Today's social bots are sophisticated and sometimes menacing. Indeed, their presence can endanger online ecosystems as well as our society.

https://dl.acm.org/doi/pdf/10.1145/2818717

The rise of social bots

The Turing test aimed to recognize the behavior of a human from that of a computer algorithm. Such challenge is more relevant than ever in today's social media context, where limited attention and technology constrain the expressive power of humans, while incentives abound to develop software agents mimicking humans. These social bots interact, often unnoticed, with real people in social media ecosystems, but their abundance is uncertain. While many bots are benign, one can design harmful bots with the goals of persuading, smearing, or deceiving. Here we discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. We then review current efforts to detect social bots on Twitter. Features related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering.

/pdf/the-rise-of-social-bots-4g4ngytedj.pdf

The Rise of Social Bots

Social media have been extensively praised for increasing democratic discussion on social issues related to policy and politics. However, what happens when this powerful communication tools are exploited to manipulate online discussion, to change the public perception of political entities, or even to try affecting the outcome of political elections? In this study we investigated how the presence of social media bots, algorithmically driven entities that on the surface appear as legitimate users, affect political discussion around the 2016 U.S. Presidential election. By leveraging state-of-the-art social bot detection algorithms, we uncovered a large fraction of user population that may not be human, accounting for a significant portion of generated content (about one-fifth of the entire conversation). We inferred political partisanships from hashtag adoption, for both humans and bots, and studied spatio-temporal communication, political support dynamics, and influence mechanisms by discovering the level of network embeddedness of the bots. Our findings suggest that the presence of social media bots can indeed negatively affect democratic political discussion rather than improving it, which in turn can potentially alter public opinion and endanger the integrity of the Presidential election.

Social bots distort the 2016 U.S. Presidential election online discussion

Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities?

Castells, M., Fernandez-Ardevol, M., Qiu, J. L., & Sey, A. (2007). Mobile communication and society: A global perspective. Cambridge, MA: The MIT Press. This theoretically hybrid, statistically sig...

Mobile Communication and Society: A Global Perspective

Systems that classify influential users in social networks have been used frequently and are referenced in scientific papers and in the media as an ideal standard of evaluation of influence in the Twitter social network. We consider such systems of evaluation to be complex and subjective, and we therefore suspect that they are vulnerable and easy to manipulate. Based on this, we performed experiments and analysis of two systems for ranking influence: Klout and Twitalyzer. We created simple robots capable of interacting by means of Twitter accounts, and we measured how influent they were. Our results show that it is possible to become influential through simple strategies. This suggests that the systems do not have ideal means to measure and classify influence.

/pdf/you-followed-my-bot-transforming-robots-into-influential-axp3lzf6ha.pdf

You followed my bot! Transforming robots into influential users in Twitter

Cyber-physical production systems retrofitting in context of industry 4.0

Frequent and accurate inspections of industrial components and equipment are essential because failures can cause unscheduled downtimes, massive material, and financial losses or even endanger workers. In the mining industry, belt idlers or rollers are examples of such critical components. Although there are many precise laboratory techniques to assess the condition of a roller, companies still have trouble implementing a reliable and scalable procedure to inspect their field assets. This article enumerates and discusses the existing roller inspection techniques and presents a novel approach based on an Unmanned Aerial Vehicle (UAV) integrated with a thermal imaging camera. Our preliminary results indicate that using a signal processing technique, we are able to identify roller failures automatically. We also proposed and implemented a back-end platform that enables field and cloud connectivity with enterprise systems. Finally, we have also cataloged the anomalies detected during the extensive field tests in order to build a structured dataset that will allow for future experimentation.

https://www.mdpi.com/1424-8220/20/8/2243/pdf

A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry.

Industry 4.0 or Fourth Industrial Revolution is a new concept which involves technological innovations applied in industrial processes. Using technologies such as Internet of Things, Cyber-Physical Systems and cloud computing, industries tend to become more efficient, autonomous and customizable. However, migration process to Industry 4.0 is not instantaneous, as it depends on the economic and technological investments in the industries of each country. In many countries, industries have not reached the Third Industrial Revolution yet and do not have many investments in the industrial sectors, which creates a challenge to modernize existing industries. From this challenge, an alternative to take off to Industry 4.0 with low cost and agility is known as retrofitting, which is the reuse of old equipment and the integration of new Technologies simultaneously. In this paper is proposed, a way of performing the retrofitting process for Industry 4.0, through a platform that, regardless the model or type of the industrial equipment, offers resources to integrate this equipment with Industry 4.0. To implement the platform, requirements and technologies necessary to perform retrofitting for Industry 4.0 were defined. Afterward, the methods usage in the platform were presented. To validate the operation of the platform a case study with a didactic plant of an automated industry was carried out and as a result the impact after retrofitting for Industry 4.0 is shown.

Industry 4.0 Retrofitting

Currently, many developing countries based their transportation infrastructure on roads. These roads qualities have a significant influence on how fast products are delivered and how much it costs. Due to the lack of low-cost solutions or negligence, roadways pavement quality inspections are usually set aside or hardly performed at some places. This fact leads to higher casualties rates, driving discomfort, vehicle damage, and money prejudice. A cheap and accurate way to solve this problem is the use of typical smartphones with accelerometers and GPS sensors to measure the impact inflicted on the car when driving over a poor quality road surface. This scrutiny helps to raise data that can be used by road maintenance staff. Most current systems tend to translate its output to an international standard called IRI (International Roughness Index) but, this practice demands high computer processing. Here is presented "RoadScan", a crowdsourcing Android application that determines pavements quality in a simple and lightweight way. Our approach allows the mobile device to be placed at any position in the car. Considered tests and results are promising, identifying in most cases road regions that have some deformity.

Ricardo Augusto Rabelo Oliveira

Papers

You followed my bot! Transforming robots into influential users in Twitter

Cyber-physical production systems retrofitting in context of industry 4.0

A UAV-Based Framework for Semi-Automated Thermographic Inspection of Belt Conveyors in the Mining Industry.

Industry 4.0 Retrofitting

Using Crowdsourcing Techniques and Mobile Devices for Asphaltic Pavement Quality Recognition