Fostering Imagined Economies on Twitter

Startups are entrepreneurial organisations that aim to develop a scalable and disruptive business. However, these small ventures operate in an environment of extreme uncertainty. The startup economy takes place in the present but is directed towards the future. This chapter critically investigates in online and offline realms the circulation of imagined futures that create causal links to bridge the gap between the present economic scenario and potential futures in the Italian startup food economy. This work adopted a mixed-method approach framed in a qualitative exploratory strategy which was designed to integrate qualitative techniques and digital methods. This work concludes by highlighting the co-evolutionary process between online and offline realms. On the one hand, online narratives allow economic actors to perform in radical uncertain economic contexts, while, on the other hand, the offline practices give legitimacy and credibility to these potential future scenarios.

Counteracting Hoax in Social Media Through Tabayyun By Islamic Student Community

This study aims to find out hoaxes on social media and how the form of tabayyun and its implementation can be a method of preventing hoaxes spread on social media. Using this type of research mixed methods with the design of the Sequential Exploratory Strategy. The subjects in this study N Total = 514 who were Muslim students at universities in Palembang City which were determined using purposive sampling technique. The data collection method used an online survey. Data analysis using coding techniques (open coding, axial coding and selective coding) version 9 of the Atlas.ti program and product moment analysis and testing for level categorization of the IBM SPSS version 24 program. This study found, first, the form of hoaxes on social media: 1) News lie; 2) False information; 3) Does not match the facts. Second, students have not implemented tabayyun optimally in responding to news on social media. The form of the application of tabayyun is to seek the truth by thinking critically, observing and confirming the news and information obtained.

Yes! I Want My Non-Cognitive Skills to Be Improved: Perceptions on an ICT-Enabled Learning Journey

Non-cognitive skills (NCS) are important for personal development and enhancing employability. However, as related literature points out, designs for NCS development are challenged by their fidelity, complexity, technology pedagogy and content value, user-centricity and so forth. Thus, this study investigates on 1) how do individuals recognise their needs, challenges and motivations for improving NCS, and 2) how do they envision their individual roadmap towards NCS achievement. An exploratory strategy is followed in capturing the learners’ perceptions. Expected data were collected by a questionnaire aiming at a sample of 80 purposely selected employable adults, followed by interviews with 11 randomly selected individuals among the sample. The outcomes provided feedback towards optimising design concepts for an NCS learning environment. Consequently, the framework for NCS improvement must include components, in the order of importance, tools for assessment of NCS, premises for learning NCS, dynamic CV, and linking to the occupations of individual interests as envisioned by the respondents. The participants recognised the significance of NCS and that NCS contribute meaningfully to their personal and professional growth. As such, research efforts shall be invested in evaluating the methods and tools in a systematic user-centric process to determine their effectiveness and impact in lifelong learning.

Anticipatory dynamics in the human brain guide foraging for primary rewards

Foraging is a fundamental food-seeking behavior in a wide range of species that enables survival in an uncertain world. During foraging, behavioral agents constantly face a trade-off between staying in their current location or exploring another. Despite ethological generality and importance of foraging, it remains unclear how the human brain guides continuous decision in such situations. Here we show that anticipatory activity dynamics in the anterior prefrontal cortex (aPFC) and hippocampus underpin foraging for primary rewards. While functional MRI was performed, humans foraged for real liquid rewards available after tens of seconds, and continuous decision during foraging was tracked by a dynamic pattern of brain activity that reflected anticipation of a future reward. When the dynamic anticipatory activity in the aPFC was enhanced, humans remained in their current environment, but when this activity diminished, they explored a new environment. Moreover, the anticipatory activity in the aPFC and hippocampus was associated with distinct decision strategies: aPFC activity was enhanced in humans adopting an exploratory strategy, whereas those remaining stationary showed enhanced activity in the hippocampus. Our results suggest that anticipatory dynamics in the fronto-hippocampal mechanisms underlie continuous decision-making during human foraging.

Pupil Dilation and Response Slowing Distinguish Deliberate Explorative Choices in the Probabilistic Learning Task

This study examined whether pupil size and response time would distinguish directed exploration from random exploration and exploitation. Eighty-nine participants performed the two-choice probabilistic learning task while their pupil size and response time were continuously recorded. Using LMM analysis, we estimated differences in the pupil size and response time between the advantageous and disadvantageous choices as a function of learning success, i.e., whether or not a participant has learned the probabilistic contingency between choices and their outcomes. We proposed that before a true value of each choice became known to a decision-maker, both advantageous and disadvantageous choices represented a random exploration of the two options with an equally uncertain outcome, whereas the same choices after learning manifested exploitation and direct exploration strategies, respectively. We found that disadvantageous choices were associated with increases both in response time and pupil size, but only after the participants had learned the choice-reward contingencies. For the pupil size, this effect was strongly amplified for those disadvantageous choices that immediately followed gains as compared to losses in the preceding choice. Pupil size modulations were evident during the behavioral choice rather than during the pretrial baseline. These findings suggest that occasional disadvantageous choices, which violate the acquired internal utility model, represent directed exploration. This exploratory strategy shifts choice priorities in favor of information seeking and its autonomic and behavioral concomitants are mainly driven by the conflict between the behavioral plan of the intended exploratory choice and its strong alternative, which has already proven to be more rewarding.

Diversity oriented Deep Reinforcement Learning for targeted molecule generation

In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

Penerapan Pendidikan Karakter di Era Digital Melalui Kegiatan Bazar Bulanan (Monthly Bazaar)

This study aims to make students have an independent character, discipline and responsibility in the digital era through the Monthly Bazaar. The method used in this research was Mixed Method with a sequential exploratory strategy. The sequential exploratory strategy involves collecting and analyzing quantitative data in the second stage based on the results of the first stage. Weights / priorities are more likely to be in the first stage, and the mixing process between these two methods occurs when the researcher makes a connection between qualitative data analysis and quantitative data collection. The results of the study were obtained from qualitative data analysis in the first stage and quantitative data in the second stage. The results of the first stage were obtained from educators' observations of students after completing the monthly bazaar. The results of the second stage are obtained through calculations using a Likert scale and it is obtained the data of 43.75% (ever) if it is categorized in interpretation of scores based on intervals. This data was obtained before the implementation of the monthly bazaar. After implementing the monthly bazaar for two times, the data is 72.75% (often). From the two stages, the relevant results obtained between qualitative and quantitative data analysis showed that there was an independent character, discipline and responsibility embedded in students after the implementation of the monthly bazaar activities.

Diversity Oriented Deep Reinforcement Learning for Targeted Molecule Generation

In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with high inhibitory power for the adenosine A2A and κ opioid receptors. The results reveal that the model can effectively modify the biological affinity of the newly generated molecules towards the craved direction. More importantly, it was possible to find auspicious sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

Challenges and Opportunities for Small and Medium Enterprises in Eastern Indonesia in Facing the COVID-19 Pandemic and the New Normal Era

Eastern Indonesia is one of the areas affected by COVID-19 pandemic. Small and Medium Enterprises (SMEs) such as; culinary businesses are the business sector most affected by this pandemic, where the implementation of social restrictions has resulted in decreased income and also changes in business patterns. This condition certainly provides new challenges and opportunities for SMEs owners. The purpose of this study is to explore the challenges and opportunities of SMEs in eastern Indonesia during the COVID-19 pandemic and the new normal era. The study uses a qualitative approach with an exploratory strategy. The results of the study found that to face the new normal era, SMEs have challenges such as the ability of human resources, understanding of information technology, and business model transformation. An interesting finding in this study is that in facing the new normal era, information technology is not a determining factor for increasing consumer trust and increasing income, but product hygiene and environmental sanitation are the determining factors for the existence of SMEs in eastern Indonesia.