Effects of short-term isolation on social animals' behavior: an experimental case study of Japanese macaque

One of the goals in animal socioecology is to understand the functions and dynamics of group living. While observations of free-ranging animals are a crucial source of information, an experimental investigation that manipulates the size or composition, or both, of animal groups in captivity can also bring complementary contributions to the research inquiry. When paired with an automatic data collection by biologging technology, experimental studies on captive animals also allow for big data analyses based on recent machine learning techniques. As an initial exploration of this research paradigm, the present study inquired to what extent isolation of captive Japanese macaques (Macaca fuscata) changed their movement patterns. Using three-dimensional location trajectories of the macaques that were systematically collected via Bluetooth Low Energy beacons and a deep neural network, we estimated the identifiability of whether a macaque was behaving in isolation or in group. We found that the neural network identified the isolation vs. in-group conditions with more than 90% accuracy from a five-minute location trajectory, suggesting that the isolation caused notable changes from the canonical group-living behaviors. In addition, the isolation made each individual more identifiable from one another based on their location trajectories.

Student Performance Prediction Based on a Framework of Teacher’s Features

Teachers teaching skills are essential to motivate students’ engagement in online educational environments, where students and teachers interact with each other, generating a large amount of educational data. However, to the best of our knowledge, there is no previous study that takes advantage of the huge quantity of teachers’ behavioral data to predict students’ performance. To fill this research gap, we elaborated a theoretically based framework of teacher’s characteristics, that guided an automatic data collection of teachers’ behaviors to predict students’ performance. The implementation of a computational prediction system applied the Random Forest classifying algorithm, which achieved better performance, according to AUC metric, when compared to other algorithms. Two exploratory case studies were conducted to investigate the efficiency and efficacy of the framework of teacher’s features in Goiás Judicial School EJUG teachers in Brazil. The results from the case studies shown that the framework is effective to predict students’ performance. This work contributes to distant education, enabling monitoring teachers’ actions aiming students’ academic best achievements.

Application Management Practice of Investment Statistics Business Based on Blockchain Technology

As a new decentralized infrastructure and distributed computing paradigm, blockchain is of great significance to break through the “mutual trust problem” between statistical specialty and basic data providing departments, and between the upper and lower levels of statistical data reporting units, and further improve the quality of statistical work. This paper first briefly analyzes the problems existing in the current investment statistics business, and introduces the basic principles and characteristics of blockchain technology. This paper studies the idea and process optimization of the application of blockchain technology in investment statistics business, and expounds the specific application schemes of blockchain technology in different links, such as automatic data collection, automatic data verification, automatic report generation, and automatic index release. Finally, the expected effect of the application of blockchain technology in investment statistics business is analyzed.

Body Temperature Detection and Data Collection during COVID-19

At present, COVID-19 is raging in many countries, which poses a great threat to people's life, health, social and economic development. Body temperature screening is very helpful for early detection of potential infected persons and blocking the spread of the epidemic. The paper mainly introduces the body temperature detection and data acquisition system, and designs a system with the function of non-contact temperature measurement and automatic data collection system. This system is suitable for the company or school where the flow of people is large and the temperature needs to be measured quickly, and upload information in real time.

Surveying Heritage with Affordable Yet Accurate Methods. A Study Case from Agrequipa, Peru

One of the key aspects to keep adequate management and preservation of the built heritage is to maintain an adequate registry of the monuments. In many cases, state-of-the-art technologies are being used to develop accurate and rapid surveys, which utilize sophisticated high-cost equipment. However, in developing countries that possess a rich heritage, many of these technologies are beyond the reach of their possibilities, having to rely on manual, inefficient and inaccurate systems that are still used.This research shows several alternatives of relatively low-cost techniques that allow a reliable data collection of built heritage, without losing the richness of the details of the historical architecture. To do this, a comparison is made between different methods of manual, semi-automatic and automatic data collection, analyzing their costs and benefits. Subsequently, a comparative survey is carried out using the most efficient and affordable methods, proposing a methodology that leads to the improvement of surveys in historical buildings without this entailing a significant increase in costs.