A Compact SAT Encoding for Non-Preemptive Task Scheduling on Multiple Identical Resources

In QSAR, a statistical model is generated from a training set of molecules (represented by chemical descriptors) and their biological activities (an "activity model"). The aim of the field of domain applicability (DA) is to estimate the uncertainty of prediction of a specific molecule on a specific activity model. A number of DA metrics have been proposed in the literature for this purpose. A quantitative model of the prediction uncertainty (an "error model") can be built using one or more of these metrics. A previous publication from our laboratory ( Sheridan , R. P. J. Chem. Inf. 2013 , 53 , 2837 - 2850 ) suggested that QSAR methods such as random forest could be used to build error models by fitting unsigned prediction errors against DA metrics. The QSAR paradigm contains two useful techniques: descriptor importance can determine which DA metrics are most useful, and cross-validation can be used to tell which subset of DA metrics is sufficient to estimate the unsigned errors. Previously we studied 10 large, diverse data sets and seven DA metrics. For those data sets for which it is possible to build a significant error model from those seven metrics, only two metrics were sufficient to account for almost all of the information in the error model. These were TREE_SD (the variation of prediction among random forest trees) and PREDICTED (the predicted activity itself). In this paper we show that when data sets are less diverse, as for example in QSAR models of molecules in a single chemical series, these two DA metrics become less important in explaining prediction error, and the DA metric SIMILARITYNEAREST1 (the similarity of the molecule being predicted to the closest training set compound) becomes more important. Our recommendation is that when the mean pairwise similarity (measured with the Carhart AP descriptor and the Dice similarity index) within a QSAR training set is less than 0.5, one can use only TREE_SD, PREDICTED to form the error model, but otherwise one should use TREE_SD, PREDICTED, SIMILARITYNEAREST1.

Building Grid Communities There has been a significant increase in interest in Grid Computing in the last year—from the computer science community, the platform vendors, and interestingly, from the application scientists (Physicists, Biologists, etc). Significantly, Grid computing emphasises the same challenges in interoperability, security, fault tolerance, performance and data management as the distributed computing community—albeit grounded with specific application scenarios from the science and engineering communities. A key aspect of Grid computing is the need to couple computational and data resources to form Virtual Organisations (VOs), via resource scheduling and discovery approaches. A VO in this context is created by combining capability across a number of different administrative domains, to run a single large problem. Hence a single application program may execute tasks on multiple resources—using the concept of a SuperScheduler . The SuperScheduler does not own any resources itself, but connects to a number of different local resource scheduling systems, within different geographically distributed administrative domains. Choosing suitable resources on which to schedule operations has generally involved a discovery process utilising a search of registry services to locate resources of interest. The mechanisms used within this process have been quite limited in scope, offering a limited set of queries to interrogate a (generally) LDAP based repository (via grid-info-search } in Globus for instance). Having an efficient discovery mechanism within a Grid environment is quite significant—as it provides the infrastructure needed to support dynamic and fault tolerant VOs. The discovery mechanism also needs to be efficient—as searching for suitable resources—or more recently services (whereby access to all computational and data resources is seen as a computational or data service)—requires querying across distributed registries. The discovery process can also be seen as a way to establish dynamic relationships between Grid services—persistent or transient—as the discover process can be seen as a first stage in establishing an association with another service. Hence, when initiating a discovery process, a service user should identify the type of association to be formed with a service provider. Examples of such associations may be client/server (generally), or Peer-2-Peer. Viewed in this way, service discovery may be undertaken passively —similar to a service lookup in a registry, or actively when the discovery mechanism is intended to form a particular type of association with another service—and based on parameters such as cost, performance, Quality of Service (QoS), or trust. Groups As the number of services on the Grid increase, so will the potential interactions between these services. This can lead to high traffic volumes on the network, and potentially act as a barrier to scalability. The use of the group paradigm is significant in this context, to limit interaction within a small community of services (in the first instance). Service groups can be formed based on a number of different criteria, ranging from geographical location of services, by service types—such as mathematical services, graphics services, data analysis services etc, service ownership, service costs, or service priorities. Members of a group can interact with each other more efficiently (via multicast messages, for instance), and can load balance requests sent to the group. The concept of forming a group or community also implies some level of trust between the members, implying the availability of efficient mechanisms to share state with other members of the group. The provision of shared memory based abstractions become necessary in this context, as group members may need to repeatedly share state, and be alerted as new members enter/leave the group. An important consideration in this context is the ability to decide which services should be allowed within a group—and how the group structure should evolve. Different service behaviours and roles can co-exist within a group—even though the services are targeted at a particular application domain. Therefore, in a group of mathematical services, one could have broker services, service users, community management services, and monitoring services. Some of these are required to manage and maintain the group itself, whilst others provide specialised capability. The group idea also leads to the formation of small world networks—primarily interaction networks with a small average path length between members, and a large internal connectivity (i.e. a clustering coefficient that is independent of network size). These networks are likely to be of great significance in scientific computing—as they closely model data sharing between scientists within a given domain (such as genomics, astronomy, etc)—and different from sharing music data in systems like Gnutella. Once a small world network has been established, various assumptions about other members in the group may be made—such as their ability to provide particular types of data (or their speciality), trust that can be placed in them, and their ability to respond in a timely fashion. Identifying scenarios where such networks may be established, and subsequently providing suitable middleware to sustain these, will be important in Grid systems. One member within such a small world network (group) may be allocated the role of being a cluster head —thereby acting as a gateway to other small world networks. Hence a federation of such small world networks may be established, that span domain and organisational boundaries. The Importance of Shared Semantics Establishing a group/community of services also necessitates the description of common semantics. Such a description should allow roles of each member to be defined, along with specialist services being offered by them, and provide a mechanism for communication between the members. Hence, two types of descriptions are necessary: (1) those that are independent of any particular application domain, and provide a means to define roles, and (2) those that are specific to particular application domains, and services that need to be provided within that domain. Significant progress has been made towards describing attributes of computational and data resources in a unified way—so that a query to a SuperScheduler could be understood by multiple resource managers. Little progress however has been made towards standardising descriptions of services within particular application domains—a time consuming and consensus building process within a scientific community. Both of these descriptions are necessary to enable multi-disciplinary science and engineering—and to enable a better understanding of common requirements of all of these communities. An important concern is the ability of these different data models to interact—as there is unlikely to be a single model used by all within or across communities. Resolving differences in representation, or providing support for automated negotiation between members to resolve semantic differences, become important services that need to be supported within a group. Although significant progress has been made in Grid computing, the next phase will require more efficient mechanisms to organise and coordinate participants (individuals or institutions) within groups. Mechanisms to support the formation of groups are also significant—i.e. identifying which members should belong to which group, and how their membership can be sustained. Current emphasis has been on infrastructure installation (such as high speed networks) and means to provide common interfaces to resource management systems. To enable more effective interaction between services which run on this infrastructure, standardisation of common data models becomes significant. The Grid should eventually provide the computational and data infrastructure necessary to enable groups of scientists to undertake multi-disciplinary work. It should also provide the necessary entry points for connecting resources with a range of different capabilities—such as high end computational clusters and parallel machines, to sensor networks capable of data capture at source. An important aspect of Grid computing has been the significant interactions between the computer science and the application science communities—and for Grid computing to mature, these need to be strengthened further. Acknowledgement Thanks to Jos'e C. Cunha of the CITI Centre, New University of Lisbon (Portugal), for interesting discussions on Groups. Omer F. Rana Cardiff University and the Welsh E-Science/Grid Computing Centre, UK

IntroductionThe analysis of discrete sequential data, such as event logs and customer clickstreams, is often challenged by the vast number of possible sequential patterns. This complexity makes it difficult to identify meaningful sequences and derive actionable insights.MethodsWe propose a novel feature selection algorithm, that integrates unsupervised sequential pattern mining with supervised machine learning. Unlike existing interpretable machine learning methods, we determine important sequential patterns during the mining process, eliminating the need for post-hoc classification to assess their relevance. Compared to existing interesting measures, we introduce a local, class-specific interestingness measure that is inherently interpretable.ResultsWe evaluated the algorithm on three diverse datasets - churn prediction, malware sequence analysis, and a synthetic dataset - covering different sizes, application domains, and feature complexities. Our method achieved classification performance comparable to established feature selection algorithms while maintaining interpretability and reducing computational costs.DiscussionThis study demonstrates a practical and efficient approach for uncovering important sequential patterns in classification tasks. By combining interpretability with competitive predictive performance, our algorithm provides practitioners with an interpretable and efficient alternative to existing methods, paving the way for new advances in sequential data analysis.

In the era of big data, robust pattern recognition and accurate data analysis have become critical in various fields, including healthcare, finance, and industrial automation. This study presents a novel hybrid computational intelligence model that integrates deep learning techniques and evolutionary algorithms to enhance the precision and resilience of pattern recognition tasks. Our proposed model combines Convolutional Neural Networks (CNN) for high-dimensional feature extraction with a Genetic Algorithm (GA) for feature optimization and selection, providing a more efficient approach to processing complex datasets. The hybrid model achieved an accuracy of 98.7% on the MNIST dataset and outperformed conventional methods in terms of recall (95.5%) and precision (97.2%) on large-scale image classification tasks. Additionally, it demonstrated substantial improvements in computation time, reducing processing duration by 35% over traditional deep learning approaches. Experimental results on diverse datasets, including time-series and unstructured data, confirmed the model's versatility and adaptability, achieving F1-scores of 0.92 in healthcare data analysis and 0.89 in financial anomaly detection. By incorporating a Particle Swarm Optimization (PSO) algorithm, the model further optimized hyperparameters, leading to a 25% reduction in memory consumption without compromising model performance. This approach not only enhances computational efficiency but also enables the model to perform reliably in resource-constrained environments. Our results suggest that hybrid computational intelligence models offer a promising solution for robust, scalable pattern recognition and data analysis, addressing the evolving demands of real-world applications.

Background: The industrial wireless automation system enables the monitoring and control of processes. It may be difficult to recharge the battery of sensors installed in harmful environments. Hence optimization of the power is the major issue to be addressed while implementing the network. The proposed hybrid data transmission approaches optimize data accuracy and energy efficiency of a wireless sensor node deployed in any industry. Methods: In the time-driven method, the sensor nodes periodically sense the environment and transmit the data continuously over time. In the event-driven method, the sensor nodes transmit data only when there is a drastic change in the occurrence of a certain event. Results: Based on the nature of the process, applications are classified as, less critical, critical and most critical. The time-driven based hybrid transmission approach is suggested for the most critical applications because they need to be monitored continuously so as to attain data accuracy. In the case of critical applications, the data is not required to be sent continuously, but instead it can be sampled and transmitted once in two seconds. Though the above suggested methods intended to provide better outcomes in terms of power utilization, in the case of process control applications, most critical and critical applications need to be monitored continuously. Hence such applications could be done as a heterogeneous industrial automation network, which is the combination of wired and wireless connectivity. This can be implemented by replacing all the signal cables with wireless communication system, regular power supply must be provided for the radio module attached with final control elements and also to the transmitters involved in most critical applications. For the least critical applications, the data can be sampled and transmitted once in four seconds. Conclusion: Simulation has been performed for time-driven based and duty-cycling based hybrid transmission approaches. The results can guide process engineers in selecting the transmission approach for optimizing the power of IWAS based on the critical level of the process. In the case of a critical process, the time-driven based hybrid transmission approach may be used, and in the case of a less critical process, the duty-cycling based hybrid transmission approach could be selected. By selecting appropriate transmission approach the life time of IWAS could be improved. Keywords: Controllers, process control, transceivers, transmitters, temperature sensors, wireless sensor networks.

Domain-specific modeling promises to close the gap between an application domain and a solution domain. As such it enables domain experts to directly model an application by means of a domain-specific language and to fully generate a final software product from the models. The advantages of domain-specific modeling have been demonstrated from several industrial case studies in various domains. However, domain-specific modeling is rarely applied in industrial automation. We have designed and developed two DSM solutions in the domains of injection molding machines and robot welding in order to enable domain experts to directly program in both domains without detailed software development expertise. In this paper we present two DSM tools, discuss challenges and experiences during design and development of both tools and draw some general insights about adapting DSM for industrial automation.

Due to the importance of neuraminidase in the pathogenesis of influenza virus infection, it has been regarded as the most important drug target for the treatment of influenza. Resistance to currently available drugs and new findings related to structure of the protein requires novel neuraminidase 1 (N1) inhibitors. In this study, a consensus QSAR model with defined applicability domain (AD) was developed using published N1 inhibitors. The consensus model was validated using an external validation set. The model achieved high sensitivity, specificity, and overall accuracy along with low false positive rate (FPR) and false discovery rate (FDR). The performance of model on the external validation set and training set were comparable, thus it was unlikely to be overfitted. The low FPR and low FDR will increase its accuracy in screening large chemical libraries. Screening of ZINC library resulted in 64,772 compounds as probable N1 inhibitors, while 173,674 compounds were defined to be outside the AD of the consensus model. The advantage of the current model is that it was developed using a large and diverse dataset and has a defined AD which prevents its use on compounds that it is not capable of predicting. The consensus model developed in this study is made available via the free software, PaDEL-DDPredictor.

The production efficiency of printed circuit board (PCB) assembly depends strongly on the organization of the component placement jobs. This is characteristic, especially in a high-mix low-volume production environment. The present study discusses the problem of arranging the jobs of one machine into groups in such a way that the job change costs will be minimized when the costs depend on the number of the job groups. This problem is motivated by the practical case where the group utilizes a common machine set-up and the number of set-up occasions is the dominating factor in the production line optimization. The problem is well known and its large instances are hard to solve to optimality. We show how real-life problem instances can be solved by three different methods: efficient heuristics, 0/1-programming, and constraint programming. The first two of these are standard approaches in the field, whereas the application of constraint programming is new for the job grouping problem. The heuristic approach turns out to be efficient: algorithms are fast and produce optimal or nearly optimal groupings. 0/1-programming is capable of finding optimal solutions to small problem instances and it therefore serves as a benchmark to approximative methods. The constraint approach solves moderately large problem instances to optimality and it has the great advantage that changing the problem formulation is relatively easy one can add new constraints or modify the details of the existing ones flexibly.

Automating robotic deburring in the automotive sector demands extreme precision in contour detection, particularly for complex components like wheel rims. This article presents the application of the U-Net architecture, a deep learning technique, for the precise segmentation of the outer contour of wheel rims. By integrating U-Net's capabilities with OpenCV, we have developed a robust system for wheel rim contour detection. This system is particularly well-suited for robotic deburring environments. Through training on a diverse dataset, the model demonstrates exceptional ability to identify wheel rim contours under various lighting and background conditions, ensuring sharp and accurate segmentation, crucial for automotive manufacturing processes. Our experiments indicate that our method surpasses conventional techniques in terms of precision and efficiency, representing a significant contribution to the incorporation of deep learning in industrial automation. Specifically, our method reduces segmentation errors and improves the efficiency of the deburring process, which is essential for maintaining quality and productivity in modern production lines.

MMIFR: Multi-modal industry focused data repository

Improved technology and economically feasible costs allow a widespread deployment of embedded systems in various application domains - ranging from integration into cars, industrial automation up to building automation. A sophisticated security architecture considering the challenging constraints on these systems and providing secure communication, secure software as well as physical security is needed. This paper presents an approach to allow untrusted, possible (intentional) malicious software to be executed securely on a low end embedded system. A proof of concept and an evaluation for a building automation system is given.

Machine-to-machine communications is characterized by involving a large number of intelligent machines sharing information and making collaborative decisions without direct human intervention. Due to its potential to support a large number of ubiquitous characteristics and achieving better cost efficiency, M2M communications has quickly become a market-changing force for a wide variety of real-time monitoring applications, such as remote e-healthcare, smart homes, environmental monitoring, and industrial automation. However, the flourishing of M2M communications still hinges on fully understanding and managing the existing challenges: energy efficiency (green), reliability, and security (GRS). Without guaranteed GRS, M2M communications cannot be widely accepted as a promising communication paradigm. In this article, we explore the emerging M2M communications in terms of the potential GRS issues, and aim to promote an energy-efficient, reliable, and secure M2M communications environment. Specifically, we first formalize M2M communications architecture to incorporate three domains - the M2M, network, and application domains - and accordingly define GRS requirements in a systematic manner. We then introduce a number of GRS enabling techniques by exploring activity scheduling, redundancy utilization, and cooperative security mechanisms. These techniques hold promise in propelling the development and deployment of M2M communications applications.

Change is the only thing in the real world which has been known to last forever. It takes various forms and progressions, ranging from gradual in some cases and abrupt in the others to even constantly incremental in yet other cases like ageing. Machine learning (ML) algorithms, in its simplest definitions, use the statistical analysis of static past data records to make predictions about the future and have reached a fair amount of accuracy on diverse data sets across different application domains. There exists an inherent contradictory friction between real life analysis and machine learning models based on above definitions, and it gets compounded while capturing the ever-changing data from streaming sources. Concept drift is a principle used for description of unpredictable variations in streaming data sourced from the real world through a given time period. The drift phenomenon occurring even in a single feature, if left unaddressed leads to silent decay and can play havoc with the accuracy of a previously accurate ML model. With increasing prevalence and scale of real-world deployments of ML analytics, models cannot remain invariant to instability of data distributions and must adapt to concept drift. We analyse the occurrence and effect of concept drift in the COVID-19 online education data sourced from LearnPlatform edtech Company in this paper. The data set has almost 20 million entries related to engagement index and can be fairly assumed to be big data for processing purposes. A comparative case analysis for the accuracy of concept drift aware modelling using adaptive windowing (ADWIN) vis-a-vis the basic ML counterpart to predict the student engagement based on digital connectivity and education technology has been carried out for the study.KeywordsMachine learning modellingConcept driftStreaming dataAdaptive windowingBig data

The paper proposes a layered scripting language technique for representation and control of avatar behavior for simpler avatar control in various domain environments. We suggest three layered architecture which is consisted of task-level behavior, high-level motion, and primitive motion script language. These layers brides gap between application domain and implementation environments, so that end user can control the avatar through easy and simple task-level scripting language without concerning low-level animation and the script can be applied various implementations regardless of application domain types. Our goal is to support flexible and extensible representation and control of avatar behavior by layered approach separating application domains and implementation tools.KeywordsVirtual EnvironmentShopping MallScript LanguageDomain EnvironmentPrimitive MotionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

ABSTRACTPosition control of direct current motors remains one of the most important control problems in various application domains like robotics, automation in industries, and aviation. Traditionally, Proportional–Integral–Derivative based controllers are most popular for such scenarios, however due to their inability to handle constraints and are not being optimal and robust by design, they are not preferred in precision position tracking applications like antenna positioning, pitch angle control for wind turbine blades, solar tracking in photovoltaic panels etc. This calls for the need to employ some robust and high‐precision controllers like model predictive control. The main objective of the work carried out is to present a better alternative for the position control problem for a DC servo motor plant using model predictive control. The optimization problem is formulated to minimize the cost function that penalizes position errors and input changes, along with the necessary constraints on output and inputs. The implementation of the proposed scheme is carried out both in simulations and with experimentation. In simulation, the scheme is verified using MATLAB/Simulink, and in experimentation on the real plant of Quanser's DC servo motor setup through Simulink real‐time interface blocks. The obtained simulation and experimental results efficiently validate the proposed theoretical findings by gracefully achieving the required position trajectory tracking. Achieved results are also compared with standard PID, which confirms the superiority of model predictive control over PID control, especially in handling constraints and yielding better tracking performance without any overshoots and with the overall lesser control energy requirement.