Situated Testimony: Forensic Architecture’s Memory Objects

“Forensic Architecture” describes both the research agency, founded in 2011, as well as its investigative method and aesthetic practice. As an emerging discipline, forensic architecture exploits the relation between space, material, and memory. My aim in this article is to consider how the agency’s “memory objects”—aestheticized virtual renderings of their investigations—operate as testimonial objects, evidentiary archives, and simulated sites of conscience. I attend to one “memory object” in particular, a film titled “Drone Strike Investigation Case no. 2: Mir Ali, North Waziristan, 4 October 2010; The Architecture of Memory,” an investigation which the U.N. Special Rapporteur on Counter Terrorism and Human Rights commissioned Forensic Architecture to undertake. This article suggests that this virtual “memory object” troubles the status of both the human witness and the physical landscape to which it refers.

The memory forensic research oriented to segment heap in Windows 10 system

The current forensic research on heaps mainly extracts information from the heap of Linux and the NT heap of Windows. However, the study of how to extract the information on the segment heap in the Windows 10 from dump files is not sufficient. To reproduce the internal information on the segment heap, this paper proposes a method for locating and extracting the internal information on the segment heap in the Windows 10 according to the field offset in the vtype description information of memory object. The method uses the pool scanning technology to locate the process object, obtains the starting position of the process heap and scans the process heap according to the structural information on the process object and the process environment block object. Then it locates the position of the segment heap with its feature values, thereby extracting its internal information. Based on the analysis results, five forensic plugins for extracting the information on the segment heap were developed on the Volatility framework. The experimental results show that this method can effectively extract the information on the address of each segment heap and its internal components in the memory and on the size of committed memory, etc. The information can help investigators to analyze the digital traces left in the memory by cyber criminals or cyber attackers.

Language-agnostic integrated queries in a managed polyglot runtime

Language-integrated query (LINQ) frameworks offer a convenient programming abstraction for processing in-memory collections of data, allowing developers to concisely express declarative queries using general-purpose programming languages. Existing LINQ frameworks rely on the well-defined type system of statically-typed languages such as C # or Java to perform query compilation and execution. As a consequence of this design, they do not support dynamic languages such as Python, R, or JavaScript. Such languages are however very popular among data scientists, who would certainly benefit from LINQ frameworks in data analytics applications. In this work we bridge the gap between dynamic languages and LINQ frameworks. We introduce DynQ, a novel query engine designed for dynamic languages. DynQ is language-agnostic, since it is able to execute SQL queries in a polyglot language runtime. Moreover, DynQ can execute queries combining data from multiple sources, namely in-memory object collections as well as on-file data and external database systems. Our evaluation of DynQ shows performance comparable with equivalent hand-optimized code, and in line with common data-processing libraries and embedded databases, making DynQ an appealing query engine for standalone analytics applications and for data-intensive server-side workloads.

A Hot-Coal theory of Working Memory

Working memory allows us to selectively remember and flexibly manipulate a limited amount of information. Importantly, once we learn a certain operation, it generalizes to any memory object, not just the objects it has been trained on. Here we propose a conceptual model for how this might be achieved on the neural network level. It relies on spatial computing, in which sensory information flows spatially within the network over time. As a result, information about, for instance, object order can be retrieved agnostically to the detailed synaptic connectivity responsible for encoding specific memory items. This spatial flow is reflected in low-dimensional brain activity complementing high-dimensional activity that accounts for storing the sensory information itself. By comparing the dimensionality of local field potentials and spiking activity from prefrontal cortex of rhesus macaques performing multi-item working memory tasks we verify predictions from this model. We discuss how spatial computing may be a principle to aid generalization and zero-shot learning by utilizing spatial dimensions as an additional information encoding dimension. The new model also helps explain several aspects of neurophysiological activity related to working memory control, including dimensionality, context-dependent selectivity as well as persistent and non-persistent delay activity.