When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game.

In many social, cyber-physical, and socio-technical systems, a group of autonomous peers can encounter a knowledge aggregation problem, requiring them to organise themselves, without a centralised authority, as a distributed information processing unit (DIP). In this article, we specify and implement a new algorithm for knowledge aggregation based on Nowak's psychological theory Regulatory Theory of Social Influence (RTSI). This theory posits that social influence consists of not only sources trying to influence targets, but also targets seeking sources by whom to be influenced and learning what processing rules those sources are using. A multi-agent simulator SMARTSIS is implemented to evaluate the algorithm, using as its base scenario a linear public goods game where the DIP's decision is a qualitative question of distributive justice. In a series of experiments examining the emergence of expertise, we show how RTSI enhances the effectiveness of the multi-agent DIP as a social group while conserving each agent's individual resources. Additionally, we identify eight criteria for evaluating the DIP unit's performance, consisting of four conflicting pairs of systemic drivers, and discuss how RTSI maintains a balanced tension between the four driver pairs through the emergence and divergence of expertise. We conclude by arguing that this shows how psychological theories like RTSI can have a crucial role in informing agent-based models of human behaviour, which in turn may be critically important for effective knowledge management and reflective self-improvement in both cyber-physical and socio-technical systems.

In the current study, we examined the consequences of adding prosodic cues to text communication by allowing users to observe the process of message creation while engaged in dyadic conversations. In the first condition, users interacted through a traditional chat that requires pressing “enter” to make a message visible to an interlocutor. In another, text appeared on the screen simultaneously as the sender was writing it, letter after letter (Synchat condition), so that users could observe the varying rhythm of message production, precise timing of message appearance, typos, and their corrections. The results show that the ability to observe the dynamics of message production had a twofold effect on the social interaction process. First, it enhanced the relational aspect of communication – interlocutors synchronized their emotional states during the interaction, their communication included more statements on relationship building, and they evaluated the Synchat medium as more personal and emotionally engaging. Second, it increased the coherence of communication, reflected in greater continuity of the topics raised in Synchat conversations. The results are discussed from the interaction design (IxD) perspective.

Dynamic instruction mixes form an important part of the toolkits of performance tuners, compiler writers, and CPU architects. Instruction mixes are traditionally generated using software instrumentation, an accurate yet slow method, that is normally limited to user-mode code. We present a new method for generating instruction mixes using the Performance Monitoring Unit (PMU) of the CPU. It has very low overhead, extends coverage to kernel-mode execution, and causes only a very modest decrease in accuracy, compared to software instrumentation. In order to achieve this level of accuracy, we develop a new PMU-based data collection method, Hybrid Basic Block Profiling (HBBP). HBBP uses simple machine learning techniques to choose, on a per basic block basis, between data from two conventional sampling methods, Event Based Sampling (EBS) and Last Branch Records (LBR). We implement a profiling tool based on HBBP, and we report on experiments with the industry standard SPEC CPU2006 suite, as well as with two large-scale scientific codes. We observe an improvement in runtime compared to software instrumentation of up to 76x on the tested benchmarks, reducing wait times from hours to minutes. Instruction attribution errors average 2.1%. The results indicate that HBBP provides a favorable tradeoff between accuracy and speed, making it a suitable candidate for use in production environments.

To address the growing difficulty of performance debugging on modern processors with increasingly complex micro-architectures, we present Hierarchical Cycle Accounting (HCA), a structured, hierarchical, architecture-agnostic methodology for the identification of performance issues in workloads running on these modern processors. HCA reports to the user the cost of a number of execution components, such as load latency, memory bandwidth, instruction starvation, and branch misprediction. A critical novel feature of HCA is that all cost components are presented in the same unit, core pipeline cycles. Their relative importance can therefore be compared directly. These cost components are furthermore presented in a hierarchical fashion, with architecture-agnostic components at the top levels of the hierarchy and architecture-specific components at the bottom. This hierarchical structure is useful in guiding the performance debugging effort to the places where it can be the most effective. For a given architecture, the cost components are computed based on the observation of architecture-specific events, typically provided by a performance monitoring unit (PMU), and using a set of formulas to attribute a certain cost in cycles to each event. The selection of what PMU events to use, their validation, and the derivation of the formulas are done offline by an architecture expert, thereby freeing the non-expert from the burdensome and error-prone task of directly interpreting PMU data. We have implemented the HCA methodology in Gooda, a publicly available tool. We describe the application of Gooda to the analysis of several workloads in wide use, showing how HCA's features facilitated performance debugging for these applications. We also describe the discovery of relevant bugs in Intel hardware and the Linux Kernel as a result of using HCA.

We modify neural networks models of the Hopfield type so that they can recognize the degree of novelty of the input stimuli on a local level. The networks control themselves the quality of recognition and can also recognize locally the bits of information in the input patterns which do not agree with known patterns, i.e. stored memories. This task is achieved by introducing local variations of the noise level beta in the network. Noise level in a given location depends on the flip frequency of the neurons close to that location.