A Story of Surveillance? Past, Present, Prediction

In this essay, I will explore the interrelations and differences between the human and digital technology through the lens of surveillance and prediction modeling: the building of profiles. I will provide some philosophical considerations on surveillance and surveillance practices especially in light of datafication and digitalization – including some epistemological considerations with regard to the underlying assumptions in algorithmic construction of profiles and human identities. The starting point is accidental encounters with the same person in the streets of Copenhagen.

Free (rational) derivation

By representing elements in free fields (over a commutative field and a finite alphabet) using Cohn and Reutenauer’s linear representations, we provide an algorithmic construction for the (partial) non-commutative (or Hausdorff-) derivative and show how it can be applied to the non-commutative version of the Newton iteration to find roots of matrix-valued rational equations.

Parametric methods for constructing the Islamic ornament

The article discusses the method of algorithmic construction of Islamic ornaments, which can be used as a decorative element in architectural design. Two necessary stages are highlighted in the generation of an ornamental motif by means of algorithmic design – the first includes operations on a base mesh consisting of a combination of regular or semiregular cells, for example, drawing rays from the centers of the edges of the original lattice or rotating figures relative to its nodes; the second stage involves the creation of a basic pattern of the ornamental design that fits into a regular or semi-regular polygon, and the decorative motif in the figure is made up of a primitive that is symmetrically reflected relative to any lines, usually rays, emanating from opposite corners of the polygon. The paper analyses the traditional patterns of Islamic ornaments, on the basis of which new designs were built, using visual programming tools (Rhino and Grasshopper).

Designing Forecasting Parameter Algorithm of Environmental Shrimp Using Recurrent Neural Network

With the strong development of science and technology, the study of technologies related to environmental forecasting is important. In recent years, the application of smart technology in aquaculture has been widely applied. Based on the requirement, we focus on predicting the environmental parameters applied in shrimp farming, especially white shrimp, one of the seafood grown in our country. In the paper, we exploit a small branch of identification problem. This paper proposes an algorithmic construction method to predict changes in shrimp farm environmental parameters and simulate the next parameters based on current parameters. The goal of the paper is to reduce the parameter of Recurrent Neural Network (RNN) while ensuring data accuracy. Experimental results show that the proposal algorithm improves up to 85 percent when selecting suitable learning factor of neural networks.

METHODOLOGICAL ASPECTS OF TEACHING PYTHON PROGRAMMING IN THE SCHOOL INFORMATICS COURSE

The article discusses the features of teaching programming in Python of high school students, taking into account the experience of programming in the Pascal language obtained by students in the main school. Based on the analysis of the content of textbooks for in-depth study of informatics and their own experience of teaching informatics, the authors show the advantages of the Python language for motivating students to study it and use specific examples to reveal certain methodological aspects of teaching programming in the Python language. In particular, using a comparative analysis of program codes for implementing data input and output procedures, conditional algorithmic construction, as well as for describing data types in two programming languages, the authors clearly show the advantages of developing programs in Python. More specific details of programming are considered on the example of the theme "Arrays".

The return of the social: Algorithmic identity in an age of symbolic demise

This article explores the socio-algorithmic construction of identity categories based on an ethnographic study of the Israeli data analytics industry. While algorithmic categorization has been described as a post-textual phenomenon that leaves language, social theory, and social expertise behind, this article focuses on the return of the social—the process through which the symbolic means resurface to turn algorithmically produced clusters into identity categories. I show that such categories stem not only from algorithms’ structure or their data, but from the social contexts from which they arise, and from the values assigned to them by various individuals. I accordingly argue that algorithmic identities stem from epistemic amalgams—complex blends of algorithmic outputs and human expertise, messy data flows, and diverse inter-personal factors. Finally, I show that this process of amalgamation arbitrarily conjoins quantitative clusters with qualitative labels, and I discuss the implausibility of seeing named algorithmic categories as explainable.

Who Gets to Choose? On the Socio-algorithmic Construction of Choice

This article deals with choice-inducing algorithms––algorithms that are explicitly designed to affect people’s choices. Based on an ethnographic account of three Israeli data analytics companies, I explore how algorithms are being designed to drive people into choice-making and examine their co-constitution by an assemblage of specifically positioned human and nonhuman agents. I show that the functioning, logic, and even ethics of choice-inducing algorithms are deeply influenced by the epistemologies, meaning systems, and practices of the individuals who devise and use them and that such algorithms are similarly affected by interorganizational relationships, various nonhuman agents, and changing geopolitical contexts. I conclude by discussing the flexibility of choice-inducing algorithms and by arguing that such algorithms are not programmed to induce specific choices but to more generally convert people into choosers, and thus, to algorithmically (re)create the modern need to choose. This article contributes to the growing literature on algorithms and culture and to our understanding of choice-making in contemporary life. At the same time, it provides a new vocabulary that offers to critically engage with algorithms and their power without losing sight of the often very specific contexts from which they arise.