Those who learned Python faster, and with greater accuracy, tended to have a mix of strong problem-solving and language abilities.
But coding also has a foundation in human language: programming involves creating meaning by stringing symbols together in rule-based ways.
New research finds that a natural aptitude for learning languages is a stronger predictor of learning to program than basic math knowledge.
Summary: Social media bots are continuously evolving and becoming more ‘human-like’ in the way they talk and interact on online platforms. Previous research has focused on bot detection, but little attention has been devoted to the characterization and measurement of the behavior and activity of bots, compared to humans. In this study, researchers have revealed distinct behavioral differences between human and bot activity on social media which could be leveraged to improve bot detection strategies.
Bots are social media accounts which are controlled by artificial software rather than by humans and serve a variety of purposes from news aggregation to automated customer assistance for online retailers. However, bots have recently been under the spotlight as they are regularly employed as part of large-scale efforts on social media to manipulate public opinion, such as during electoral campaigns.
A new study in Frontiers in Physics has revealed the presence of short-term behavioral trends in humans that are absent in social media bots, providing an example of a ‘human signature’ on social media which could be leveraged to develop more sophisticated bot detection strategies. The research is the first study of its kind to apply user behavior over a social media session to the problem of bot detection.
“Remarkably, bots continuously improve to mimic more and more of the behavior humans typically exhibit on social media. Every time we identify a characteristic we think is prerogative of human behavior, such as sentiment of topics of interest, we soon discover that newly-developed open-source bots can now capture those aspects,” says co-author Emilio Ferrara, Assistant Professor of Computer Science and Research Team Leader at the University of Southern California Information Sciences Institute.
In this work, the researchers studied how the behavior of humans and bots changed over the course of an activity session using a large Twitter dataset associated with recent political events. Over the course of these sessions, the researchers measured various factors to capture user behavior, including the propensity to engage in social interactions and the amount of produced content, and then compared these results between bots and humans.
To study the behavior of bot and human users over an activity session, the researchers focused on indicators of the quantity and quality of social interactions a user engaged in, including the number of retweets, replies and mentions, as well as the length of the tweet itself. They then leveraged these behavioral results to inform a classification system for bot detection to observe whether the inclusion of features describing the session dynamics could improve the performance of the detector. a range of machine learning techniques were used to train two different sets of classifiers: one including the features describing the session dynamics and one with-out those features, as a baseline.
The researchers found, among hu
Summary: Data queries written in Python, a commonly used programming language, can grind data analytics platforms to a crawl, but a new platform may finally solve the Python efficiency problem.
Researchers from Brown University and MIT have developed a new data science framework that allows users to process data with the programming language Python — without paying the “performance tax” normally associated with a user-friendly language.
The new framework, called Tuplex, is able to process data queries written in Python up to 90 times faster than industry-standard data systems like Apache Spark or Dask. The research team unveiled the system in research presented at SIGMOD 2021, a premier data processing conference, and have made the software freely available to all.
“Python is the primary programming language used by people doing data science,” said Malte Schwarzkopf, an assistant professor of computer science at Brown and one of the developers of Tuplex. “That makes a lot of sense. Python is widely taught in universities, and it’s an easy language to get started with. But when it comes to data science, there’s a huge performance tax associated with Python because platforms can’t process Python efficiently on the back end.”
Platforms like Spark perform data analytics by distributing tasks across multiple processor cores or machines in a data center. That parallel processing allows users to deal with giant data sets that would choke a single computer to death. Users interact with these platforms by inputting their own queries, which contain custom logic written as “user-defined functions” or UDFs. UDFs specify custom logic, like extracting the number of bedrooms from the text of a real estate listing for a query that searches all of the real estate listings in the U.S. and selects all the ones with three bedrooms.
Because of its simplicity, Python is the language of choice for creating UDFs in the data science community. In fact, the Tuplex team cites a recent poll showing that 66% of data platform users utilize Python as their primary language. The problem is that analytics platforms have trouble dealing with those bits of Python code efficiently.
Data platforms are written in high-level computer languages that are compiled before running. Compilers are programs that take computer language and turn it into machine code — sets of instructions that a computer processor can quickly execute. Python, however, is not compiled beforehand. Instead, computers interpret Python code line by line while the program runs, which can mean far slower performance.
“These frameworks have to break out of their efficient execution of compiled code and jump into a Python interpreter to execute Python UDFs,” Schwarzkopf said. “That process can be a factor of 100 less efficient than executing compiled code.”