Information Processing Requirements Research Articles

Strain-mediated reservoir computing with temporal and spatial co-multiplexing in multiferroic heterostructures

Physical reservoir computing (PRC), a brain-inspired computing method known for its efficient information processing and low training requirements, has attracted significant attention. The key factor lies in the number of computational nodes within the reservoir for its computational capability. Here, we explore co-multiplexing reservoirs that leverage both temporal and spatial strategies. Temporal multiplexing virtually expands the node count through the use of masking techniques, while spatial multiplexing utilizes multiple physical locations (e.g., Hall bars) to achieve an increase in the number of real nodes. Our experiment employs a strain-mediated reservoir based on multiferroic heterostructures. By applying a single voltage across the PMN-PT substrate (acting as global input) and measuring the output Hall voltages from four Hall bars (real nodes), we achieve significant efficiency gains. This co-multiplexing approach results in a reduction in the normalized root mean square error from 0.5 to 0.23 for a 20-step prediction task of a Mackey–Glass chaotic time series. Furthermore, the single input and four independent outputs lead to a fourfold reduction in energy consumption compared to the strain-mediated PRC with temporal multiplexing solely. This research paves the way for future energy saving PRC implementations utilizing co-multiplexing, promoting a resource-efficient paradigm in reservoir computing.

An Examination of Cyber Security Solutions in Public and Private IaaS Infrastructures

The digital transformation is a dynamic process that unfolds within the data and information cycle, aimed at maximizing the efficiency businesses derive from data and information. For the structuring and sustenance of this process require information processing resources, typically sourced from cloud computing infrastructures. In this context, the aim of this study is to scrutinize the cyber security measures provided by both public and private IaaS infrastructures, aiding businesses in their selection process for digital transformation. To address the objectives of this research, a mixed-methods approach was adopted, integrating both qualitative and quantitative research techniques. The study is structured around two primary research questions. The first research question (RQ1) seeks to delineate the cyber security measures in Amazon AWS EC2, Google Cloud CE, and Proxmox VE IaaS (Internet as a Service) infrastructures. The second research question (RQ2) aims to identify the similarities and differences in cyber security measures across these infrastructures. Additionally, to verify the findings derived from (RQ1) ensure the credibility of the study, and to experimentally examine cyber security measures within these infrastructures, the study adopts an experimental research method from quantitative analysis techniques and the hypothesis (H0) " The findings obtained as a result of RQ1 are confirmed in AWS EC2 and Google Cloud CE IaaS infrastructures" is tested. As a result of the experimental research, hypothesis H0 was accepted. A review of existing literature, there has been encountered no research that concurrently examines, compares, and experimentally verifies the cybersecurity measures across both public and private IaaS infrastructures. Therefore, this study can be considered to make an original contribution to the existing body of knowledge by addressing an important gap in the literature on the comparative and experimental evaluation of cyber security practices in public and private IaaS infrastructures.

Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a three-dimensional Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs converge on structural and functional features that are also commonly found in primate cerebral cortices. Specifically, they converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically efficient mixed-selective code. Because these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs incorporate biophysical constraints within a fully artificial system and can serve as a bridge between structural and functional research communities to move neuroscientific understanding forwards.

Error per single-qubit gate below 10−4 in a superconducting qubit

Implementing arbitrary single-qubit gates with near perfect fidelity is among the most fundamental requirements in gate-based quantum information processing. In this work, we fabricate a transmon qubit with long coherence times and demonstrate single-qubit gates with the average gate error below 10−4, i.e. (7.42 ± 0.04) × 10−5 by randomized benchmarking (RB). To understand the error sources, we experimentally obtain an error budget, consisting of the decoherence errors lower bounded by (4.62 ± 0.04) × 10−5 and the leakage rate per gate of (1.16 ± 0.04) × 10−5. Moreover, we reconstruct the process matrices for the single-qubit gates by the gate set tomography (GST), with which we simulate RB sequences and obtain single-qubit fidelities consistent with experimental results. We also observe non-Markovian behavior in the experiment of long-sequence GST, which may provide guidance for further calibration. The demonstration extends the upper limit that the average fidelity of single-qubit gates can reach in a transmon-qubit system, and thus can be an essential step towards practical and reliable quantum computation in the near future.

GUIDELINES FOR COMPUTATIONAL MODELING OF FRIENDSHIP

AbstractHumans participate in an immense variety of relationships with other persons and other entities: human and nonhuman, living and nonliving, tangible and intangible, real and imagined. Participation in relationships is considered a key benchmark of personhood. Some of these relationships, particularly friendships, involve close emotional attachments, and some friendships have been described since antiquity as spiritual in nature. Different types of friendship depend upon factors such as proximity, social formality, physical intimacy, information exchanged, and the costs and benefits of maintaining the relationship. There are time‐extended processes and narrative practices involved in forming and dissolving relationships. A question is raised how androids (hypothetical humanoid robots that people would accept as equals in society) can participate in friendships with humans and other entities. This article explores the space of friendships with the aim of formulating guidelines for a computational model that can make explicit the information processing requirements and step‐by‐step processes involved with participating in the many different types of friendships, including those known as spiritual friendships.

The importance of theory at the Information Systems Journal

The importance of theory at the <i>Information Systems Journal</i>

Bright semiconductor single-photon sources pumped by heterogeneously integrated micropillar lasers with electrical injections

The emerging hybrid integrated quantum photonics combines the advantages of different functional components into a single chip to meet the stringent requirements for quantum information processing. Despite the tremendous progress in hybrid integrations of III-V quantum emitters with silicon-based photonic circuits and superconducting single-photon detectors, on-chip optical excitations of quantum emitters via miniaturized lasers towards single-photon sources (SPSs) with low power consumptions, small device footprints, and excellent coherence properties is highly desirable yet illusive. In this work, we present realizations of bright semiconductor SPSs heterogeneously integrated with on-chip electrically-injected microlasers. Different from previous one-by-one transfer printing technique implemented in hybrid quantum dot (QD) photonic devices, multiple deterministically coupled QD-circular Bragg Grating (CBG) SPSs were integrated with electrically-injected micropillar lasers at one time via a potentially scalable transfer printing process assisted by the wide-field photoluminescence (PL) imaging technique. Optically pumped by electrically-injected microlasers, pure single photons are generated with a high-brightness of a count rate of 3.8 M/s and an extraction efficiency of 25.44%. Such a high-brightness is due to the enhancement by the cavity mode of the CBG, which is confirmed by a Purcell factor of 2.5. Our work provides a powerful tool for advancing hybrid integrated quantum photonics in general and boosts the developments for realizing highly-compact, energy-efficient and coherent SPSs in particular.

Navigating digital transformation through an information quality strategy: Evidence from a military organisation

AbstractThe use of digital technologies for extracting information from various data sources can help organisations to reduce uncertainty and improve decision‐making. The increasing availability in volume, velocity, and variety of data, however, can give rise to significant risks and challenges in ensuring a high level of information quality (IQ). Pre‐digital organisations can be particularly susceptive to such challenges due to their limited experience with digital technologies and IQ governance. We adopt a theory‐infused interventionist research approach to assist a pre‐digital multinational military organisation in navigating its digital transformation (DT) by focusing on IQ. We design and implement an IQ strategy (IQS) by drawing upon organisational information processing theory and examining how the level of IQ can affect the balance between information processing requirements and capacity. We demonstrate that an IQS that incorporates both technological, as well as IQ governance solutions, can support organisations in setting the scope of their DT, decreasing employees' resistance to change, and increasing their satisfaction, while concurrently improving organisational efficiency. Our work stresses the importance of IQ in the digital era and delineates how pre‐digital organisations can navigate DT by strategically addressing IQ.

An empirical analysis of vulnerability information disclosure impact on patch R&amp;D of software vendors

The vulnerability patch R&amp;D has become an important part of information security governance. An effective collaboration with software vendors in patch R&amp;D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&amp;D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China’s national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&amp;D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&amp;D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&amp;D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&amp;D.

The magnetization reversal driven by spin-orbit-assisted spin-transfer torque

As the data writing scheme of magnetization reversal driven by spin-transfer torque can overcome the shortcomings of traditional magnetic-field writing mechanism, it has become a mainstream way of implementing information writing in magnetic random access memory. However, the explosive growth of information shows higher requirements for data storage and information processing, thus magnetic random access memories based on spin-transfer torque data writing method pose several issues, including barrier reliability and limited storage speed. Recent experimental studies have shown that the spin-orbit torque through the spin Hall effect or Rashba effect in heavy-metal/ferromagnetic bilayer structures has the potential advantages in overcoming these limitations. They can also be used to drive magnetization to achieve rapid reversal. Especially, the three-terminal magnetic tunnel junction separates data reading from writing current. It has the advantages of faster writing speed and better stability and thus becomes the most promising magnetic storage technique at present. The magnetization reversal driven by spin-orbit-assisted spin-transfer torque in a three-terminal magnetic tunnel junction is studied theoretically in this work. By linearizing the Landau-Lifshitz-Gilbert equation with the additional spin-transfer torque term and spin-orbit torque term in the spherical coordinates, two coupled differential equations and the new equilibrium directions are obtained. With the stability analysis of the new equilibrium directions, the phase diagrams defined in parameter space spanned by the current densities of spin-transfer and spin-orbit torques are established. There are several magnetic states in the phase diagrams, including quasi-parallel stable states, quasi-antiparallel stable states, and bistable states. By adjusting the current density of the spin-transfer torque, the magnetization reversal between two stable states is realized. It is found that the magnetization reversal time is greatly reduced with the assisting of spin-orbit torque, and it decreases with the augment of current density of spin-orbit torque. Meanwhile, the zero-field magnetization reversal can be realized through the interplay between spin-orbit torque and spin-transfer torque. In addition, compared with the damping-like term of spin-orbit torque, the field-like one plays a leading role in magnetization reversal. The presence of field-like term of spin-orbit torque can also reduce the reversal time that decreases with the increase of the ratio of field-like torque to damping-like one.

Mode-dispersion phase matching single photon source based on thin-film lithium niobate

In the domain of integrated quantum photonics, the burgeoning superiority of lithium niobate’s second-order nonlinearity in electro-optic modulation makes thin-film lithium niobate a leading quantum photonic platform after silicon. To date, single-photon sources using thin-film lithium niobate has mainly adopted periodic polarization quasi-phase matching technology, which requires the preparation of complex electrodes for domain inversion in the waveguide to realize quasi-phase matching. This method inevitably introduces complexity, such as complex processing methods, enlarged polarization regions, and compromised integration density. With the development of quantum information technology, the ever-increasing degree of integration constantly creates new demands. Consequently, the development of a streamlined, high-efficiency quantum light source on a lithium niobate platform is a pressing issue. In this study, we propose a novel thin-film lithium niobate parametric down-conversion single-photon source based on mode dispersion phase matching theory. The strategy is different from conventional strategies that utilize periodic polarization to generate single-photon sources in thin-film lithium niobate devices. In contrast to traditional quasi-phase matching techniques that utilize the phase matching between pump fundamental mode light and parametric fundamental mode light, our method employs the phase matching between the pump light’s higher-order mode and the parametric light’s fundamental mode. The pump light’s higher-order mode is obtained by designing an asymmetric directional coupler. The device’s single-photon yield can attain &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$3.8\times10^{7}$\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20230743_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="15-20230743_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;/(s·mW), satisfying the requirements for optical quantum information processing. This innovative solution is expected to replace the traditional quasi-phase-matching single-photon sources, thus further promoting the study of optical quantum information based on thin-film lithium niobate chips.

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems.

Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars; thus, digital twins' radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars.

How is mobile task performance different? The case of information processing without information search

ABSTRACT Studies have repeatedly highlighted the inferior performance of mobile users relative to that of personal computer (PC) users, largely due to lower usability and higher search costs arising from the use of smaller screens. Research, however, has yet to substantively address the situation in which mobile users perform tasks that require information processing without information search, implying an absence of device-related performance differences in such situations. Against this background, we propose that mobile task performance may be inferior in cognitive tasks, even when usability and search costs cannot be used as explanatory mechanisms, due to the predisposition of users to process information differently on different devices. We provide evidence in support of this proposition in two experiments. We find that mobile users perform worse (less accurately) than PC users do when the tasks demand high cognitive load. By contrast, when the tasks demand low cognitive load, performance is comparable across devices. We observe this interaction effect for different types of cognitive load – intrinsic and extraneous – despite their opposite effects on task performance. In so doing, we extend existing explanations of mobile task performance and shed light on the boundary conditions under which mobile use negatively affects task performance.

Processing of information from networks of airspace surveillance radar systems

The presented paper examines the principles and methods for processing of information from airspace surveillance radar networks. Information technologies allow implementing automatic collection, processing, storage, transmission and issuance of radar information to users. In this work, the synthesis and analysis of the optimal structure of the interstage processing of signal data and radar information of primary processing in the network of airspace surveillance radar systems is carried out. The quality of information from radar networks of airspace surveillance systems affects almost all indicators of the quality of the radar systems network functioning. The stages of radar information processing in radar systems networks are analyzed. The importance of specifying the above stages for creating a complete picture of the air situation in the area of responsibility is shown. To improve the quality of information support for consumers, a network of radar systems requires information processing at all stages. At each stage of information processing, information processing quality indicators were analyzed. This made it possible to show that the staged implementation of information processing, on the one hand, simplified the optimization of processing within each processing stage, but on the other hand, it made it difficult to carry out compatible optimization of both the detection of an aerial object and the measurement of the coordinates of an aerial object. The synthesized structure of processing radar information of the network of radar systems of airspace surveillance, which in its turn made it possible to carry out interstage optimization of processing of both signal data and primary processing information. Calculations have shown that the method of information processing, in which the combination of information is carried out at the level of decision-making on the detection of airborne objects in each signal data processing channel, has some advantages in the quality of information processing of the network of radar systems compared to the one currently used. time option combination information at the stage of signal processing. At the same time, for the method of combining information at the level of decision-making about the detection of aerial objects, the flow of transmitted information to the joint processing point is significantly reduced. All this allows improving the quality of information processing in the airspace control system.

Design of an embedded machine vision system for smart cameras

Abstract With the rapid increase in computer users’ requirements for image information and image processing, and the rapid development of the intelligent process, the ability of the traditional visual system to process image information and data has been difficult to meet the needs of users. Therefore, in this article, we upgrade the vision system of smart cameras by introducing three network algorithm structures: convolutional neural network (CNN), LSTM and CNN-LSTM. We compare the classification performance of the three algorithms and evaluate them with three metrics: accuracy, precision and recall. The experimental results show that using the CNN algorithm, the accuracy of image information processing is 98.2%, the precision can reach 87.5% and the recall rate is 99.8%; the LSTM accuracy is 97.7%, its precision is 89.6% and its recall rate is 87.3%; its precision can be improved to 90.5% and the recall rate to 99.7%.

How do platforms improve social capital within sharing economy-based service triads: an information processing perspective

This study examines how platforms within sharing economy-based service triads (SESTs) can improve their social capital through information processing and knowledge management mechanisms, with the objective to provide better platform services to both service suppliers and end customers. Data were collected through multiple case studies, primarily relying on insight gathered from 32 semi-structured interviews with managers from four sharing economy platform companies in the Chinese ride-sharing (DiDi and Uber China) and logistics-sharing (Huo-che-bang and Yun-man-man) industries. Through our analysis we uncover uncertainties faced by sharing economy platforms in SETSs (including task complexity, over-competition, and task coordination), delineating their information processing requirements. Based on our findings we suggest that these requirements can be addressed by enhanced intra-organizational structures that would contribute to the SESTs’ information processing capacity. Accomplishing this fit between a platform’s information processing requirements and associated information processing capacity carries great promise to not only affect the platform’s knowledge management capability (PKMC) but also the social capital created within SESTs.

How Does Working Memory Capacity Affect Students’ Mathematical Problem Solving?

&lt;p style="text-align:justify"&gt;Problem-solving process requires information processing, and the information processing is related to working memory capacity (WMC). This study aims to determine the effect of WMC on students' mathematical abilities and to describe the ability of the students with high and low WMC in solving mathematical problems. This research used mixed method with Sequential Explanatory Design. The quantitative data were collected through the provision of OSPAN tasks and math tests to 58 students aged 15-17 years, while the qualitative data were collected through interviews based on mathematical problem-solving tasks. The results showed that WMC had a significant effect on students' mathematical abilities (R=0.536; p=0.000). Researchers found differences in students' mathematical problem-solving abilities with high and low WMC. Students with high WMC can remember and manage information well which supports the determination of more advanced problem-solving strategies and have better attention control so that they find varied appropriate solutions. Students with low WMC experienced decreased attention control as the complexity of the tasks increased, missed important information in problem solving strategies, and did not recheck their work, leading to wrong solution/answer. The mathematical performance of students with high WMC outperformed the mathematical performance of students with low WMC.&lt;/p&gt;

Influence of the Athlete’s Training Physical State Test Based on the Principle of Artificial Intelligence Sensor

Artificial intelligence is the study of the laws of human intelligent activities, the construction of artificial systems with certain intelligence, and the study of how to make computers perform tasks that required human intelligence in the past. Basic Theories, Methods, and Techniques. With the maturity of the highly integrated hardware technology, the rapid development of sensor technology provides a material basis for the study of sports states based on smart terminals, and the mature model method theory provides a theoretical research basis for the research. The working principle of the sensor is to convert the specific measured signal into a certain “available signal” according to a certain rule through the sensitive element and the conversion element and output it to meet the requirements of information transmission, processing, recording, display, and control. In order to deeply study the application of artificial intelligence sensor theory in athlete training physical condition testing, this article uses theoretical analysis method, formula image combination method, and real person survey method, collects samples, analyzes artificial intelligence sensor, and streamlines the algorithm. In studying the accuracy of smart sensors on athlete training tests, a total of 9 athletes, 4 females, and 5 males are selected. Each experimenter performed three actions of running, jumping, and squatting 10 times, with an interval of more than 20 s. The results showed that the recognition accuracy of running in this paper was 98.51%, and the recognition accuracy of jumping and squatting was 92.59% and 93.33%; we have achieved more than 92% recognition rate for the three kinds of actions. On further study of the real-time performance of the sensor, the average response time of the algorithm is the average value obtained from 80 experimental records in this paper. The average response time of the algorithm proposed in this paper is within 1.5 s. Since the falling process occurs within 2.1 s, the recognition algorithm proposed in this paper has high real-time performance. It is basically realized that starting from the theory of artificial intelligence sensors, a high-precision sensor that can be applied to athlete training is designed.

Quantifying incompatibility of quantum measurements through non-commutativity

The existence of incompatible measurements, i.e. measurements which cannot be performed simultaneously on a single copy of a quantum state, constitutes an important distinction between quantum mechanics and classical theories. While incompatibility might at first glance seem like an obstacle, it turns to be a necessary ingredient to achieve the so-called quantum advantage in various operational tasks like random access codes or key distribution. To improve our understanding of how to quantify incompatibility of quantum measurements, we define and explore a family of incompatibility measures based on non-commutativity. We investigate some basic properties of these measures, we show that they satisfy some natural information-processing requirements and we fully characterize the pairs which achieve the highest incompatibility (in a fixed dimension). We also consider the behavior of our measures under different types of compositions. Finally, to link our new measures to existing results, we relate them to a robustness-based incompatibility measure and two operational scenarios: random access codes and entropic uncertainty relations.

Unique contributions of perceptual and conceptual humanness to object representations in the human brain

The human brain is able to quickly and accurately identify objects in a dynamic visual world. Objects evoke different patterns of neural activity in the visual system, which reflect object category memberships. However, the underlying dimensions of object representations in the brain remain unclear. Recent research suggests that objects similarity to humans is one of the main dimensions used by the brain to organise objects, but the nature of the human-similarity features driving this organisation are still unknown. Here, we investigate the relative contributions of perceptual and conceptual features of humanness to the representational organisation of objects in the human visual system. We collected behavioural judgements of human-similarity of various objects, which were compared with time-resolved neuroimaging responses to the same objects. The behavioural judgement tasks targeted either perceptual or conceptual humanness features to determine their respective contribution to perceived human-similarity. Behavioural and neuroimaging data revealed significant and unique contributions of both perceptual and conceptual features of humanness, each explaining unique variance in neuroimaging data. Furthermore, our results showed distinct spatio-temporal dynamics in the processing of conceptual and perceptual humanness features, with later and more lateralised brain responses to conceptual features. This study highlights the critical importance of social requirements in information processing and organisation in the human brain.