative phosphorylation, and pentose phosphate pathway as differentially enriched in GCs. According to the area under the ROC curve (AUC) (AUC=0.829 in TCGA and 0.966 in GSE54129), CDH6 had high diagnostic efficiency. Patients with high expression of CDH6 was associated with higher T classification and worse prognoses than those with low CDH6 expression in GC. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001). ConclusionCDH6 was upregulated in GC and high CDH6 expression indicated higher T classification and worse prognoses. CDH6 could be a potentially independent molecular biomarker for diagnosis and prognosis of GC.
Application of Ensemble Learning in EEG Signal Analysis of Fatigue Driving

Fatigue detection has now become an important direction of fatigue driving research. As a reliable physiological signal, EEG signals can be used to detect fatigue driving. This passage uses the idea of ensemble learning to establish an ensemble learning classification model of Bagged Tree, RSM Discrimination and RUSBoosted Tree, and uses the fatigue driving experiment data as the object to divide the fatigue driving object into normal state and fatigue state, using Pearson Correlation Coefficient method build a functional brain network. Six methods of Betweenness Centrality, Edge Betweenness Centrality, Clustering Coefficient, Degree, Local Efficiency, and Rich Club Coefficient are used to process and analyze six kinds of EEG signal characteristics, and then bring them into different in the ensemble learning model, the EEG signal characteristics and the ensemble learning model are matched and selected to improve the accuracy and stability of the detection model. By simulating 24 subjects, the results show that the accuracy and robustness of RSM Discrimination ensemble learning model and Degree method are the best, with an average accuracy rate of 90.35% and a standard deviation of 0.081.
Reference Framework for Measuring the Level of Technological Acceptance by the Elderly: A Case Study of Virtual Assistants

Elderly people often have problems to use technology. As a result, the existing diversity of systems and applications adapted to this kind of users is expanding, thus facilitating the communication with their social environment, and supporting their daily activities. The aim of this study was to identify the intention of a group of older adults in Pasto, Colombia, to use smart virtual assistants and the activities in which they think these devices would be useful. Considering the participants’ characteristics and in order to capture their feelings, thoughts, and experiences, we held focus groups to gather precise, clear, and simple information. The results obtained indicate that older adults can potentially use virtual assistants for their daily personal, work-related, and recreational activities. Nevertheless, motivation and technology acceptance are key aspects to ensure the usability of technological systems by this populational group, and user requirements should be well defined in order to identify the characteristics and functionalities that they expect to find in such technology products. Therefore, technology developed for the elderly must meet their essential expectations in order for them to accept and adopt it in their daily lives.Elderly people often have problems to use technology. As a result, the existing diversity of systems and applications adapted to this kind of users is expanding, thus facilitating the communication with their social environment, and supporting their daily activities. The aim of this study was to identify the intention of a group of older adults in Pasto, Colombia, to use smart virtual assistants and the activities in which they think these devices would be useful. Considering the participants’ characteristics and in order to capture their feelings, thoughts, and experiences, we held focus groups to gather precise, clear, and simple information. The results obtained indicate that older adults can potentially use virtual assistants for their daily personal, work-related, and recreational activities. Nevertheless, motivation and technology acceptance are key aspects to ensure the usability of technological systems by this populational group, and user requirements should be well defined in order to identify the characteristics and functionalities that they expect to find in such technology products. Therefore, technology developed for the elderly must meet their essential expectations in order for them to accept and adopt it in their daily lives.
BREACH OF PSYCHOLOGICAL CONTRACTS AND DISCRETIONARY BEHAVIOUR IN NIGERIAN ACADEMICS

This study explored the breach of psychological contracts and discretionary behaviour by Nigerian academics. A survey was employed to elicit information from 378 academics that were randomly selected from 7,131 academics in federal universities in the North-Central Nigeria. The results from the hypotheses testing revealed that most of the universities in the North-Central zone did not comply with the psychological contract (as supported by the negative results from statistical estimates and Levene’s t-values). There was a positive correlation between breach of psychological contract and organisational citizenship behaviour (OCB) as well as positive relationship between breach of psychological contract and deviant workplace behaviour (DWB) (r=0.196; p<0.05 and r=0.126; p<0.05, respectively). The breach of psychological contract has influenced academics in different ways showing a tendency for OCB and DWB. Unfortunately, the tendency for DWB outweigh that of OCB. Therefore, academics who uphold OCB despite the perceived BPC should be encouraged to sustain their good behaviour.
A values based leadership approach to (re)defining Latino manhood and masculinity

ABSTRACT Utilizing a values-based leadership philosophy, the authors explored how Latino undergraduate men make meaning of their masculinity and how this meaning shapes their understanding and performance of leadership. The findings highlighted Familismo Leadership as a form of leadership practiced by Latino men related to how they define masculinity as a form of strength, identify the role of provider as a form of leadership, and consider the performance of leadership as direct action. Recommendations include the importance of an approach to research and practice that engages Latino undergraduate men students via leadership development and involvement that is reflective of the way Latino masculine gender identity and leadership performance is socialized within the social construct of familismo.
Building portfolios based on machine learning predictions

Abstract This paper demonstrates that portfolio optimization techniques represented by Markowitz mean-variance and Hierarchical Risk Parity (HRP) optimizers increase the risk-adjusted return of portfolios built with stocks preselected with a machine learning tool. We apply the random forest method to predict the cross-section of expected excess returns and choose n stocks with the highest monthly predictions. We compare three different techniques—mean-variance, HRP, and 1/N— for portfolio weight creation using returns of stocks from the S&P500 and STOXX600 for robustness. The out-of-sample results show that both mean-variance and HRP optimizers outperform the 1/N rule. This conclusion is in the opposition to a common criticism of optimizers’ efficiency and presents a new light on their potential practical usage.
Co-attention Spatial Reasoning for Visual Question Answering

Visual question answering (VQA) requires both inferring and providing correct answers based on the semantics of the question and the content of the image. We propose a co-attention spatial reasoning model, it models the co-attention, and spatial reasoning attention for the VQA task. The model maps the question text information to image region to reason the fusion features, which contain fine-grained semantic information of two models. Meanwhile, it can obtain the spatial relationship between question words and image regions. The model was trained and verified on the VQA-v2 dataset, the best single model achieves an accuracy rate of 69.55% on the test-dev set.
RELATIONSHIP BETWEEN MOBILE PHONE USES WITH AN ANXIETY AMONG STUDENTS IN UNIVERSITI TEKNOLOGI MARA, SEREMBAN

There is sufficient evidence regarding the potential risk of mobile phone use on anxiety. Therefore, the aim of research was to examine the relationship between mobile phone use and anxiety among students from two faculties in University TeknologI Mara (UiTM) Seremban. This cross-sectional study included 317 students of both genders. The sampling method was measured using proportionate stratified sampling. Level of mobile phone use was measured using Problem Use Mobile Phone (PUMP) Scale and level of an anxiety was measured using Beck Anxiety Inventory (BEI) Scale. In Malaysia, there are very small amount who made this research. The finding of this study showed that majority respondents are at normal level for mobile phone use. Pearson correlation result shows there was significant correlation between mobile phone use with an anxiety among students. This study indicates the important mobile phone use toward an anxiety among university students.
Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches

A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including HfO2 dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs.
An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher R2 score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.
Energy-Aware Scheduling of Streaming Applications on Edge-Devices in IoT-Based Healthcare

The reliance on Network-on-Chip (NoC)-based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this article we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI)-based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming-based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG)-based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.
Understanding equity repurchase motives for different firm set-up: Indian evidence

Corporates express their intention to reward shareholders during repurchase announcements by maximizing their wealth. However, most empirical research finds that stocks’ performance is poor when repurchase announcements are made, and there are no significant abnormal returns. In the Indian context, the present study examines firms’ real intention behind repurchase decisions. The sample comprises 132 firms listed on the Bombay Stock Exchange (BSE) from 2012 to 2018. A Tobit regression model has been used on different firm set-up. The empirical results reveal that low stock valuation is the prominent reason for buybacks among corporates. Firms prefer repurchases to provide abnormal returns to the investors; however, the Indian market does not react much positively to the repurchases, and this might be the reason for less encouraging buybacks in the Indian market. Further, the tender offer is the most preferred mode to open market repurchases. In the case of service firms, undervaluation, low earnings, and low debt ratios are the contributing factors impacting repurchases. Firms with low dividend intend to have more buybacks to reduce their tax burden. Acknowledgment The infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.
Electrochemical deposition induced continuum damage-healing framework for the cementitious composite

The electrochemical deposition method is a promising approach to repair the deteriorated concrete in the aqueous environment. In this paper, a continuum damage-healing framework is presented for the electrochemical deposition method based on the multi-field coupling growth process of the electrochemical deposition products. The ion transportation and the electrode reactions are characterized by employing the Nernst-Planck equation and the current conservation equation. The level set method is adopted to capture the growth of the deposition products. Based on the deposition process, a new empirical healing law is presented, with which a new continuum damage-healing framework is presented for electrochemical deposition method. Numerical examples are conducted by applying the presented framework to the damaged cementitious composite under the tensile loadings. The presented framework is compared with the classic continuum damage-healing theory and the experimental data. The results show that the presented models can describe the electrochemical deposition method induced damage-healing for the cementitious composite. Furthermore, the effects of the healing time, the solution concentration and the external voltage on the damage-healing behaviors are investigated based on our proposed framework.
The self-directed learning of adult music students: A comparison of teacher approaches and student needs

Adult music learners may expect to be more independent and therefore more inclined to engage in self-directed learning than younger learners; however, adults may not feel encouraged or supported to self-direct. In this qualitative study, the relationships between six adult instrumentalists and their teachers were examined using Grow’s Staged Self-Directed Learning (SSDL) Model to determine if there was congruence or a mismatch between individual student learning needs and their teachers’ strategies. Teachers reported a willingness to accommodate the self-direction needs of their students. Even in cases of teacher–student mismatch, more direction from the teacher was welcomed when they encountered technical difficulty or an unfamiliar style. Students who reported that they had little experience playing were more inclined to have low to moderate levels of self-direction, whereas more advanced players reported intermediate to high levels of self-direction. Cultural expectations were found to play an important role in determining if students desire to be self-directed learners. The quality of the teacher–student relationship and communication were both found to be an important determinant of successful collaboration. These findings suggest that the theoretical application of the SSDL model could provide teachers a means to assess and discern their adult students’ learning needs.
The Isoflavone Puerarin Exerts Anti-Tumor Activitiy in Pancreatic Ductal Adenocarcinoma by Suppressing Akt/mTOR Activity

Background: Puerarin (7,4’-dihydroxyisoflavone-8-β-glucopyranoside) is a natural flavonoid compound isolated from the traditional Chinese herb Radix puerariae. Recent studies have demonstrated that puerarin has potential anti-tumor effects via induction of apoptosis and inhibition of proliferation. However, the effect and molecular mechanism of puerarin in pancreatic ductal adenocarcinoma (PDAC) remains unknown.Methods: The effects of puerarin on the proliferation, apoptosis, migration and invasion of pancreatic cancer cells (PCCs), and tumor growth and metastasis in PDAC xenograft mouse model were performed. In addition, Akt/mTOR signaling activity was evaluated both in vivo and in vitro.Results: Puerarin treatment significantly repressed PCC proliferation in concentration- and time-dependent manners. Puerarin induced the mitochondrial-dependent apoptosis of PCCs by causing a Bcl-2/Bax imbalance. Moreover, puerarin inhibited PCC migration and invasion by antagonizing epithelial-mesenchymal transition (EMT). In nude mouse model, PDAC growth and metastasis were reduced by puerarin administration. Mechanistically, puerarin exerted its therapeutic effects on PDAC by suppressing Akt/mTOR signaling. Importantly, puerarin bound to the kinase domain of mTOR protein, affecting the activity of the surrounding amino acid residues associated with the binding of the ATP-Mg2+ complex. Further studies showed that the inhibitory effects of puerarin on PCCs were abolished by a mTOR activator MHY1485, indicating a crucial role of mTOR in anti-tumor effects of puerarin in PDAC. As a result, puerarin hindered glucose uptake and metabolism by downregulating the oxygen consumption rate (OCR) and the extracellular acidification rate (ECAR) dependent upon HIF-1α and glucose transporter GLUT1.Conclusion: Puerarin has therapeutic potential for the treatment of PDAC by suppressing Akt/mTOR activity.