General-purpose System Research Articles

Scalable Metadata-Hiding for Privacy-Preserving IoT Systems

Modern cloud-based IoT services comprise an integrator service and several device vendor services. The vendor services enable users to remotely control their devices, while the integrator serves as a central intermediary, offering a unified interface for managing devices from different vendors. Although such a model is quite beneficial for IoT services to evolve quickly, it also creates a serious privacy concern: the vendor and integrator services observe all interactions between users and devices. Toward this, we propose Mohito, a privacy-preserving IoT system that hides such interactions from both the integrator and the vendors. In Mohito, we protect both the interaction data and the metadata, so that no one learns which user is communicating with which device. By utilizing oblivious key-value storage as a primitive and leveraging the unique communication graph of IoT services, we build a scalable protocol specialized in handling large concurrent traffic, a common demand in IoT systems. Our evaluation shows that Mohito can achieve up to 600x more throughput than the state-of-the-art general-purpose systems that provide similar security guarantees.

The Canadian Artificial Intelligence and Data Act and the EU AI Act: Will Sanity Prevail as they more closely align? – Part 2 — Changes to both Acts bring them closer together... but not too close

Abstract Part 1 of this paper (Beardwood, CRi 2024, 97) provided an update on the progress of AIDA and the EU AI Act (I), outlined a summary roadmap of the base similarities and differences between the two items of legislation (II), reviewed the objectives of AIDA in contrast to the EU AI Act (III), compared their respective jurisdictional scope (IV), reviewed their respective definitions of AI systems (V), outlined new definitions/concepts which have been introduced into the legislation (VI), outlined the extent to which there are exclusions for the public sector (VII) and for research (VIII), and provided an overview of their respective risk-based approaches (IX). This Part 2 compares in detail the obligations for High-Impact Systems and General-Purpose Systems (AIDA) (X), and for High-Risk AI Systems and General-Purpose AI Systems (EU AI Act) (XI), and finally reviews the penalties and offences for noncompliance imposed by AIDA and EU AI Act (XII) before concluding (XIII).

The Unified Theory of Acceptance and Use of Technology (UTAUT) Used on Mobile Application: Literature Review

This paper aims to perform a systematic review of articles that have used the unified theory of acceptance and use of technology (UTAUT) from 2019 to 2023. The results in this research are based on the literature analysis of 10 existing articles on the UTAUT model. This has been performed by collecting data including demographic details, methodological details, limitations, and significance of relationships between constructs from the available articles based on the UTAUT. The findings indicated that general-purpose systems and specialized business systems were examined in the majority of the articles using the UTAUT. The analysis also indicated that the cross-sectional approach, survey methods, and structural equation modeling analysis techniques were the most explored research methodologies whereas Smart PLS was found to be the largely used analysis tool. This is the continued research that examined the literature on the UTAUT for the past five years and provided the researchers with accumulative knowledge about the model.

System Auditing for Real-Time Systems

System auditing is an essential tool for detecting malicious events and conducting forensic analysis. Although used extensively on general-purpose systems, auditing frameworks have not been designed with consideration for the unique constraints and properties of Real-Time Systems (RTS). System auditing could provide tremendous benefits for security-critical RTS. However, a naive deployment of auditing on RTS could violate the temporal requirements of the system while also rendering auditing incomplete and ineffectual. To ensure effective auditing that meets the computational needs of recording complete audit information while adhering to the temporal requirements of the RTS, it is essential to carefully integrate auditing into the real-time (RT) schedule. This work adapts the Linux Audit framework for use in RT Linux by leveraging the common properties of such systems, such as special purpose and predictability. Ellipsis , an efficient system for auditing RTS, is devised that learns the expected benign behaviors of the system and generates succinct descriptions of the expected activity. Evaluations using varied RT applications show that Ellipsis reduces the volume of audit records generated during benign activity by up to 97.55% while recording detailed logs for suspicious activities. Empirical analyses establish that the auditing infrastructure adheres to the properties of predictability and isolation that are important to RTS. Furthermore, the schedulability of RT tasksets under audit is comprehensively analyzed to enable the safe integration of auditing in RT task schedules.

Performance evaluation of resolution and sensitivity of C-SPECT's variable slat-stack collimator.

Myocardial perfusion imaging is commonly performed using SPECT, where both general-purpose and dedicated scanners are available. A limitation with general-purpose systems has been the inability to image dynamically since different projections are obtained far apart in time due to scanner rotation. Dedicated systems can have this capability since they acquire completely sampled projections (i.e., those with enough angular views for reconstruction) with short time frames. C-SPECT, does not need any scanner or patient motion to obtain complete projections, allowing fast dynamics. When imaging fast dynamics, the optimal collimator settings are not necessarily the same as for static imaging, where longer acquisitions can be utilized. Thus, C-SPECT offers adaptive collimation in the transverse and axial directions. The performance of adaptation in the axial direction was characterized herein. The ratio of the resolution metric in high-sensitivity mode to that in the high-resolution mode, termed resolution boost factor, was determined. Analogously, the sensitivity boost factor was also determined. Comparisons were made with theory and simulations. The boost factors for resolution and sensitivity, averaged over the 14 modules of the system, were determined to be 1.72 and 1.75, respectively. The boost factors, which ideally would be two, were between 10% and 15% below optimal values and tracked each other, suggesting mechanical challenges in the apparatus, such as incomplete closure of adjacent slats, but show reasonably successful adaptation between modes.

Ultrasonic vibration measurement using heterodyne interferometry and SDR phase meter

Ultrasonic vibration measurement and the development of ultrasonic vibration sensors have a significant impact on underwater pattern detection, providing good support for the exploitation and usage of marine resources. Advanced digital signal processing algorithms improve mechanical displacement measurements using ultrasonic speed and pm-level interferometers in real-time. In recent years, developing digital algorithms and employing low-cost software-defined radio (SDR) software applied to communication systems and other general-purpose systems with flexible solutions are essential in modern industrial applications. Specifically, the SDR can be consistent with real-time phase-change measurements of MHz-frequency interference signals for a heterodyne interferometer. This paper combines a heterodyne interferometer and a real-time SDR phase meter, demonstrating an ultrasonic vibration instrument's high-speed vibrating measurement capabilities. A double-pass interferometer is implemented to produce interference signals modulated with a sine waveform phase change associated with a tool's ultrasonic vibrating displacement, calculated by the phase meter's quadrature demodulation algorithm. The measurement results show that the system detects the sine-wave vibration trajectory generated by the vibrator at a frequency of 20 kHz and an amplitude of ~460 nm. The principle of the measurement system, instrumentations, experiments, and results are discussed in the paper.

An ultra-low power fully-programmable analog general purpose type-2 fuzzy inference system

Neuro-fuzzy systems is a soft computing technique for developing intelligent systems that solve complex real-world problems in a way that closely resembles human reasoning. In this work, a novel ultra-low power pure analog integrated type-2 fuzzy inference system architecture is proposed. The system consists of Gaussian membership function and MIN/MAX operator circuits and a center of gravity defuzzification block. The architecture is easily scalable in the design-level, regarding various system’s hyperparameters, resulting in a general-purpose system that can be modified to suit a wide variety of applications. Moreover, the system is fully programmable and electronically tunable even post-fabrication. Finally, a novel fuzzy classifier is implemented in a 90nm CMOS process using the Cadence IC Suite for the electrical and physical design utilizing the proposed architecture. The classifier is evaluated on the breast cancer Wisconsin (original) dataset and is compared with the equivalent software model and other analog classifiers. Post-layout simulation results confirm the proper operation of the design while demonstrating the optimal combination of ultra-low power and high accuracy performance relative to the other analog classifiers.

An intelligent machine learning model for real-time early detection of undesirable cancer events: AIM2REDUCE.

1557 Background: Cancer and its treatment cause undesirable cancer events (UCEs). Automated warning systems could reduce the frequency and severity of UCEs by alerting the healthcare team and allocating preventative interventions. Most previous studies predicted single UCEs at the initiation of treatment. In AIM2REDUCE, we developed and evaluated a general-purpose system for predicting UCEs during outpatient systemic anti-cancer therapy. Methods: Each time a patient receives treatment, AIM2REDUCE applies machine learning to the preceding data in the electronic medical record (EMR) to predict future UCEs. We identified patients treated for aerodigestive cancers from the EMR at Princess Margaret Cancer Centre, who were randomly split into development, validation, and test cohorts. Features included cancer diagnosis, treatment sessions with doses, laboratory tests, and patient-reported symptoms. UCEs are listed in the Table. We trained LASSO regression and random forests models in the training cohort and tuned hyperparameters in the validation cohort using Bayesian optimization. We evaluated performance across discrimination, calibration, and net benefit in the test cohort. Results: The cohort included 5,760 patients who received 175,565 treatment sessions, with 13,612,746 unique data points across 102 features. Of these patients, 2,352 (40.8%) were female, the median age was 64.0 years (interquartile range 14.0), the most common diagnoses were lung cancer (2,071, 36.0%) and pancreatic cancer (926, 16.1%), and the most common treatment regimens were weekly gemcitabine (433, 7.5%) and maintenance pemetrexed (417, 7.2%). The Table shows the performance of AIM2REDUCE. Conclusions: We demonstrate that longitudinal machine learning systems trained using EMR data can accurately predict a wide range of UCEs. Based on these results, automated warning systems should be implemented and evaluated in real-time clinical practice. [Table: see text]

Identification of modal parameters of a model turbine blade with a curved surface under random excitation with a three-dimensional continuously scanning laser Doppler vibrometer system

Modal parameters of a model turbine blade with a curved surface excited by white noise are identified by a novel general-purpose 3D continuously scanning laser Doppler vibrometer (CSLDV) system via an extended demodulation method (EDM). The proposed general-purpose system is calibrated to synchronously and continuously move three laser spots on the curved surface of the blade along the same scan trajectory and capture its 3D vibrations. The EDM is used to estimate its damped natural frequencies (DNFs) and 3D full-field undamped mode shapes. Identified modal parameters are compared with those from a commercial 3D scanning laser Doppler vibrometer (SLDV) system. Differences between their first six DNFs are less than 1.5%; their first six mode shapes are highly correlated as their modal assurance criterion values all exceed 95%. However, the 3D CSLDV system has much higher efficiency in obtaining 3D mode shapes of the blade than the 3D SLDV system.

Problem] Cerberus: Minimalistic Multi-shard Byzantine-resilient Transaction Processing

To enable scalable resilient blockchain systems, several powerful general-purpose approaches toward sharding such systems have been demonstrated. Unfortunately, these approaches all come with substantial costs for ordering andexecution of multi-shard transactions.In this work, we ask whether one can achieve significantcost reductions for processing multi-shard transactions by limiting the type of workloads supported. To initiate the study of this problem, we propose CERBERUS, a family of minimalistic primitives for processing single-shard and multi-shard UTXO-like transactions. The first CERBERUS variant we propose is core-CERBERUS (CCERBERUS). CCERBERUS uses strict UTXO-based environmental requirements to enable powerful multi-shard transaction processing with an absolute minimum amount of coordination between shards. In the environment we designed CCERBERUS for, CCERBERUS will operate perfectly with respect to all transactions proposed and approved by well-behaved clients, but does not provide any other guarantees.To illustrate that CCERBERUS -like protocols can also be of use in environments with faulty clients, we also demonstrate two generalizations of CCERBERUS, optimistic-CERBERUS and resilient-CERBERUS, that make different tradeoffs in complexity and costs when dealing with faulty behavior and attacks. Finally, we compare these three protocols and show their potential scalability and performance benefits over state-of-the-art general-purpose systems. These results underline the importance of the study of specialized approaches toward sharding in resilient systems.

DIRAC at JINR as a general purpose system for massive computations

Joint Institute for Nuclear Research (JINR) has several large computing facilities: Tier1 and Tier2 grid clusters, Govorun supercomputer, cloud, and LHEP computing cluster. Each of them has different access protocols, authentication and authorization procedures, data access methods. With the help of the DIRAC Interware, we were able to integrate all these resources to provide a uniform access to all these facilities. Right now, it is possible to perform basic workflows on all resources. The main use-cases covered by the DIRAC service in JINR: centralized Monte-Carlo simulation for the MPD experiment, Monte-Carlo for the Baikal-GVD neutrino telescope, as well as running jobs for the Folding@HOME project.During the pre-production stage, it is important to estimate the characteristics of user jobs. That information is crucial for planning of the execution process on different resources. An approach was elaborated to collect data about RAM, CPU, and network consumption by each user job. This helped to tune the mass production algorithms before the production actually starts. Since the system processes tens of thousands of similar jobs during one particular production, it appeared to be possible to collect from DIRAC data about these jobs and perform analysis of their execution parameters. We collect data related to the CPU model, wall-time, CPU benchmark DB12, hostname, username, and resource name. An approach was elaborated to extract meta-data about job execution and visualize it. With this visualization, it became possible to compare different computing resources, study the CPU and worker node performance. This approach does not require submitting special jobs, so the resources are not wasted for this analysis.

Generalized Secure and Dynamic Decentralized Reputation System With a Dishonest Majority

Reputation management systems are essential for establishing trust among network users. They are tools for reinforcing cooperation and sanctioning malicious behavior. This importance becomes a requirement in decentralized environments such as mobile ad-hoc networks (MANETs), peer-to-peer systems (P2P), wireless sensor networks (WSNs), or decentralized social networks (DSNs) where there is no trusted third party to monitor and enforce good behavior among users. In this paper, we propose a dynamic decentralized reputation system that fits such network characteristics, namely decentralization, dynamism, and openness, without conceding on security. The novel system is a general-purpose system that uses blockchain to gather and supply global reputation information while remaining fully decentralized. Unlike previous works on decentralized reputation systems where reputation information is inconsistent and limited to users’ direct experience and recommendations from peers (neighbors), our system gathers feedback from all over the network and stores reputation information on a distributed ledger fully accessible to all users. In terms of security, the proposed method achieves privacy utilizing secure multiparty computation, a cryptographic primitive that preserves feedback privacy even with a dishonest majority reaching <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n-2$ </tex-math></inline-formula> malicious parties while requiring only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(n)$ </tex-math></inline-formula> messages. The employed techniques enable the system to achieve unique characteristics like consistency, conservation, and verifiability in addition to privacy. The security analysis we provide confirms these properties, and the performed simulation shows the protocol’s effectiveness.

Programming Language Conversion Using NLP

Natural language processing strives to build machines that understand and respond to text or voice data—and respond with text or speech of their own—in much the same way humans do. NLP models language computationally and deals with linguistic features of computation. Once a computer learns to do mathematical calculations it can perform many complex and big calculations much faster than humans. Similarly once computer starts to understand the human languages it can process all aspects of that language much faster than humans also opening a large number of possibility. So it cuts down on employment as one computer is capable of giving an output 10 times faster than a human can. So it benefits the employer not only financially but also by giving extremely accurate and faster outcome. Here we lay out an overall architecture to explain the overall processing. So now we take a look at the two general classes of systems they are special-purpose system and general-purpose system, explaining how they differ and their relative advantages and disadvantages. After that we point at the few remaining problems that require additional research. Finally, we conclude by discussing when natural language processing technology can be practically used at various levels . We also discuss about when it will become commercially practical, and what will be the cost to practically use this technology. The techniques specifically developed for analysing and understanding the inner-workings and representations acquired by neural models of language is EMNLP 2018 BlackboxNLP. The approach includes: investigating the impact on the performance of neural network on systematic manipulation of input and also testing whether the interpretable knowledge can be decoded from intermediate representations to propose modifications to make the knowledge state or generated output more and also to examine the performance of networks on simplified or formal languages. In the following report we aim to convert a program of a given language to an equivalent program of another language. For that we have taken help of NLP that is Natural Language Processing. By using Natural Language Tool Kit, we have successfully identified the variables, datatypes, operators, keywords, indentations. We have also discussed various aspects and domains of NLP and some real-world applications of it.

A survey of Semantic Reasoning frameworks for robotic systems

Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in diverse and dynamic environments. To address the challenges associated with operation in real-world domains, robots must effectively generalize knowledge, learn, and be transparent in their decision making. This survey examines Semantic Reasoning techniques for robotic systems, which enable robots to encode and use semantic knowledge, including concepts, facts, ideas, and beliefs about the world. Continually perceiving, understanding, and generalizing semantic knowledge allows a robot to identify the meaningful patterns shared between problems and environments, and therefore more effectively perform a wide range of real-world tasks. We identify the three common components that make up a computational Semantic Reasoning Framework: knowledge sources, computational frameworks, and world representations. We analyze the existing implementations and the key characteristics of these components, highlight the many interactions that occur between them, and examine their integration for solving robotic tasks related to five aspects of the world, including objects, spaces, agents, tasks, and actions. By analyzing the computational formulation and underlying mechanisms of existing methods, we provide a unified view of the wide range of semantic reasoning techniques and identify open areas for future research.

General-Purpose Ultrasound Neuromodulation System for Chronic, Closed-Loop Preclinical Studies in Freely Behaving Rodents.

Transcranial focused ultrasound stimulation (tFUS) is an effective noninvasive treatment modality for brain disorders with high clinical potential. However, the therapeutic effects of ultrasound neuromodulation are not widely explored due to limitations in preclinical systems. The current preclinical studies are head-fixed, anesthesia-dependent, and acute, limiting clinical translatability. Here, this work reports a general-purpose ultrasound neuromodulation system for chronic, closed-loop preclinical studies in freely behaving rodents. This work uses microelectromechanical systems (MEMS) technology to design and fabricate a small and lightweight transducer capable of artifact-free stimulation and simultaneous neural recording. Using the general-purpose system, it can be observed that state-dependent ultrasound neuromodulation of the prefrontal cortex increases rapid eye movement (REM) sleep and protects spatial working memory to REMsleep deprivation. The system will allow explorative studies in brain disease therapeutics and neuromodulation using ultrasound stimulation for widespread clinical adoption.

Development and validation of transient Thermal-Hydraulic model for SFR steam generators

The steam generator (SG) is an important component in sodium-cooled fast reactors (SFRs). It transfers the heat from sodium systems to the balance of plant (BOP), producing steam for electricity production. Any disturbances originating from the BOP propagate to the nuclear systems through SG. Hence, it is especially important to model and predict the thermal–hydraulic behavior of SG during such transients. Towards this, a thermal–hydraulic model of SG of a typical medium-sized SFR has been developed using general-purpose system simulation code – FLOWNEX. Usually, the moving boundary method is used to model SGs as it involves phase change and multiple heat transfer regimes. However, in the current model, the fixed mesh approach, generally used for modeling pipelines, single-phase heat exchangers, etc. in plant dynamic codes, has been used to model SGs. In this paper, the modeling details and the solution procedures used in the FLOWNEX are discussed in detail. The steady-state results of the model developed have been compared with the results from in-house code DESOPT. The results were in good agreement at various power levels as well as in off-design conditions. The transient performance of the model has been validated against an in-house experiment carried out in Steam Generator Test Facility (SGTF), IGCAR, India, and a steam generator shutdown experiment carried out in Energy Technology Engineering Center (ETEC), Santa Susana Field Laboratory (SSFL), USA. The transient results were also in good agreement, thereby increasing the confidence level of the predictions. The model developed will be integrated to plant dynamic codes containing other system models and will be used to carry out system-level simulations in the future.

How General-Purpose Is a Language Model? Usefulness and Safety with Human Prompters in the Wild

The new generation of language models is reported to solve some extraordinary tasks the models were never trained for specifically, in few-shot or zero-shot settings. However, these reports usually cherry-pick the tasks, use the best prompts, and unwrap or extract the solutions leniently even if they are followed by nonsensical text. In sum, they are specialised results for one domain, a particular way of using the models and interpreting the results. In this paper, we present a novel theoretical evaluation framework and a distinctive experimental study assessing language models as general-purpose systems when used directly by human prompters --- in the wild. For a useful and safe interaction in these increasingly more common conditions, we need to understand when the model fails because of a lack of capability or a misunderstanding of the user's intents. Our results indicate that language models such as GPT-3 have limited understanding of the human command; far from becoming general-purpose systems in the wild.

Design of Side-Channel-Resistant Power Monitors

In modern computing platforms, power monitors (PwrMons) are employed to deliver online power estimates to support different runtime power-performance optimization methodologies. However, the possibility of setting up a successful side-channel attack by analyzing the power estimates imposes the use of a suitable and systematic approach in the design of such PwrMons. This article proposes a design methodology to automatically identify and implement side-channel-resistant PwrMons at the hardware level, for generic computing platforms. The methodology works by designing a PwrMon for which the switching activity of the signals used to compute the power estimates is not a function of both the secret key and the plaintext/ciphertext values processed by the computing platform. According to the most recent standardized methodologies to assess the side-channel security, our experimental validation leverages both correlation power analysis and <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-test analysis considering a general purpose System on Chip executing different cryptographic primitives and an application-specific accelerator implementing the AES-128 algorithm. Our results confirm the impossibility of retrieving the secret key from the power estimates provided by our side-channel-resistant PwrMon. Considering several temporal resolutions, we highlight an accuracy error of the power estimates limited to less than 2.7&#x0025;, as well as an average area and power overheads for the protected PwrMons lower than 6&#x0025; and 5&#x0025;, respectively. To this end, the proposed methodology is able to deliver a side-channel-resistant PwrMon within state-of-the-art accuracy error and overheads.

Gated Tomographic Radionuclide Angiography Using 3D-Ring CZT StarGuide SPECT/CT Head-To-Head Comparison With a Cardiac-Dedicated CZT Camera

Gated tomographic radionuclide angiography can assess and monitor left cardiac function. Dedicated cardiac CZT cameras have enabled dose reduction and quicker acquisitions. New 3D-ring CZT general purpose systems are now available. We report 50 patients who underwent a 7-minute acquisition on a cardiac-dedicated CZT camera and 9 minutes on a new 3D-ring CZT system after mean injection of 321.4 ± 55.9 MBq 99mTc-labelled human serum albumin. There was no significant difference in left ventricular volumes, and left and right ventricular ejection fractions. These preliminary results seem to validate the use of 3D-ring CZT system for LEVF and cardiac function evaluation.

Simulation modeling of a computer system multithreaded architecture using AnyLogic

The simulation is the most effective way to investigate information processing in computer systems. Computer system simulation is advantageous due to the fact, that such class of systems are well formalized in the form of queuing systems. Any computer system can be considered in terms of queuing system theory as an interconnected set of requests for service, system re-sources, queues to resources, and rules of service. The problem with the study of computer systems is that the existing simulation software tools have been developed as universal tools for the study of general-purpose systems. There are no specialized libraries or separate modeling tools for the study of computer systems. Therefore, modeling computer systems based on the use of standard libraries of such a modern modeling tool as AnyLogic, and the possible creation of elements of a specialized library is of great importance. The objective of the paper is to model multithreaded computing in a multiprocessor computer system using AnyLogic software. The designed computer system diagram enables us to have a deep insight into all details of information processing, and the obtained characteris-tics of the computer system, such as CPU usage coefficients, queue dynamics, distribution of thread execution time in the system, etc., allow us to determine the most effective configuration of the system, the parameters of its elements and operation modes.