Information Behavior and Political Preferences

This article shows that citizens consider policy positions for the formation of their political preferences when they actively seek and find high-quality information, while they dismiss passively acquired and low-quality information. The study develops an extended theory of information and political preferences that incorporates the process of information acquisition and its connection with information quality. A novel experimental design separates the effects on political preferences due to information behavior as an activity from those due to selective exposure to information. The study applies this design in a laboratory experiment with a diverse group of participants using the example of issue voting and European integration in the context of the 2014 European Parliament elections

Large scale and information effects on cooperation in public good games

The problem of public good provision is central in economics and touches upon many challenging societal issues, ranging from climate change mitigation to vaccination schemes. However, results which are supposed to be applied to a societal scale have only been obtained with small groups of people, with a maximum group size of 100 being reported in the literature. This work takes this research to a new level by carrying out and analysing experiments on public good games with up to 1000 simultaneous players. The experiments are carried out via an online protocol involving daily decisions for extended periods. Our results show that within those limits, participants' behaviour and collective outcomes in very large groups are qualitatively like those in smaller ones. On the other hand, large groups imply the difficulty of conveying information on others' choices to the participants. We thus consider different information conditions and show that they have a drastic effect on subjects' contributions. We also classify the individual decisions and find that they can be described by a moderate number of types. Our findings allow to extend the conclusions of smaller experiments to larger settings and are therefore a relevant step forward towards the understanding of human behaviour and the organisation of our society.A.A. gratefully acknowledges the financial support of the Ministerio de Economía y Competitividad of Spain under grant no. FJCI-2016-28276. This work was also supported by the EU through FET-Proactive Project DOLFINS (contract no. 640772, A.S.) and FET-Open Project IBSEN (contract no. 662725, A.S.), and by the Ministerio de Economía y Competitividad of Spain (grant no. FIS2015-64349-P, J.C. and A.S.) (MINECO/FEDER, UE), and by Ministerio de Ciencia, Innovación y Universidades/FEDER (Spain/UE) through grant PGC2018-098186-B-I00 (BASIC)

Review of research and evaluation on improving adult literacy and numeracy skills

The purposes of this literature review are threefold. First, this review summarises findings of the research from the last decade in six fields identified by the Department for Business, Innovation and Skills (BIS) as critical to its forward planning: (1) the economic, personal and social returns to learning; (2) the quality and effectiveness of provision; (3) the number of learning hours needed for skills gain; (4) learner persistence; (5) the retention and loss of skills over time; (6) the literacy and numeracy skills that are needed. Second, this review assesses this evidence base in terms of its quality and robustness, identifying gaps and recommending ways in which the evidence base can be extended and improved. Thirdly, this review attempts to interpret the evidence base to suggest, where possible, how returns to ALN learning for individuals, employers and wider society might be increased through effective and cost-effective interventions

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Human Activity Recognition (HAR) systems are devised for continuously observing human behavior - primarily in the fields of environmental compatibility, sports injury detection, senior care, rehabilitation, entertainment, and the surveillance in intelligent home settings. Inertial sensors, e.g., accelerometers, linear acceleration, and gyroscopes are frequently employed for this purpose, which are now compacted into smart devices, e.g., smartphones. Since the use of smartphones is so widespread now-a-days, activity data acquisition for the HAR systems is a pressing need. In this article, we have conducted the smartphone sensor-based raw data collection, namely H-Activity , using an Android-OS-based application for accelerometer, gyroscope, and linear acceleration. Furthermore, a hybrid deep learning model is proposed, coupling convolutional neural network and long-short term memory network (CNN-LSTM), empowered by the self-attention algorithm to enhance the predictive capabilities of the system. In addition to our collected dataset ( H-Activity ), the model has been evaluated with some benchmark datasets, e.g., MHEALTH, and UCI-HAR to demonstrate the comparative performance of our model. When compared to other models, the proposed model has an accuracy of 99.93% using our collected H-Activity data, and 98.76% and 93.11% using data from MHEALTH and UCI-HAR databases respectively, indicating its efficacy in recognizing human activity recognition. We hope that our developed model could be applicable in the clinical settings and collected data could be useful for further research.publishedVersio