On-line Computer Research Articles

Two-stage dynamic real-time optimization framework using parameter-dependent differential dynamic programming

The purpose of chemical process control includes proactive adjustment of the operation to make the most profit out of it. Within this context, real-time optimization (RTO) is proposed and extended to dynamic RTO (DRTO) in the hierarchical control structure, usually having model predictive control (MPC) below. However, online tractability confined the model complexity of RTO and MPC, which results in model inconsistency and, even, incompatible solutions. Here we use parameter-dependent differential dynamic programming (PDDP) to incorporate the closed-loop behavior of the controller in an RTO layer to reduce problem complexity and online computation time. The adaptive control performance of PDDP and the efficacy of closed-loop DRTO formulation with PDDP is demonstrated with the reaction-storage-separation network system control. Consequently, PDDP provides a useful parameterization method to express closed-loop system dynamics, which enables fast feedback control and integrated plant optimization.

Low-Cost Constant Time Signed Digit Selection for Most Significant Bit First Multiplication

Serial binary multiplication is frequently used in many digital applications. In particular, left-to-right (aka online) manipulation of operands promotes the real-time generation of product digits for immediate utilization in subsequent online computations (e.g., successive layers of a neural network). In the left-to-right arithmetic operations, where a residual is maintained for digit selection, utilization of a redundant number system for the representation of outputs is mandatory, while the input operands and the residual may be redundant or non-redundant. However, when the input data paths are narrow (e.g., eight bits as in BFloat16), conventional non-redundant representations of inputs and residual provide some advantages. For example, the immediate and costless sign detection of the residual that is necessary for the next digit selection; a property not shared by redundant numbers. Nevertheless, digit selection, as practiced in the previous realizations, with both redundant and non-redundant inputs and/or residual, is slow and rather complex. Therefore, in this paper, we offer an imprecise, but faster digit selection scheme, with the required correction in the next cycle. Analytical evaluations and synthesis of the proposed circuits on FPGA platform, shows 30% speedup and less cost with respect to both cases with redundant and non-redundant inputs and residual.

Secondary Metabolites and Their Biological Evaluation from the Aerial Parts of Staehelina uniflosculosa Sibth. & Sm. (Asteraceae)

Phytochemical investigation of Staehelina uniflosculosa Sibth. & Sm. resulted in the isolation of twenty-two natural products: eleven sesquiterpene lactones, artemorin (1), tamirin (2), tanachin (3), reynosin (4), baynol C (5), desacetyl-β-cyclopyrethrosin (6), 1β-hydroxy-4α-methoxy-5α,7α,6β-eudesm-11(13)-en-6,12-olide (7), 1β,4α,6α-trihydroxyeudesm-11-en-8α,12-olide (8), 1β-hydroxy-arbusculin A (9), methyl-1β,4α,6α-trihydroxy-5α,7αH-eudesm-11(13)-en-12-oate (10) and methyl-1β,6α,8α-trihydroxy-5α,7αH-eudesma-4(15),11(13)-dien-12-oate (11); one lignan, pinoresinol (12); one norisoprenoid, loliolide (13); six flavonoids (four genins and two glycosides), hispidulin (14), nepetin (15), jaceosidin (16), eriodictyol (17), eriodictyol-3′-O-β-D-glucoside (18) and eriodictyol-7-O-β-D-glucuronide (19); and three phenolic derivatives (one phenolic acid and two phenolic glucosides), protocatechuic acid (20), arbutin (21) and nebrodenside A (22). From the isolated compounds, only nepetin (15) has been reported previously from the Staehelina genus and, to the best of our knowledge, it is the first time that compound (18) has been identified in Asteraceae. A number of these substances were tested for (a) inhibition of lipoxygenase and acetylocholinesterase, (b) their antioxidant activity using the DPPH (1,1-Diphenyl-2-picrylhydrazyl) method or/and (c) inhibition of lipid peroxidation. The tested components exhibited low antioxidant activity with the exception of 5 and 22, while the effectiveness of these compounds in the inhibition of acetylocholinesterase is limited. Furthermore, Molinspiration, an online computer tool, was used to determine the bioactivity ratings of the isolated secondary metabolites. The compounds’ bioactivity ratings for potential therapeutic targets were very promising.

DDQN-based online computation offloading and application caching for dynamic edge computing service management

Multi-access Edge Computing (MEC) reduces task service latency and energy consumption by offloading computing tasks to MEC servers. However, constrained by the limited bandwidth and computing resources, MEC servers often cannot parallelly process all computing tasks. Simultaneously, the high dynamism of service popularity necessitates MEC servers to dynamically update cached applications, under ensuring compliance with storage resource constraints and the system cache updating cost budget for service providers. In response to the above two issues, this paper firstly formulates computation offloading and application caching as a dual-timescale decision optimization problem, aiming to minimize the average service latency for users by obtaining optimal offloading decision, cache decision, transmission bandwidth, and computing resource. Then, we propose a Deep Reinforcement Learning (DRL)-based two-stage online computation offloading and application caching (DTSO2C) algorithm, effectively stabilizing application cache update costs and enhancing Quality of Service (QoS) for users. Furthermore, we utilize convex optimization algorithms to derive the optimal communication bandwidth and computing resource allocation strategy, further reducing the average service latency for users. Simulation results demonstrate that the DTSO2C algorithm outperforms the compared algorithms, achieving an average reduction in service latency of 66.2%, with an average cache update cost of only 0.15 USD per time frame.

Правовой анализ доходных видеоигр

The purpose of this study is to provide an in-depth legal analysis of the phenomenon of profitable video games, the copyright holders of which, unlike the owners of online computer games of previous generations, recognise the copyright of users in the works created or processed by them, and provide players with the opportunity to dispose of their intellectual rights to make a profit. The work employs scientific methods to analyse contentious relationships in the legal aspects of profitable video games and compares different approaches to their regulation. The fundamental legal differences in the conditions for using free and profitable video games have been identified and analysed. The paper provides the systematisation and discloses the legal nature of methods players use to generate income within network computer games. The research discovers the legal, organisational, and economic reasons and risks leading to the non-recognition of user copyrights and the imposition of a ban on their disposal by copyright holders of free video games. A mechanism has been discovered and disclosed that allowed the owners of profitable video games to eliminate intellectual and legal risks, recognise gamers’ copyrights to the works they created and launch the legal circulation of rights to them. The work analyses the model for using the blockchain infrastructure and non-fungible tokens as its elements for fixing and disposing of rights to user-generated gaming artefacts. The smart contract as the legal instrument of blockchain platforms is critically analysed. Authors made their proposals to improve the legal efficiency of a smart contract by adding a system of standard public licenses. The scientific novelty of the work lies in explaining the legal nature of the phenomenon of profitable video games and the mechanism for disposing of copyrights within them, as well as in proving the absence of identity between a profitable video game and such an object of special regulation as gambling. The practical significance of the article consists in the applicability of its results for improving the legal regulation in the context of the complication of the ecosystem, the growth of the market and the scale of the audience of online computer games.

Designing a Multi-Level Caching Forward Index for Recommendation System Services

This paper introduces a forward index based on microservices, which effectively addresses the issue of forward read amplification in recommendation systems through a multi-layer caching mechanism. This allows the forward service to handle a large number of concurrent requests with limited resources while ensuring service stability and final value rate. The paper provides a detailed description of the forward index's data structure design, traffic penetration calculation, and overall service architecture design, dividing the system into three parts: SDK, proxy service, and storage, corresponding to data query, data processing, and data storage respectively. In the SDK design, three components are employed: Cache Slot, synchronous queue, and timer, supporting synchronous, asynchronous, and flexible request forms. The proxy service design handles heterogeneous data and online computation, supporting batch and asynchronous designs, and provides a dynamic data protocol to adapt to the characteristic needs of different microservices. The storage layer design considers performance, cost, and maintainability, utilizing a Cache & SSD model to cope with high-concurrency scenarios.

Online functional connectivity analysis of large all-to-all networks in MNE Scan

Abstract The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity

Practical two-party SM2 signing using multiplicative-to-additive functionality

Threshold signatures are important tools for addressing issues related to key management, certificate management, and cryptocurrencies. Among them, two-party SM2 signatures have received considerable interest recently. In this paper, we propose a fast and secure online/offline two-party SM2. By employing the re-sharing technique, we have successfully made the online phase of the signing process non-interactive while achieving nearly optimal computational efficiency. Additionally, in the offline phase, there is just a single call to the multiplicative-to-additive functionality based on Paillier encryption. Our protocol is existentially unforgeable under adaptive chosen message attacks in the random oracle model in the presence of a static adversary. Experimental results demonstrate that our proposed scheme outperforms previous similar schemes by approximately a factor of 2 in online computation and a factor of 3 in online communication. Our scheme can be applied in scenarios such as Certificate Authority (CA) and the signing of blockchain transactions to provide them with a more secure and flexible implementation method, enhancing the security and reliability of the systems.

The process of language acquisition: an experimental approach from a minimalist perspective

We present the approach to the study of language acquisition and processing developed in LAPAL (Laboratório de Psicolinguística e Aquisição da Linguagem – PUC-Rio) in the last decades, which incorporates a minimalist conception of language in a procedural model of grammar identification, and in the online computation of sentences. We refer to experimental results regarding infants’ sensibility to elements of functional categories, the early acquisition of gender and number, the early parsing of recursive structures, the role of recursion in cognitive development, and the processing cost of recursive structures and long-distance dependencies. Methodological issues concerning the assessment of children’s ability to process costly structures are considered, which contribute to the identification of the nature of children’s difficulties in comprehension tasks. Particular attention is paid to the application of this approach to the tracking of DLD (Developmental Language Disorder).

The Quantization Monte Carlo method for solving radiative transport equations

We introduce the Quantization Monte Carlo method to solve thermal radiative transport equations with possibly several collision regimes, ranging from few collisions to massive number of collisions per time unit. For each particle in a given simulation cell, the proposed method advances the time by replacing many collisions with sampling directly from the escape distribution of the particle. In order to perform the sampling, for each triplet of parameters (opacity, remaining time, initial position in the cell) on a parameter grid, the escape distribution is precomputed offline and only the quantiles are retained. The online computation samples only from this quantized (i.e., discrete) version by choosing a parameter triplet on the grid (close to actual particle’s parameters) and returning at random one quantile from the precomputed set of quantiles for that parameter. We first check numerically that the escape laws depend smoothly on the parameters and then implement the procedure on a benchmark with good results.

Distributed power system coordination via parametric optimization and ADMM

In this paper we present an efficient model predictive control algorithm for distributed coordination of an electrical power system comprising many spatially distributed controllable units. The coordination problem is formulated using parametric solutions of local optimization problems corresponding to individual subsystems leading to favorable problem structure which can be split across all subsystems and nodes in the network. The structure of the obtained coordination problem is exploited to develop a very efficient solution algorithm based on an ADMM technique. The key features of the overall approach are: (i) private data of individual subsystems are protected, (ii) simple and efficient on-line computations, (iii) parallelization of computation across all subsystems and all nodes in the network. Efficiency of the proposed control strategy is demonstrated on a number of numerical case studies of varying size.

Improving Student Engagement and Success in Computer Programming Courses through Social Learning in Online Environments

This paper addresses the necessity to enhance the effectiveness of online learning (OL) environments by examining the influence of social interactions on learner motivation, engagement, and success, specifically focusing on online computer science education (CSE). Acknowledging the limitations of peer-to-peer and learner-to-teacher interactions on online platforms, this paper delves into the potential of social learning (SL) organized through learner groups, communities, or networks to significantly enrich the learning experience. To test the hypothesis that SL positively impacts student outcomes, an experiment was conducted with 49 engineering students divided into two groups. The first group undertook an object-oriented programming course in C++ using the Moodle platform, while the second group, in addition to the online course, participated in asynchronous group activities through forums and synchronous interactions via chat. The results reveal a notable positive impact of SL on student outcomes, with participants in the second group reporting higher satisfaction levels and achieving superior results compared to those in the first group. The findings underscore the significance of integrating social interaction into OL environments, with potential implications for enhancing the overall quality of education and student performance in society.

Improved Model-Free Predictive Control of a Three-Phase Inverter

Model predictive control (MPC) performance depends on the accuracy of the system model. Moreover, the optimization algorithm of MPC requires numerous online computations. These inherent limitations of MPC hinder its application in power electronics systems. This paper proposes a two-part solution for these challenges for a three-phase inverter with an output LC filter. The first part of the control scheme is a linear and modified model-free approach based on the auto-regressive structure (ARX) with exogenous input. The second part is the computationally efficient optimization algorithm based on the active set method to solve the optimization problem of the MFPC. The objective of the control scheme is to regulate the output voltages of the inverter in the presence of constraints. The constraints are the maximum admissible filter current and optimal duty cycle to avoid any damage to the system. To validate the performance of the proposed control scheme, simulations and hardware-in-loop (HIL) real-time investigations have been performed, comparing the results of the proposed approach with the model-based predictive control. The results showcase the computational efficiency and effectiveness of the MFPC approach, demonstrating its potential for overcoming the limitations of traditional MPC in power electronics systems.

MPC-Based Asynchronous Attack Tolerant Control for Uncertain Markov Jump Cyber-Physical Systems.

The model predictive control (MPC)-based asynchronous attack tolerant control scheme is investigated in this article for uncertain Markov jump cyber-physical systems (MJCPSs) under the Denial-of-Service (DoS) attack. To tackle the problem of the system running mode may not be observed in the control center, an asynchronous model predictive controller is proposed. Specifically, a dynamic controller, which can tune the performance online, is designed besides a traditional state feedback one. Even though such a combination may cause possible degradation of system performance, it can expand the initial feasible region and relieve the online computation burden efficiently. In addition, a decision variable is introduced to alleviate limitations on the feasible region generated by the constraints in the traditional MPC method. A series of solvable optimal problems are further constructed to achieve the desired performances. Finally, an application of the proposed method is given to demonstrate its effectiveness.

Entry guidance for spatial no-fly zones avoidance via model-based reinforcement learning

This paper proposes a novel guidance law for hypersonic entry vehicles, considering no-fly zones with height limits. Traditional planar assumptions restrict the flexibility of trajectory design for scenarios like radar avoidance. Besides, the numerical integration proves inefficient for long-term prediction and avoidance. Therefore, a model-based reinforcement learning policy is designed. It offline learns entry dynamics in advance and onboard plans a feasible trajectory. The planner's state includes flight status and no-fly zones; action presents waypoints; and reward ensures constraint while maximizing terminal precision. Then, analytical prediction converts spatial no-fly zone constraints to flight-path angle constraints, improving precision compared to traditional one-step estimates. Finally, the two parts are assembled into the predictor-corrector framework, which gives the augmented guidance commands. While retaining its robustness to bias, our method reduces online optimization calculations and outperforms constraint satisfaction. Experiments show that the model-based method reduces 60% training in offline training compared with proximal policy optimization. Besides, our method is 80% faster than conventional predictor-corrector guidance regarding online computation speed.

Evaluating the Reliability of a Social Presence Composite Construct for Online Computer Science Degree Programmes

As a contribution to ongoing discussions about the implications of social presence for online instruction technology, this study evaluated the reliability and validity of a composite social presence construct in online computer science programmes using archival data from the Computing Research Association’s (CRA) Center for Evaluating the Research Pipeline (CERP) Data Buddies Survey. Questions from the survey were aligned to three interdependent subconstructs of social presence proposed by Kreijns et al (2021). Spearman’s rank correlation coefficient was used to analyse the relationships between the subconstructs. Cronbach’s alpha was used to evaluate the internal consistency and reliability of the subconstructs of social presence, sociability, and social space, as well as the composite construct of social presence. The findings indicate that the social presence construct and its subconstructs are internally consistent and highly reliable, aligned with the CERP survey. Results indicated that the subconstructs are interrelated indicators of the perception of social presence in online computer science programmes. This study contributes to the literature concerning measuring social presence in online learning by providing a reliable and valid construct that can be used to assess the construct using different permutations of analysis on the CERP dataset.

Efecto de normas de persuasión en el servicio personal shopper

Some key disadvantages of online computer sales stem from unmet consumer expectations, insufficient product knowledge, and product returns. This study examined cognitive route factors and persuasive norms in online advertisements to determine the purchase intention of personal shopper services for laptops. Covariates included gender, educational level, marital status, prior shopping experiences, monthly income, and workplace location. The results approach indicate that peripheral route advertising messages positively impact the purchase intention of personal shopper services, especially among women. Findings also demonstrate that monthly income and educational level influence the purchase intention of laptops when aided by a personal shopper. It was found that women prefer personal shoppers providing clear responses, while men prefer those with technical knowledge. Additionally, women prefer to pay for the personal shopper service as 3% of the purchase value, whereas men favoured a single payment. Finally, the purchase intention for the personal shopper service is higher among women. The potential effects of advertising messages on the purchase intentionof the service are discussed.

Espace européen de l’éducation : renforcer la recherche en pédagogie universitaire pour aller à la quête d’environnements informatiques et de pratiques éducatives novatrices

In the digital age, technological developments have emerged, which have prompted researchers and education practitioners to readapt to the new digital context. Research in university pedagogy has been extensive, with the aim of inserting this new digital culture into recent reforms. Computer systems accompanied by technological devices have been put into effect in order to demonstrate pedagogical innovation through the use of pioneering educational practices. According to the strategic framework of the European Education Area, the Member States of the European Union must set new objectives based not only on the strengthening of the means of strategic cooperation and synergies, in particular the European Research Area and the Bologna Process, but also on the setting of objectives and indicators of progress towards the achievement of the European Education Area. While taking into account the renewal of the university context in terms of education and training in the European area, it would be interesting to focus on the essential contribution of the Francophone University Agency (AUF) in the renewal of university pedagogy and training. Then digital pedagogical innovation (DPI) is carried out through networks and online computer systems. Finally, given the internationalization and digitalization of education, innovative pedagogical practices have emerged in the higher cycle. The implementation of learning management systems, whether in a blended learning model environment or in an open and distance learning environment, plays an essential role at the personal level in the development of learning strategies, but also at the collective level in the cultivation of professional competences.

Assessment of Factors and Level of Patient Satisfaction in Out-patient Department of a Private Hospital

Background: Measurement of patient satisfaction in the outpatient department (OPD) of a hospital can be used to evaluate and redesign the process of caregiving and to improve the quality of services. Sometimes study findings depend on the setting.Objectives: a) To identify the factors associated with the patient's experience, and satisfaction about health care services of Out-patient department(OPD) of Khwaja Yunus Ali Medical College Hospital receives multidisciplinary health services. b) To measure the satisfaction level regarding doctors, health assistance, and other auxiliary staff working in OPD to help patients.Materials and Methods: A cross-sectional study was conducted among 914 patients of age ranging from 1 to 80 years selected randomly from OPD between January 2021 and May 2021. A self-designed semi-structured questionnaire was developed to assess patients' satisfaction with health care services. Statistical analysis was done by online computer software.Results: Overall 92.23 % of the patients were satisfied with the behaviour of the doctors and 75.93 % were satisfied with the services of other staff, and the remaining 7.77% were unsatisfied with doctors' behaviour and 24 % with other staff services respectively.Conclusions: The percentages of unsatisfied patients were deficient. Various deficiencies should always be covered by the hospital administration that will turn into a good result of improvement in the hospital services for more satisfaction of the patients. KYAMC Journal Volume: 14, No: 04, January 2024: 234-239.

Empowering Learners in Higher Education: Redesigning an Online Computer Science Course Through Universal Design for Learning Implementation

This article focuses on the practical implementation of Universal Design for Learning (UDL) in an online computer science course, articulating the collaborative efforts between the instructional designer (the first author) and the faculty member to redesign the course using UDL principles. Specific instances of redesigned learning modules and artifacts in adherence to UDL principles and results and insights gained from the implementation are reported. The article concludes with recommendations for higher education faculty and instructional designers to consider when applying UDL in their online courses.